Consequences, opportunities and challenges of modern ...

Consequences, opportunities and challenges of modern biotechnology for

Europe (Bio4EU)

Task 1 – Mapping of modern biotechnology applications and industrial sectors, identification of data needs and

development of indicators

FINAL REPORT

DELIVERABLE 3

Framework Service Contract 150083-2005-02-BE

Ref. SCO5_05_BIO25 Study 1

Version no. 4

Framework Service Contract 150083-2005-02-BE Consequences, opportunities and challenges of modern biotechnology for Europe

Final Report/Deliverable 3 Page 2 of 172

This report has been produced by the ETEPS AISBL with contributions from: Thomas Reiss (PM), Fraunhofer Institute for Systems and Innovation Research, Germany Sibylle Gaisser, Fraunhofer Institute for Systems and Innovation Research, Germany Bernhard Buehrlen, Fraunhofer Institute for Systems and Innovation Research, Germany Christien Enzing, TNO Innovation Policy Group, Netherlands Annelieke van der Giessen, TNO Innovation Policy Group, Netherlands Anthony Arundel, Maastricht Economic Research Institute on Innovation and Technology (MERIT), Netherlands Cati Bordoy, Maastricht Economic Research Institute on Innovation and Technology (MERIT), Netherlands Susan Cozzens, Georgia Tech Technology Policy Assessment Center (TPAC), USA Pablo Catalán, Georgia Tech Technology Policy Assessment Center (TPAC), USA Sonia Gatchair, Georgia Tech Technology Policy Assessment Center (TPAC), USA Gonzalo Ordóñez, Georgia Tech Technology Policy Assessment Center (TPAC), USA



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Table of Contents

List of figures.......................................................................................................................... 5 List of tables ........................................................................................................................... 6

Executive summary .......................................................................................8

I. Introduction...............................................................................................10

II. Results......................................................................................................12 1. Introduction (see section I) .............................................................................................. 12 2. Key biotechnologies ........................................................................................................ 12 3. Biotechnology applications.............................................................................................. 18 4. Concept for elaborating indicators .................................................................................. 43 5. Input statistics and indicators .......................................................................................... 48 6. Medical and pharmaceutical applications: application-specific output and impact indicators ......................................................................................................................... 61 7. Agro-food: application-specific output and impact indicators.......................................... 79 8. Industrial manufacturing, energy, environment: application-specific output and impact indicators ......................................................................................................................... 91 9. Generic impact indicators.............................................................................................. 123

III. Conclusions ..........................................................................................129

IV. Annexes.................................................................................................132



List of figures Figure 3.1: The Industrial Biotechnology Production Chain................................................. 37 Figure 4.1: Conceptual framework for biotechnology indicators.......................................... 43



List of tables Table 2.1: List-based definition of modern biotechnology ............................................... 12 Table 2.2: Web-based information sources for description of key biotechnologies ........ 13 Table 3.1: Overview of use of key technologies in research and production in the three

sectors ............................................................................................................ 18 Table 3.2: Selected examples of biopharmaceuticals for low incidence diseases in the

US and European Markets ............................................................................. 21 Table 4.1: Typology of biotechnology indicators with examples...................................... 44 Table 5.1: Business sector input indicators from government surveys (consulting firms

when no official data) ...................................................................................... 51 Table 5.2: Public sector input indicators .......................................................................... 53 Table 5.3: Availability of business sector input indicators by application field................. 55 Table 5.4: Value of input indicators for assessing investments in biotechnology and

future potential outputs ................................................................................... 57 Table 6.1: Output indicators for medical and pharmaceutical applications of biotechnol-

ogy .................................................................................................................. 63 Table 6.2: Key indicators and methods for data collection for a study in the medical and

pharmaceutical sector..................................................................................... 71 Table 6.3: Phenomena and indicators that characterize the impact of biotechnology on

the medical and pharmaceutical sector .......................................................... 74 Table 7.1: Classification system for field trial traits.......................................................... 81 Table 7.2: Output indicators for agro-food biotechnology/GM......................................... 83 Table 7.3: Key missing data for agro-food outputs .......................................................... 85 Table 7.4: Impact indicators for agro-food biotechnology................................................ 88 Table 8.1: Output phenomena and indicators for biotechnology in the chemical sector . 93 Table 8.2: Output phenomena and indicators for biopolymers........................................ 96 Table 8.3: Output phenomena and indicators for enzymes in downstream sectors........ 98 Table 8.4: Output phenomena and indicators for biofuels............................................. 101 Table 8.5: Output phenomena and indicators for bioremediation.................................. 104 Table 8.6: Recommended output indicators for biotechnology in the chemical sector . 107 Table 8.7: Recommended output indicators for biopolymers ........................................ 108 Table 8.8: Recommended output indicators for enzymes in downstream sectors ........ 108 Table 8.9: Recommended output indicators for biofuels ............................................... 109 Table 8.10: Recommended output indicators for bioremediation .................................... 110 Table 8.11: Impact phenomena and indicators for biotechnology in the chemical sector112 Table 8.12: Impact phenomena and indicators for biopolymers...................................... 115 Table 8.13: Impact phenomena and indicators for enzymes in the downstream sectors 118 Table 8.14: Impact phenomena and indicators for biofuels............................................. 121 Table 8.15: Impact phenomena and indicators for bioremediation.................................. 123 Table 9.1: Statistics for biotechnology turnover............................................................. 124 Table 9.2: Generic impact indicators ............................................................................. 126 Table A.6.1: Data availability............................................................................................. 133 Table A.6.2: Sources for output indicators ........................................................................ 138 Table A.7.1: Data availability............................................................................................. 141 Table A.7.2: Sources for agro-food indicators................................................................... 142 Table A.8.1.1: Data characteristics chemicals ..................................................................... 144 Table A.8.1.2: Data availability of sources for chemicals (excl polymers) ........................... 148 Table A.8.1.3: Source Key Chemicals.................................................................................. 150 Table A.8.2.1: Data characteristics biopolymers.................................................................. 151 Table A.8.2.2: Data availability of sources for biopolymers ................................................. 155 Table A.8.2.3: Source Key Biopolymers .............................................................................. 156 Table A.8.3.1: Data characteristics enzymes in downstream industries.............................. 157 Table A.8.3.2: Data availability of sources for enzymes in downstream industries: (food

and feed, textile and leather, pulp and paper, mining and others) ............... 159 Table A.8.3.3: Source key for enzymes in the downstream sector...................................... 160 Table A.8.4.1: Data characteristics biofuels......................................................................... 161



Table A.8.4.2: Data availability of sources for biofuels ........................................................ 164 Table A.8.4.3: Source Key for Biofuels ................................................................................ 165 Table A.8.5.1: Data characteristics bioremediation.............................................................. 166 Table A.8.5.2: Data availability of sources for bioremediation ............................................. 168 Table A.8.5.3: Source Key for Bioremediation ..................................................................... 169 Table A.9.1: Data availability............................................................................................. 171 Table A.9.2: Sources for generic impact indicators........................................................... 172



Executive summary In response to a request from the European Parliament, the European Commission, and in particular its Joint Research Centre initiated a study aiming at providing a comprehensive assessment of the economic, social and environmental consequences, opportunities, and challenges from the application of modern biotechnology in Europe. This assessment should keep in mind major European policy goals: to become the most competitive and dynamic knowledge-based economy in the world, capable of sustainable economic growth with more and better jobs and greater social cohesion and respect for the environment. The present Task 1 prepares the ground for a number of empirical analyses within the Bio4EU study by 1) elaborating a comprehensive picture of relevant existing modern biotechnologies, 2) identifying and describing existing biotechnology applications, 3) identifying appropriate indicators to enable an analysis of biotechnology applications and

their consequences, and 4) identifying and evaluating required data and sources. A precondition for this assessment is a suitable definition of modern biotechnology. We recommend using the latest OECD definition from 2005 which defines biotechnology as “the application of science and technology to living organisms as well as parts, products and models thereof, to alter living or non-living materials for the production of knowledge, goods and services”. A combination with a list-based definition ensures that traditional biotechnol-ogies are excluded. Using the OECD definition will improve international comparability, since this definition is the most widely used in government biotechnology surveys and has resulted in several useful statistics. The analysis of applications of modern biotechnology in various industry and service sec-tors shows that the main application areas of modern biotechnology can be classified into three groups: medical and pharmaceutical applications, biotechnology applications in primary production and the agro-food sector, and biotechnology in industrial manufacturing, energy and environment. For elaborating indicators and identifying data needs a conceptual approach was developed which differentiates between three main categories. Firstly, we use input indicators which de-scribe capabilities and capacities in biotechnology. Secondly, we use output indicators that evaluate the extent of the adoption of biotechnology within the different application sectors. Thirdly, impact indicators are proposed which assess the economic, social and environmental impacts of modern biotechnology applications. The most important input indicators are of a generic nature and not disaggregated by appli-cations. Key input indicators are based on private and public R&D expenditures, the number of employees, patent data and bibliometric data. The main sources for input indicators com-prise business sector statistics from official surveys or reports, public sector statistics from official surveys or reports, database statistics such as publications and patent databases, and consulting firm statistics. Data availability is almost inversely proportional to the value of the input indicator. Availability is greatest for basic firm counts, which is a highly misleading indicator, and lowest for R&D investment and employment by field of applications, which would be among the most useful indicators. Output indicators are both sector-specific to application areas in terms of sector-specific products or processes to be measured and generic, as many of the phenomena to be measured in the different application areas are identical, such as building up biotechnology



know-how in the sector, product approval, producing biotechnology-based products, gaining market shares for biotechnology products, or replacing established processes by biopro-cesses. Sector-specific output indicators for pharmaceutical and medical applications include indica-tors for the early developmental stages, such as the share of clinical studies with novel bio-based approaches, the number of patents and publications, the specific legal framework con-ditions, such as the reimbursement situation, information to the public about bio-procedures, and finally the adoption of biotechnology processes for small molecules. For agro-food appli-cations, the most important available indicators are based on the number of GM field trials and GM acreage, but there is a need for new indicators for the use of biotechnology-based techniques, such as Marker Assisted Selection, for the development of non-GM plant and animal varieties and for the use of diagnostics and other veterinary applications. In industrial applications new production processes via biotechnology form the basis for typical indicators. Data availability and data quality are very heterogeneous in the different application areas. In the case of pharmaceutical and medical applications, we find reasonable data sources for only some of the key indicators. For GM crops we observe a nearly complete coverage of all countries and all indicators, other agro-food applications are characterised by a poor data availability. In industrial biotechnology data availability and data quality are rather poor. Important generic impact indicators include the number of employees, sales or turnover from biotechnology products, value-added from biotechnology products, and the financial costs or benefits from the use of biotechnology processes. Application-specific indicators for the medical and pharmaceutical field are morbidity, surro-gate endpoints, mortality and composite indicators such as the cost-benefit ratio of treat-ments. Indicators for agro-food applications include environmental effects such as carbon gas savings, nutrient efficiency, effects on soil erosion and pesticide use, and societal effects. Relevant indicators for industrial applications of biotechnology include environmental effects such as the ecological foot-print, energy saving, changes in greenhouse gas emissions, savings of toxic chemicals, savings of water, and effects on land use. In general, for most impact indicators, data availability and data quality are low. In many cases we find only case-specific data. In summary, a number of good indicators for all three application fields and also for the input and impact side of the use of biotechnology has been identified. However, for a considerable number of indicators, the available data (mainly based on different types of statistics) is not sufficient. Therefore, we recommend not restricting data gathering during the following em-pirical studies to the analysis of available statistical and survey materials. Rather, additional methodological approaches are required. In particular we suggest: • Including specific questions on R&D expenditure and employees in European company

surveys, • Conducting case studies, such as life cycle analyses in the case of industrial biotechnol-

ogy or cost-benefit-analyses in the case of biotechnology-based medical treatments, • Including patent and bibliometric analyses in all planned sector studies in order to provide

highly comparable indicators about biotechnology capabilities and capacities. In conclusion, this study shows the feasibility of carrying out a quantitative assessment of the use and impact of modern biotechnology. Implementing our recommendations should con-tribute to an improved evaluation of the consequences, opportunities and challenges of modern biotechnology for Europe.



I. Introduction Modern biotechnology is one of the key enabling technologies of the 21st century, with a po-tentially wide range of applications in health care, agriculture and industrial processes. How-ever, recent analyses suggest that the actual adoption of modern biotechnology by various European industry sectors could be lower than anticipated. In general, data on the actual uptake of modern biotechnology by various sectors and its socio-economic consequences in Europe are still scarce. Against this background, the European Parliament requested the Commission in late 2004 to carry out an assessment of the opportunities and challenges of modern biotechnology applications in Europe. This assessment will be carried out in the con-text of the study "Consequences, opportunities and challenges of modern biotechnology for Europe“, under the responsibility of the JRC-IPTS. The objective of the study is to provide a comprehensive assessment of the economic, social and environmental consequences, opportunities and challenges of applications of modern biotechnology in Europe, while keeping in mind major European policy goals: to become the most competitive and dynamic knowledge-based economy in the world capable of sustainable economic growth with more and better jobs and greater social cohesion and respect for the environment. The study includes a number of tasks. Task 1 provides a comprehensive picture of relevant existing modern biotechnologies, the identification and description of biotechnology applications (work package 1), the identification of appropriate indicators to enable an analysis of biotechnology applications and their consequences (work package 2), and the identification and evaluation of available sources of required data to prepare the ground for searches, surveys and interviews in different biotechnology application sectors (work package 3). Task 1 is the preparatory study for a number of following empirical analyses which comprise the core data gathering and evaluation exercise of the study. These empirical analyses will focus on biotechnology application in human and animal health; agriculture, fisheries and food and food production; industrial processes, energy and environment. This report presents the results of Task 1. It is organised in the following way: Chapter 2 defines the key technologies that are the basic tools in modern biotechnology research and production in one or more application fields. The selection of these key biotechnologies has been made on the basis of the OECD list-based definition of biotechnology (OECD 20051). Chapter 3 describes the main applications of modern biotechnology in the three sectors planned for the empirical analyses: applications of biotechnology in the medical and pharmaceutical sector, biotechnology applications in primary production and the agro-food sector and biotechnology applications in industrial manufacturing, energy and environment. Chapter 4 describes the conceptual framework which was used during Task 1 for the elaboration and assessment of indicators. In chapter 5 general input indicators are presented which illustrate the capabilities of a national system in biotechnology. Chapters 6, 7 and 8 elaborate on output indicators that are used for evaluating the adoption of biotechnology within the three sectors under consideration and on application-specific impact indicators for assessing the economic, social and environmental impacts of modern biotechnology applications. These sector-oriented chapters are organised in the following 1 OECD (2005) Biotechnology Statistical Framework, Paris.



way: In the first section output indicators are described. The elaboration starts with a description of the phenomena in the adoption process which should be measured by suitable indicators. Indicators are presented and the required data assessed in terms of availability and quality. Additional information on these indicators in particular on their availability by country are summarised in a number of tables in the annex. Finally, recommendations for the empirical sector studies in terms of indicators, sources and methods are given. The second part of these chapters discusses impact indicators which are sector specific. In chapter 9 general impact indicators which are not sector specific are presented. Chapter 10 summarises the results of the study and presents conclusions for the carrying out of the empirical analyses.



II. Results 1. Introduction (see section I)

2. Key biotechnologies

2.1 Introduction Biotechnology can be defined as ‘the application of science and technology to living organisms, as well as parts, products and models thereof, to alter living or non-living materials for the production of knowledge, goods and services’ (OECD 20052). This definition includes traditional biotechnology processes that have been used for a very long time in the food and drinks industry as well as modern biotechnological processes. The focus of the project is on modern biotechnology, although in some cases modern biotechnology combines DNA, protein or cell-based technologies with traditional processes, such as fermentation and cell culture. Table 2.1: List-based definition of modern biotechnology

Nucleic acid (DNA/RNA)-related technologies

• High-throughput sequencing of genome, gene, DNA • DNA synthesis and amplification • Genetic engineering • Anti-sense technology

Protein-related technologies

• High throughput protein/peptide identification, quantification and sequencing

• Protein/peptide synthesis • Protein engineering and biocatalysis

Metabolite-related technologies

• High throughput metabolite identification and quantification • Metabolic pathway engineering

Cellular-/ subcellular-related technologies

• Cell hybridisation/fusion • Tissue engineering • Embryo technology • Stem cell-related technologies • Gene delivery • Fermentation and downstream processing

Supporting tools • Bioinformatics Table 2.1 provides a list-based definition of the key technologies used in modern biotechnol-ogy research and production. The list includes four general categories for nucleic acid, pro-tein, metabolite, and cell-related technologies, plus a fifth category for supporting tools. Some of these tools include a number of technologies. The description of the key technologies has been made using a number of sources. Several websites that provide extensive descriptions of biotechnology are given in table 2.2.

2 OECD (2005) Biotechnology Statistical Framework, Paris.



Table 2.2: Web-based information sources for description of key biotechnologies

http://biotechterm.org/sourcebook/index.phtml http://www.fao.org/biotech/index_glossary.asp?lang-en http://www.biologydaily.com/biology/ http://biotech.icmb.utexas.edu/search/dict-search.html http://filebox.vt.edu/cals/cses/chagedor/glossary.html

2.2 Nucleic acid (DNA/RNA)-related technologies High-throughput genome, gene and DNA sequencing DNA, RNA, gene or genome sequencing is a process for determining the nucleotide se-quence of a DNA or RNA fragment, a gene or the whole genome. The genome comprises the whole hereditary information of an organism encoded in the DNA, including both the genes and the non-coding sequences. The genes are specific regions of genomic sequence that correspond to a unit of inheritance for a specific trait, disease or condition. Messenger RNA (mRNA) encodes and carries information from DNA to sites of protein synthesis. Genomics is the study of an organism's genome and the information contained in it. The rate at which ge-nomes have been sequenced has increased enormously since 1995 when traditional DNA sequencing techniques were increasingly replaced by high-throughput versions. Transcrip-tomics is the study of the expression level of genes as measured in the set of all mRNA molecules in one or a population of biological cells for a given set of environmental conditions. DNA sequencing serves three main research strategies: the identification of genome struc-tures (genomics mapping), the comparative analysis of gene sequences in order to find simi-lar sequences, and the prediction of protein structures. The most widely used method for sequencing uses fluorescent ‘tag’ molecules attached to the DNA fragments, followed by spectrophotometry to identify the respective DNA fragments by their differing ‘tags’ (which fluoresce at different wavelengths). This method can be automated and is applied in micro-arrays. Micro-arrays are one of the main technologies used in high throughput whole genome sequencing. The major advantage of micro-arrays is the extent to which the process of genotyping can be automated to sequence and analyse large amounts of DNA fragments of the whole genome. Micro-arrays are also used to analyse patterns of gene expression and the presence of biomarkers. To manufacture a DNA micro-array, cellular mRNA is used to make segments of complementary DNA (cDNA, with length of 500-5,000 base pairs), using the reverse transcriptase polymerase chain reaction (see DNA synthesis). The cDNA segments attached to a nylon or glass surface at known spots, hybridize to sample DNA. DNA synthesis and amplification DNA synthesis is the reproduction of a known sequence of nucleotides into genes or gene fragments for use in research, but also in the security sector. The synthesis is carried out through the PCR technique (Polymerase Chain Reaction). First the DNA (a double helix) that has to be copied (i. e. synthesized) is split into two separate DNA strands. After a primer has been attached to each of the strands, a complementary strand to each of the strands is made by the so-called DNA Polymerase. This results in two new double helical DNA molecules, each of which has one strand from the original DNA molecule and one that was newly syn-thesized. DNA amplification is a specific DNA synthesis process; it deals with the duplication of DNA sequences. DNA amplification is needed to detect very small amounts of DNA. Genetic fingerprinting or genotyping is the use of specific techniques for the identification of individuals and for distinguishing between individuals of the same species using only sam-ples of their DNA. As each individual has its own specific DNA profile, this is an ultimate iden-tification tool. It is used in plant and animal breeding, but also in forensic research. PCR is



one of the main technologies to produce fingerprints. In this context marker assisted selection (MAS) is to be mentioned. The idea behind marker assisted selection is that there may be genes with significant effects that may be targeted specifically in selection. Specific genes can be detected by genome mapping. Genetic engineering Genetic engineering is modifying the genotype, and hence the phenotype, by transgenesis. Transgenesis is the introduction of a gene or genes into animal or plant cells or into microor-ganisms, which leads to the transmission of the input gene (transgene) to successive genera-tions. Transgenesis can be performed by several techniques, such as injection and the ‘shot-gun’ method. The aim is to introduce new characteristics to an organism in order to increase its usefulness. The genetically modified organism can produce endogenous proteins with properties that differ from the original protein or produce entirely different (foreign) proteins. Transgenesis also can include replacing a single functional gene by a non-functional form of the gene, in order to knockout specific functions of the organism. Other terms for genetic en-gineering are gene splicing, gene/genetic manipulation or modification, or recombinant DNA technology. Anti-sense technology Anti-sense technology is the blocking of the transcription of a DNA using anti-sense mRNA. During transcription, the double stranded DNA produces mRNA from the sense strand; the other, complementary, strand of DNA is termed anti-sense. Anti-sense mRNA is a RNA-strand complementary in sequence to the mRNA. The presence of an anti-sense mRNA can inhibit gene expression by base-pairing with the specific mRNAs. This technology is used to study gene function: by switching off the studied gene by adding its anti-sense mRNA tran-script. It has applications in the treatment of genetic disorders. 2.3 Protein/peptide-related technologies High-throughput identification, quantification and sequencing There are a number of technologies that play an important role in the study of the structure and function of proteins (proteomics). They include two-dimensional gel electrophoresis, mass spectroscopy and nuclear magnetic resonance. These methods are part of the standard set of analytical research tools and are continuously being up-graded and turned into faster (medium/high) throughput versions. Gel-electrophoresis (GE) techniques are used to separate, identify and quantify levels of proteins and peptides in a mixture. Proteins consist of one or more peptides. Both peptides and proteins consist of amino acids linked by peptide bonds, but proteins are much longer (consisting of more amino acids) than peptides. In 2-dimensional GE the technique separates the proteins in two steps, according to two dimensions: isoelectic points through isoelectric focusing (IEF) and mass through sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The separated proteins can be detected by a variety of means; the most com-mon is silver staining. Mass spectroscopy (MS) is used to identify proteins or other macromolecules through their molecular weights (mass) and to sequence protein molecules (composition and order of amino acids). For the identification of proteins, the protein molecules are first separated, mostly through gel electrophoresis followed by alkylation and break-down in specifically-known ways via enzymes into peptides. Separation on the basis of their mass/charge ratio can be done by several techniques, including time-of-flight mass spectrometry, electrostatic quadrupole, confronting it with a magnetic field, or by using FT-MS (Fourier Transform – mass spectrometry). When passed through the mass spectrometer, the ionised peptides (and by derivation, the initial proteins) are identified by comparing their mass-charge spectra to those within a database of known proteins. The peptides need to be counted, done by letting them bump against a target, resulting in a number of electrons.



The nuclear magnetic resonance (NMR) technique is used to characterize the three-dimen-sional structure of proteins, peptides and other macromolecules. NMR is a physical phe-nomenon based upon the magnetic property of an atom’s nucleus. NMR studies a magnetic nucleus by aligning it with an external magnetic field and perturbing this alignment using an electromagnetic field. NMR spectrometry is the only technique that can provide detailed in-formation on the exact three-dimensional structure of biological molecules in solution. Protein/peptide synthesis Protein or peptide synthesis is the chemical construction of a known protein or peptide mole-cule. The basic methodology is solid phase synthesis. In this method molecules are bound to a bead and synthesized step-by-step in a reactant solution. The constituent amino acids are repetitively coupled to a growing polypeptide backbone which itself is attached to a polymeric support (substrate). This procedure has been automated, so it is now possible to make pro-teins via automated synthesizers. Protein engineering and bio-catalysis Protein engineering is the selective, deliberate design and synthesis of proteins in order to alter specific functions, mostly applied for enzymes in industrial production processes but also in bioremediation. The use of enzymes as catalysts to perform transformations on organic compounds is called biocatalysis. Enzymes can be used in isolated form, or inside living cell-lines or microorganisms (bacteria, fungi, yeasts). There are two general strategies for protein engineering: 1) rational design: using the detailed knowledge of the structure and function of the protein to make desired changes and 2) directed evolution. In the latter, random mutagenesis (such as DNA shuffling) is applied to a gene and a selection regime is used to pick out variants that have the desired qualities. DNA shuffling involves taking a set of closely related DNA sequences, fragmenting them randomly, and reassembling the fragments into genes. This process rapidly produces a combination of positive – i. e. desired – mutations as the output of one cycle becomes the input for the next cycle. This reiterative DNA shuffling leads to effective directed evolution and can be applied to evolve any protein rapidly, even if the structure or the catalytic mechanism is unknown. 2.4 Metabolite-related technologies Metabolites are molecules that are the intermediates and products of metabolism. They are the end product of the gene expression process and are involved in the normal growth, de-velopment, and reproduction of living organisms. Cell metabolites are also active moieties of antibiotics, therapeutic drugs, and pigments. The metabolome is the complete set of small-molecule metabolites (such as metabolic intermediates, hormones and other signalling mole-cules, and secondary metabolites) present in an organism and which are formed by metabolic pathway reactions. High-throughput technologies for identification, quantification and analysis A number of technologies that are used in protein identification and quantification are also applied to metabolites. MS and NMR are the two leading technologies for metabolomics. MS is used to identify and to quantify metabolites after separation (the mostly commonly used separation technology is gas chromatography in combination with MS). These technologies are presented in the previous section. Metabolic pathway engineering Metabolic pathway engineering includes the modification of endogenous metabolic pathways of microorganisms and the introduction of metabolic pathways into new host organisms. In addition, metabolic engineering also deals with the up-regulation of the production of mole-cules. It is one of the most important tools in the industrial biotechnology. The metabolism of microorganisms is engineered in order to improve their suitability for biotechnical processes and for efficient production of many sorts of chemical compounds. Metabolic pathway engi-



neering encompasses a combination of technologies, including technologies used in ge-nomics and proteomics studies, genetic engineering, etc. 2.5 Cellular and sub-cellular level-related technologies Cell hybridisation/fusion Basically, cell fusion combines the cell contents of two or more cells in a single cell. Two cells of different species origin can be fused in vitro into a single hybrid cell. The donor nuclei can remain separate or fuse, but during subsequent cell divisions a single spindle is formed so that each daughter cell has a single nucleus containing complete or partial sets of chromo-somes from each parental line. The hybridoma technique is the use of cell fusion techniques for the production of mono-clonal antibodies. The technique involves the fusion of plasma cells of a B lymphocyte with myeloma cancer cells. The former secretes a single antibody, while the latter confers the property of growing indefinitely in tissue culture. The fusion product of myeloma cancer cells and the plasma cells, a synthetic hybrid cell, is called hybridoma. The hybridoma produces the monoclonal antibodies that react on a single antigenic determinant of an antigen. Monoclonal antibodies are often used in immunoassays as they usually bind to only one site of a particular molecule. An immunoassay is a biochemical test that measures the level of a substance in a biological liquid using the reaction of an antibody to its antigen. The assay takes advantage of the specific binding of an antibody to its antigen. Monoclonal antibodies provide a specific and accurate biochemical test. Both the presence of antigen or antibodies can be measured. Cell and tissue culture and engineering Cell culture technologies, the in-vitro growth of cells isolated from multi-cellular organisms, are mainstream technologies. These techniques are very different for plant cell cultures and for animal and human cell cultures. Micro-propagation is a specific example of the in vitro growth and/or regeneration of plant material under controlled conditions. Tissue engineering refers to more advanced culture technologies used to induce specific animal of human cells to grow and form entire tissues that can be implanted in the human body, or to induce extant cells within the body to grow and from desired tissues via precise injection of relevant com-pounds (e. g. growth factors or growth hormones). Tissue engineering involves the use of a combination of cells, engineering materials and biochemical factors to develop biological sub-stitutes that restore, maintain or improve tissue function. Cells are generally implanted or seeded into an artificial structure capable of supporting three-dimensional tissue formation, also called scaffolds. Cells can come from the same body as that to which they will be re-im-planted, from another body, or even from other species. Embryo technology Embryo technology can consist of simply removing an embryo from a human or animal donor and immediately transferring it to a surrogate mother or it can be more complicated, involving microsurgery on the embryo and maintaining the embryo in special culture systems before transferring the embryo to the surrogate mother (including in-vivo and in-vitro embryo produc-tion). Embryo technologies that already are in use or being adapted to livestock include em-bryo transfer (animal embryos are transferred to recipients via artificial inembryonation), embryo splitting (the splitting of young embryos into several sections, each of which de-velops into an animal that is genetically identical to the others) and cloning. Embryo technol-ogy may also include a number of ancillary technologies such as in vitro fertilization, artificial insemination, hormonal manipulation, semen and embryo sexing etc. Cloning is the process of creating an identical genetic copy of the original organism through asexual processes that do not involve the interchange or combination of genetic material. As a result, members of a clone have identical genetic compositions. A technique to clone an



organism is somatic cell nuclear transfer. In this method, the nucleus is removed from an egg cell (oocyte) and replaced with a nucleus extracted from another conventional somatic cell (a cell other than a sperm or egg cell) of the organism to be cloned. The technique is also used to produce embryonic stem cells. In this case, the new egg is stimulated to start dividing and the embryonic stem cells are harvested as soon as the dividing cells have formed a blastocyst. Embryo technologies involving the targeted embryo stem cells can also be em-ployed to generate chimeric animals (animals whose cells are not all genetically identical, through somatic mutation, grafting or because the individual is derived from two or more em-bryos or zygotes), which are then used to generate the knockout animals, used mainly in re-search. Apomixis is related to cloning only applied in plants. It is biological reproduction without fer-tilization, with the result that the plant seeds are genetically identical to the parent plant. Stem cells-related technologies Stem cells are undifferentiated somatic cells that can grow into different cells or tissues of the body. Stem cells differentiate either in daughter stem cells or in any specialized cell type given the appropriate signals. This ability allows them to act as a ´repair system´. This matu-ration process is stimulated and controlled by stem cell growth factor (SCF), granulocyte colony stimulating factor (G-CSF), and by granulocyte-macrophage colony stimulating factor (GM-CSF). Basically, cell isolation and cell cultivation techniques are used, however they need to be adapted to specific requirements of stem cells, which is at the moment still very much in the development stage. Stem cells can be totipotent, pluripotent, multipotent or uni-potent, indicating their degree of potency. They can be adult or embryonic, indicating their source. Gene delivery technologies Gene delivery is the insertion of genes into selected cells of an organism. Vectors are small DNA molecules (plasmid, virus, bacteriophage, artificial or cut DNA molecule) that are used to deliver the DNA into a cell. Vectors must be capable of being replicated and contain cloning sites for the introduction of foreign DNA. In order to insert the genes, several gene transfer methods can be used: 1) non-viral methods for instance human artificial chromosomes) 2) viral vectors (retroviral vectors, adenovirus, adeno-associated virus, baculovirus expression vector). Fermentation and downstream processing Fermentation originally is the anaerobic breakdown of complex organic substances, espe-cially carbohydrates, by microorganisms, yielding energy. Today, the term fermentation is used in industry to describe both aerobic, anaerobic and microaerofilic culturing of defined microorganisms. Sometimes the term is even extended to cover the culturing of mammalian and insect cells. In a bioreactor or fermenter, a biochemical process takes place which in-volves organisms, cells, cell extracts or biochemically active substances (such as enzymes) derived from such organisms. Bioreactors are commonly cylindrical, ranging in size from a litre to several cubic metres, and are often made of stainless steel. A bioreactor can also refer to a device or system to grow cells or tissues in culture, for instance in tissue engineering. After the fermentation process is completed, a large quantity of a dilute mixture of sub-stances, products and microorganisms is produced. These must be separated in a controlled way, and the product concentrated, purified and converted into a useful form. This is the downstream processing. 2.6 Supporting tools Bioinformatics Bioinformatics is the use of techniques from applied mathematics, informatics, statistics, and computer science to solve biological problems. Bioinformatics deals with the generation/



creation, collection, storage (in databases), and efficient use of data and information from all kinds of ‘omics’ and combinatorial chemistry research. Examples of the data that are ma-nipulated and stored include gene sequences, biological activity or function, pharmacological activity, biological structure, molecular structure, protein-protein interactions, and gene ex-pression products, amounts and timing. Major research efforts in the field include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure pre-diction, prediction of gene expression and protein-protein interactions, and the modelling of evolution. The terms bioinformatics and computational biology are often used interchange-ably, although the latter typically focuses on algorithm development and specific computa-tional methods.

3. Biotechnology applications 3.1 Introduction In this chapter a description is given of the main applications of biotechnology in the three sectors planned for the empirical studies: medical/pharmaceutical sector, primary production and agro-food sector and industrial manufacturing, energy and environment. The overview in this chapter in terms of products is indicative. However, in terms of applications the descrip-tion is comprehensive.

Table 3.1: Overview of use of key technologies in research and production in the three sectors

MEDICAL/PHARMACEUTICAL SECTOR KEY TECHNOLOGIES

Therapeutics Diagnostics Vaccines

Nucleic acid (DNA/RNA)-related technologies High-throughput sequencing of genome, gene, DNA

DNA synthesis and amplification Genetic engineering

Anti-sense technology Protein-related technologies High throughput protein identification, quantification and

sequencing

Protein/peptide synthesis Protein engineering and biocatalysis

Metabolite-related technologies High throughput metabolite identification and

quantification

Metabolic pathway engineering Cellular-/ sub-cellular-related technologies

Cell hybridisation/fusion Cell and tissue culture and engineering

Embryo technology Stem cell-related technologies

Gene delivery Fermentation and downstream processing

Supporting tools Bioinformatics

AGRO-FOOD SECTOR Animal pro-

duction Crops and

forestry Molecular pharming

Nucleic acid (DNA/RNA)-related technologies High-throughput sequencing of genome, gene, DNA

DNA synthesis and amplification Genetic engineering

Anti-sense technology



Table 3.1 continued

Animal pro-duction

Crops and forestry

Molecular pharming

Protein-related technologies High throughput protein identification, quantification and

sequencing



quantification






INDUSTRIAL MANUFACTURING, ENERGY AND ENVIRONMENT Chemicals Biofuels Bioreme-

diation Nucleic acid (DNA/RNA)-related technologies

High-throughput sequencing of genome, gene, DNA DNA synthesis and amplification

Genetic engineering Anti-sense technology

Protein-related technologies High throughput protein identification, quantification and

sequencing



quantification






Table 3.1 provides an overview of the applications of the key technologies presented in chapter 2 in the three sectors. The table shows that the use of key technologies differs very much between sectors and even between sub-sectors within a sector. The table highlights the multiple uses of key biotechnologies. A prerequisite for biotechnology research for all three sectors are nucleic acid-related technologies, protein-related technologies and supporting tools in the field of bioinformatics; all the corresponding cells are shadowed. Metabolite-re-lated technologies and cellular-related technologies are used only for specific application areas. The latter technologies are characterised by an earlier stage of development and thus directly linked to a specific research area. Future impetus for the exploitation of biotechnology could result from the wider integration of these novel technologies in existing processes. The table also shows that some key technologies are less broadly applied, for instance fermenta-tion technologies that are applied in some sub-sectors of medical applications and all sub-sectors of the sector Industrial manufacturing, energy and environment.



In the following three sections the application of these key technologies and other – more sector-specific – biotechnologies in the three sectors will be presented in more detail. 3.2 Applications of biotechnology in the medical and pharmaceutical sector 3.2.1 Introduction Biotechnology is increasingly playing a role in conventional drug discovery, both as a tool box in research applications and as a means for the production of biopharmaceuticals. It is opening up new possibilities to prevent, treat and cure hitherto incurable diseases using novel methods of treatment and diagnosis. Biotech medicines such as antibodies and enzymes now account for 20 % of all marketed medicines and 50 % of those in clinical trials (EuropaBio 20053). Many biotechnology applications in health are based on the results of sequencing the human genome, leading to the identification of potential targets for both small and large mole-cule therapeutics. The deeper understanding of the genetic prerequisite of humans and ani-mals, systems approaches to diseases and advances in the development of diagnostics and therapeutics are transforming current diagnostic and therapeutic approaches to medicine. Together with new technologies such as e-medicine, this will enable a predictive and preven-tive medicine that will lead to personalized medicine (Hood et al. 20044; Bolsin et al. 20055). Through genetic engineering, biotechnology can modify different living organisms – plant and animal cells, bacteria, viruses and yeasts - to produce medicines for human use (bio manu-facturing). This aspect is further explained in section 3.2. Biotechnology has applications in the discovery and development of medicines, vaccines, diagnostics and emerging cell and gene therapies. All applications apply to the human sector, the veterinary and the animal companion sector. However, some approaches are predomi-nantly developed for human applications, as costs are high and an adequate return on in-vestment is only expected in human applications. Animal application could follow succes-sively. Some of the most promising applications of biotechnology are in the field of animal health and production, especially in areas such as assisted reproduction, increased disease resistance, nano-based diagnostic and ‘smart’ treatment delivery systems, improved vaccines and refined diagnostic techniques (MacKenzie 20056). 3.2.2 Therapeutics for Humans 3.2.2.1 Drugs Biotechnology offers different approaches for the development of new drugs: both in respect to the origin of therapeutic agents and in respect to therapeutic principles (Avidor et al. 20037). An example of the former is the identification and exploitation of new active molecules produced by marine microbiota, using improved microbial cultivation techniques and the ap-plication of DNA-based molecular methods (Zhang et al 20058). Comparative genomics applications such as the comparison of the metabolic pathways of parasites and their hosts facilitate the identification of new drug targets (Chaudhary and Ross 20059). Such advances in metabolomics (mapping the entire metabolic pathways) are bound to expedite the de-velopment of new drugs for known pathogens. The discovery of novel therapeutic modes of action such as antisense technology favours the development of new medicines for the treat-

3 EuropaBio (2005): http://www.europabio.org/healthcare.htm. 4 Hood L, Heath JR, Phelps ME, Lin B.Science. (2004): Systems biology and new technologies enable predictive and preventative medicine. Oct 22;306(5696):640-3. 5 Bolsin S, Patrick A, Colson M, Creatie B, Freestone L. (2005): New technology to enable personal monitoring and incident reporting can transform professional culture: the potential to favourably impact the future of health care. J Eval Clin Pract.Oct;11(5):499-506. 6 MacKenzie, A.A. (ed.): Biotechnology applications in animal health and production. Scientific and Technical Review, Volume 24 (1), April 2005. 7 Avidor Y, Mabjeesh NJ, Matzkin H.: Biotechnology and drug discovery: from bench to bedside. South Med J. 2003 Dec;96(12):1174-86. 8 Zhang L., An R., Wang J., Sun N., Zhang S., Hu J., Kuai J. (2005): Exploring novel bioactive compounds from marine microbes. Curr Opin Microbiol 2005 Jun;8(3):276-81. 9 Chaudhary, K.; Ross D.S. (2005): Protozoan genomics for drug discovery. Nat Biotechnol 2005 Sep, 23(9): 1089-1091.



ment of unmet medical needs, as in the field of cancer (Aboul-Fadl 200510; Coppelli and Grandis 200511). These applications are in an early developmental stage, i. e. preclinical de-velopment and early clinical testing. Advances in drug manufacturing such as improved fer-mentation technology, easier establishment of animal cell cultures and improved production methods of monoclonal antibodies (MAB) in transgenic animals (Butler 200512; Lonberg 200513), could make small market drugs more attractive for investment and reduce the phar-maceutical industry’s reliance on developing blockbuster medicines. In this respect, biotech-nology can contribute especially in the field of orphan drugs and individualized medicines. Examples for biopharmaceuticals with a large market are erythropoietin, follicule-stimulating hormone, hyaluronidase, monoclonal antibodies and tumour necrosis factors. Advances through modern biotechnology are also expected for the development of new antimicrobial agents. Against the background of multi drug resistance in hospital infections the develop-ment of novel antibiotics such as peptides and the enhancement of already available antimi-crobial drugs is an important R&D area. Products at the boundary between drugs and food are nutraceuticals. Biotechnological methods are used in their development and production. Both probiotic food, containing bacte-ria that have health effects and products that are supplemented with biotechnologically pro-duced compounds such as phytosterols, that help to regulate blood cholesterol level are examples for products already on the market. Orphan Drugs Between 20 and 30 million Europeans are affected by 5,000 rare diseases. Biotechnology provides several tools to develop diagnostics and treatments for orphan diseases, derived from the identification of new targets from the complete sequencing of the human genome. Since the EU Orphan Drugs Regulation came into force in early 2000, it has covered over 212 applications for an orphan drug designation. Among them are enzymes to treat metabolic dis-orders and cancer drugs with small incidence rates (EuropaBio 2005 14). Some examples of biotechnology drugs to treat rare diseases are given in table 3.2. Table 3.2: Selected examples of biopharmaceuticals for low incidence diseases in the

US and European Markets

Category Product Recombinant DNA Products EGF receptor Factor VIII Interferon beta-1a Interleukin-1 and 2 Somatropin Enzymes Algalsidase Algucerase Glucocerebrosidase Glucosidase Galactosidase

Source: MERIT bio-pharmaceuticals database; Rader 200515 10 Aboul-Fadl T. (2005): Antisense oligonucleotides: the state of the art. Curr Med Chem 2005;12(19):2193-214. 11 Coppelli F.M., Grandis J.R. (2005): Oligonucleotides as anticancer agents: from the benchside to the clinic and beyond. Curr Pharm Des 2005;11(22):2825-40. 12 Butler, M. (2005): Animal cell cultures: recent achievements and perspectives in the production of biopharmaceuticals. Appl Microbiol Biotechnol 2005 Aug;68(3):283-91. 13 Lonberg N. (2005): Human antibodies from transgenic animals. Nat Biotechnol 2005 Sep;23(9):1117-25. 14 www.europa-bio.org 15 Rader, R. A. (2005): Biopharmaceutical Products in the U.S. and Euroepean Markets. BioPlan Associates, Inc. 4th edition, 1-1207.



Tailor-made medicines For patients, finding the right medication with less trial and error is critical. Applications for tailor-made medicines rely on developments in pharmacogenetic testing and the knowledge of the individual metabolising situation. Healthcare biotechnology aims to bring tailor-made treatments to patients by early detection of the patient's genetic status and his or her indivi-dual response to a drug. This allows matching medicine doses and medical treatments to in-dividual patients (EuropaBio 200514). Some application have reached the clinic such as the use of Herceptin (trastuzumab), for treatment of breast cancer patients that overexpress the HER2 protein, or dose adjustment of thiopurines according to the biochemical and genetic status of the patient. Widespread application of individualized treatment is still limited due to unclear reimbursement situation and insufficient knowledge of potential applications and the procedures16. Finally, drug delivery can be improved through the use of new materials and methods based on new discoveries in bio-nanotechnology. The incorporation of drugs in nanoparticles facili-tates the delivery of the drug to specific sites, reducing adverse side reactions (Kubik et al 200517, Kayser et al. 200518). Nanoencapsulated drugs have already reached the market. However, their cost-effectiveness is controversial. 3.2.2.2 Cell-based therapies In the past few years, cell therapies with stem cells, allogenic and autologous differentiated cells, have expanded greatly as a tool to develop potential therapies for various indications. Though they are still in the stage of clinical development, they offer a way of using a person’s own cells and tissue to create prosthetic, restorative, therapeutic and even cosmetic health care solutions. Under normal conditions, damaged joint cartilage does not – or only poorly - regenerate in the body. For several years now, cell therapy for restoring knee cartilage defects has been avail-able by growing a patient's own cartilage cells to repair cartilage defects. Other tissue- engi-neered products include skin and bone replacement (Hüsing et al. 2003a19). Research on human cell and tissue-based products is currently being conducted in the regeneration and repair of bones, tendons, nerves, ligaments, heart valves and blood vessels. The overall, but still distant goal of tissue engineering is to construct in vitro human organs to overcome a scarcity of donor organs and to improve disease treatments. Research has been carried out on the urinary bladder, kidney, heart, liver and pancreas (Oberpenning et al. 199920, Humes 200021). Products are still far away from clinical use and several scientific and technical hurdles still need to be overcome (e. g. vascularisation, controlled three-dimensional structure, and coordinated action of different cell types). Cell-based cancer immunotherapies such as cell-based tumour vaccines are under develop-ment to combat cancer. This type of therapy could one day provide new efficient strategies for the treatment of several incurable types of cancer (EuropaBio 2005). Presently, however, such approaches are only in the early clinical development stage.

16 Zika et al. (2006): Pharmacogenetics and pharmacogenomics: State-of-the-art and potential socio-economic impacts in the EU. Report of JRC-IPTS 17 Kubik T., Bogunia-Kubik K., Sugisaka M. (2005): Nanotechnology on duty in medical applications. Curr Pharm Biotechnol 2005 Feb;6(1):17-33. 18 Kayser O., Lemke A., Hernandez-Trejo N. (2005): The impact of nanobiotechnology on the development of new drug delivery systems. Curr Pharm Biotechnol 2005 Feb;6(1):3-5. 19 Hüsing, B.; Bührlen, B.; Gaisser, S. (2003a): Human Tissue Engineered Products - Today's Markets and Future Prospects. Final Report for Work Package 1: Analysis of the actual market situation - Mapping of industry and products. Karlsruhe: Fraunhofer Institute for Systems and Innovation Research, 2003, 122 p. 20 Oberpenning, F., Meng, J., Yoo, J. J. & Atala, A. (1999): De novo reconstitution of a functional mammalian urinary bladder by tissue engineering. Nat Biotechnol, 17(2), 149-55. 21 Humes, H. D. (1999): Bioartificial kidney for full renal replacement therapy. Semin Nephrol, 2000, 20(1), 71-82.



Research into stem cells could result in important cell-based therapies to treat serious dis-eases and conditions such as neurodegenerative disease of the central nervous system (Si-lani and Corbo 200422), diabetes, coronary diseases and stroke, spinal cord injuries, autoim-mune diseases and skin disorders (Hüsing et al. 2003b23). Researchers are working on three types of human stem cells: adult, foetal and embryonic. The use of human embryonic stem cells is currently at the centre of an ethical and societal debate. Xenotransplantation has been a field of extensive research in the last decade. It uses existing varieties or genetically modified animals (usually pigs) and successive cloning techniques to produce organs that can be transplanted into humans. Technical barriers due to immunogen-ity and concerns about human safety through the transfer of possibly dangerous and infec-tious viruses into the human population led to a decline in interest in xenotransplantation. According to expert opinion, interest in xenotransplantation will further decline as alternative cell therapies based on human stem cells become reality. 3.2.2.3 Gene therapies Despite the high standard of today's medical treatments, and the number of already available drugs, many of the most debilitating human diseases do not yet have a cure. The molecular basis of many genetic disorders, such as haemophilia, cystic fibrosis and muscular dystrophy, has become better understood, due to the discovery of the affected genes. In some forms of cancer, genetic predisposition could play as important a role as environmental factors in tu-mour growth and malignancy. Identifying the genes that play a role in these diseases and combating their effects is one of the most promising ways to treat certain diseases. Gene therapy has entered a phase of active clinical investigation in many areas of medicine. Human clinical trials have been started for the treatment of severe immunodeficiency dis-eases, cystic fibrosis, hypercholesterolemia, haemophilia, muscular dystrophy, and many types of cancers (melanoma, prostate, ovarian and lung cancer), AIDS, and cardiovascular disorders (Kempten et al. 2004; Gosh et al 200524; Ebert and Svendsen 200525; Hideshima et al. 200526; Budak-Alpdogan et al. 200527; Cavazzana-Calvo and Fischer 200428; EuropaBio 2005). So far, the FDA has not yet approved any human gene therapy product for sale nor did the EMEA. In January 2004, SiBiono GeneTech received approval by the Chinese State Food and Drug Administration (SFDA) to commercially market Gendicine, a gene-therapy-based treatment for nasopharyngeal cancer. This seems to be the first approved gene-therapy treatment in the world (Wilson 200529). The treatment delivers a healthy copy of the anti-tu-mour p53 gene through a simple adenovirus construct that does not integrate into the ge-nome of cells. The cost of a single dose of therapy is expected to be only 360 US$. The amount of gene-related research and development occurring worldwide continues to grow rapidly. The FDA has received many requests from medical researchers and manufac-turers to study gene therapy and to develop gene therapy products. Such research could lead

22 Silani V, Corbo M. (2004): Cell-replacement therapy with stem cells in neurodegenerative diseases. Curr Neurovasc Res 2004 Jul;1(3):283-9. 23 Hüsing, Bärbel; Engels, Eve-Marie; Frietsch, Rainer; Gaisser, Sibylle; Menrad, Klaus; Rubin, Beatrix; Schubert, Lilian; Schweizer, Rainer, Zimmer, René (2003b): Menschliche Stammzellen. Abschlussbericht. TA44/2003. Bern: Zentrum für Technologiefolgen-Abschätzung beim Schweizerischen Wissenschafts- und Technologierat, 2003 337 p. 24 Ghosh K., Khare A., Shetty S. (2005): Implications of human genome and modern cell biology research in management of cardiovascular diseases. Indian Heart J. 2005 May-Jun;57(3):270-3. 25 Ebert A.D., Svendsen C.N. (2005): A new tool in the battle against Alzheimer's disease and aging: ex vivo gene therapy. Rejuvenation Res. 2005 Fall;8(3):131-4. 26 Hideshima T., Chauhan D., Richardson P., Anderson K.C. (2005): Identification and validation of novel therapeutic targets for multiple myeloma. J Clin Oncol 2005 Sep 10;23(26):6345-50. 27 Budak-Alpdogan T, Banerjee D, Bertino JR. (2005): Hematopoietic stem cell gene therapy with drug resistance genes: an update. Cancer Gene Ther 2005 Nov;12(11):849-63. 28 Cavazzana-Calvo M., Fischer A. (2004): Efficacy of gene therapy for SCID is being confirmed. Lancet. 2004 Dec 18-31;364(9452):2155-6. 29 Wilson J.M. (2005): Gendicine: The first commercial gene therapy product. Human Gene Therapy. 2005 Sept 16:1014.



to gene-based treatments for cancer, cystic fibrosis, heart disease, haemophilia, wounds, in-fectious diseases such as AIDS, and graft-versus-host disease (FDA 200530). Some clinical trials such as the gene therapy of a four-year old US-girl in the early 1990s, who was born with an adenosine-deaminase deficiency syndrome, resulted in a permanent cure (Budak et al. 2005). However, clinical trials have also shown the risks and scientific-technical problems associated with gene therapy, for example in the case of the death of Jesse Gelsinger (Smith and Byers 200231) and the development of leukaemia in patients treated with a retrovirus to cure SCID X1 (severe combined immunodeficiency) (Gaspar and Thrasher 200532). 3.2.2.4 Therapeutic vaccines Therapeutic vaccines play an important role for the control of infectious diseases in people that are already infected with a virus. The application area with the most experiences is the control of HIV by stimulating the immune system to fight HIV and slow the progression of the disease. More than 20 therapeutic vaccines to stimulate T-cell responses of persons with HIV were under development within the last 20 years, most of them in the USA, but some also in Europe (McMichael and Hanke 200333). At present, more than 15 phase I, II or III trials are ongoing involving a variety of different strategies, including more complex subunit vaccines, recombinant viral vectors, prime-boost strategies and DNA vaccines, as well as new delivery mechanisms like intranasal application (Smith and Renaud 200334). There are a number of main challenges ahead to developing an effective HIV vaccine. These lie in protein engi-neering, the optimisation of T-cell inducing vaccines, to increase the capacity to carry out phase-III trials, and to manufacture them in sufficient quantities (McMichael and Hanke 2003).35 3.2.3 Therapeutics for Animals Biotechnology plays an important role in many veterinary and companion animal areas such as infectious diseases, animal production and food-safety. A number of applications are available on the market or are in an advanced status of (clinical) development. Thus far, ge-nomics and systems biology have not been largely introduced significantly in typical veteri-nary pharmacological and toxicological research programmes. The high costs and complexity connected to these large projects often form major obstacles for research groups with limited budget (Wittkamp 2005)36. A first example of the utilisation of genomic research in the de-velopment of animal drugs is the identification of a specific enzyme, the Babesia bovis L-lac-tate dehydrogenase as a potential chemotherapeutical target against bovine babesiosis (a parasitic disease; Bork et al 2004)37. 3.2.3.1 Recombinant drugs and hormones Although product lists for veterinary drugs including companion animal drugs approved by the United States Department of Agriculture’s Center for Veterinary Biologics and the FDA’s Center for Veterinary Medicine are published regularly it is difficult to distinguish biological 30 FDA (2005): Cellular and Gene Therapy. Internet release 11/08/2005. http://www.fda.gov/cber/gene.htm. 31 Smith L. and Byers J.F. (2002): Gene therapy in the post-Gelsinger era. JONAS Health Law Ethics Regul 2002 Dec; 4(4):104-10. 32 Gaspar H.B., Thrasher A.J. (2005): Gene therapy for severe combined immunodeficiencies. Expert Open. Biol. There. 2005 Sep;5(9):1175-82. 33 McMichael A.J., Hanke T. (2003): HIV vaccines 1983-2003. Nat Med. 2003 Jul;9(7):874-80. 34 Smith, R. and Renaud, R. C. (2003): Vaccines of the future. Nat Rev Drug Discov 2003 Oct;2(10):767-8. 35 McMichael A, Hanke T. (2003): The quest for an AIDS vaccine: is the CD8+ T-cell approach feasible? Nat Rev Immunol 2002 Apr;2(4):283-91. 36 Wittkamp, R.F. (2005): Genomics and systems biology – how relevant are the developments to veterinary pharmacology, toxicology and therapeutics? J. vet. Pharm Therap 28: 235-245. 37 Bork, S.; Okamura, M.; Boonchit, S.; Hirata, H.; Yokoyama, N.; Igarashi, I. (2004): Identification of Babesia bovis L-lactate dehydrogenase as a potential chemotherapeutical target against bovine babesiosis. Mol Biochem Parasitol 2004 Aug;136(2):165-72.



and recombinant drugs among the thousands of products approved (Walsh 2003)38. Within the EU, the assessment of veterinary biotechnology products falls under the auspices of the EMEA’s Committee for Veterinary Medicinal Products. Among the recombinant products approved for veterinary use in the EU all but one are recombinant vaccines. The recombinant interferone-omega was approved in 2001 for the therapy of canine parvovirosis. In a placebo-controlled field trial the drug reduced mortality also both in the vaccinated and unvaccinated cohort of cats when it was applied after clinical signs of canine parvovirosis were observed (de Mari et al 2003)39. This led to the extended approval of Virbagen Omega for cats in 2004 (Press Release of the Committee of Veterinary Medicinal Products (14/15 March 2004)). Several recombinant drugs are currently in the status of clinical or preclinical trial. One example is the recombinant porcine interferone-alpha/gamma. In preclinical trials it could be shown that it inhibits classical swine fever virus and other important viral pathogens in dif-ferent cell lines (Xia et al 2005)40. Bone healing in dogs and cats was stimulated in a prospective clinical study on a non-glyco-sylated recombinant human bone morphogenetic protein-2 (ngly-rhBMP-2)/fibrin composite. It could be shown that it is an efficient alternative to bone autografts in dogs and cats. (Schmoekel et al 2005)41. Human recombinant factor VIIIa is discussed to be used in veteri-nary medicine under the prerequisite that the two major obstacles: immunogenity and costs can be solved (Kristen et al. 2003)42. Efficacy and safety of recombinant feline erythropoietin (rfEPO) was tested in clinical trials with anaemic cats due to chronic kidney diseases (CKD). It could be shown that treatment with rfEPO can reestablish active erythropoiesis in most cats with CKD. However one third of the tested animals developed red-cell aplasia (RCA) a type of anaemia which is refractory to additional rfEPO treatment (Randolph et al. 2004)43. Erythropoietin is also used in another context in animals. Human recombinant drugs such as erythropoietin (rHuEPO) are used for the (illegal) doping of racehorses by subcutaneous administration. The methods of molecular diagnostics described in the diagnostics paragraph such as enzyme-linked immunosorbent assays are suitable to conduct anti-doping control (Lasne et al. 2005)44. One of the first biotechnological product for animal production was bovine somatotropin (bST) a hormone that increases milk yield by an altered use of nutrients for milk synthesis, (Bauman 1992)45. In this case the Council of the European Union decided in 1999 to ban the possible use in the EU for animal welfare reasons. In the US rbST is approved. Other recombinant product such as the equine growth hormone failed for safety issues already in clinical trial. Long term therapy for this recombinant product showed to result in insulin resistance in horses with various disease states (de Graaf-Roesldema et al 2003). 46

38 Walsh, G. (2003): Biopharmaceutical Benchmarks – 2003. Nature Biotechnology 21(8): 865-870 39 de Mari, K. Maynard, L. Leun, H.M. Lebreux, B. (2003): Treatment of canine parvoviral enteritis with interferon-omega in a placebo-controlled field trial. Vet. Rec. 152(4): 105-108. 40 C.Xia, W. Dan, W. Wen-Xue, W. Jian-Qing, W. Li, Y. Tian-Yao, W. Qin , N. Yi-Bao (2005): Cloning and expression of interferon-alpha/gamma from a domestic porcine breed and its effect on classical swine fever virus. N. Vet. Immunol Immunopathol 104(1-2):81-9. 41 Schmoekel, H.G. Weber, F.E. Hurter, K. Schense, J.C. Seiler, G. Ryrz, U. Spreng, D. Schawalder, P. Hubbell J.J. (2005): Enhancement of bone healing using non-glycosylated rhBMP-2 released from a fibrin matrix in dogs and cats. Small Anim Pract 2005 Jan;46(1):17-21. 42 Kristen, A.T. Edwars, M.L. Devey, J. (2003): Potential use of recombinant human factor VIIIa in veterinary medicine. Vet Clin North Am Small Anim Pract 33(6): 1437-51. 43 Randolph, J.E. Scarlett, J.M. Stokol, T. Saunders, K.M. MacLeod, J.N. (2004): Expression, bioactivity, and clinical assessment of recombinant feline erythropoietin. Am J Vet Res.;65(10):1355-66. 44 Lasne, F. Popot, M.A. Varlet-Marie, E. Martin, L. Martin, J.A. Bonnaire, Y. Audran, M. de Ceaurriz, J. (2005): Detection of recombinant epoetin and darbepoetin alpha after subcutaneous administration in the horse. J Anal Toxicol 29(8): 835-837. 45 Baumann, D.E. (1992): Bovine Somatotropin: Review of an Emerging Animal Technology. J Dairy Sci 75: 3432-3451. 46 de Graaf-Roelfsema, E. Tharasanit, T. van Dam, K.G. Keizer, H.A. van Breda, E. Wijnberg, I.D. Stout, T.A. van der Kolk, J.H. (2005): Related Articles, Effects of short- and long-term recombinant equine growth hormone and short-term hydrocortisone administration on tissue sensitivity to insulin in horses. Am J Vet Res. 66(11):1907-13.



In the context of veterinary drugs novel principles for drug delivery and biomaterials are also discussed in veterinary applications. Senel and McClure (2004)47 review current applications of chitosan including wound healing, bone regeneration, and drug delivery for antibiotics, an-tiparasitics, anaesthetics, painkillers, growth promoters and immunomodulatory agents and vaccines. Biodegradable polymers are also discussed with applications including intravaginal devices, injectables and implantable systems in the animal health market (Winzenburg et al. 2004)48. Membrane transporter/receptor-targeted prodrug design is another trend currently under development to enhance bioavailability of drugs both in humans and animals (Maju-madar et al. 2004).49 3.2.3.2 Antiinfectious agents Antiinfectious agents, i. e. antibiotics and antifungal products are together with vaccines the most important group of biopharmaceuticals in animal health applications. With most thera-peutic antimicrobials used to treat bacterial infection in animals there are related antimicro-bials used in human medicine from the same family. Among them are aminoglycosides, amoxicillin and clavunalate, cephalosporins, polyketides, and fluoroquinolones (www.vetgate.ac.uk; release 3 February 2006). Antiinfectious agents were used in the past in the EU with three different intentions

• as therapeutic agent after the onset of an infectious disease

• as preventive agent when animals are most at risk, and animals are known to be sus-ceptible,

• as enhancing agents in sub-therapeutic concentration that should help growing animals digest their food more efficiently (growth promoters)

The application of antibiotics as growth promoters was banned in the EU by 1 January 2006. Feed additives being promoted as possible alternatives to antibiotic growth promoters include amino acids, enzymes, prebiotics, probiotics, organic acids, and immune modulators (Frost&Sullivan Market Insight 24 Nov 2005). Many of these feed additives can be produced by biotechnological fermentation processes, followed by downstream processing. 3.2.4 Molecular Diagnostics Molecular diagnostics are becoming a driving force in drug development, drug application, surveillance of human and animal health status. Applications have spread from identifying infections to include screening for cancer, hepatitis, a variety of genetic disorders and even tissue screening to minimize the risk of tissue rejection (Dutton 200550). Improved micro-array technology with cheaper process costs and new application areas lead to diversify molecular diagnostics in new directions including in vitro diagnostics. The human health benefits of bio-technology detection methodologies go beyond disease diagnosis. For example, biotechnol-ogy detection tests can screen donated blood and organs for the pathogens that cause AIDS, hepatitis and a variety of other infectious diseases (EuropaBio 2005). While traditional testing methods are still widely used in veterinary diagnostic laboratories, promising new technologies, such as biosensors and micro-array techniques, are being de-veloped. Nucleic acid diagnostic techniques such as polymerase chain reaction (PCR) have become routine veterinary diagnostic tools for rapidly screening large numbers of samples during disease outbreaks. In addition, nanotechnologies, although not yet implemented in veterinary laboratories, hold the promise of screening for numerous pathogens in a single 47 Senel, S. McCllure, S.J. (2004): Potential applications of chitosan in veterinary medicine. Adv Drug Deliv Res 56(10): 1467-80. 48 Winzenburg, G. Schmidt, C. Fuchs, S. Kissel, T. (2004): Biodegradable polymers and their potential use in parenteral veterinary drug delivery systems. Adv Drug Deliv Rev 56(10): 1453-66. 49 Majumadar, S. Duvvuri, S. Mitra, A.K. (2004): Membrane transporter/receptor-targeted prodrug design. Adv Drug Deliv Rev. 56(10): 1437-52. 50 Dutton, G. (2005): Molecular diagnostics as Clinical Tool. Genetic Engineering News 2005; 25(18): 1-25.



assay. Other biotechnologies are likely to be widely used in the future as they can improve diagnostic capabilities while reducing the time and perhaps, the costs, associated with con-ventional technologies. Although a lot of developmental work is still required, biotechnology and its applications hold great promise for improving the speed and accuracy of diagnostics for veterinary pathogens (Schmitt and Henderson 200551). Protein testing Protein micro-arrays and immunoassays help to determine the molecular status of a certain disease such as some types of cancer and guide the clinician toward the choice of optimal therapy. About 1500 different proteins have recently been identified in the blood, and a num-ber of potential new markers of diseases have been characterized. Thus haematology in combination with micro-array technology offers enormous promises of plasma/serum prote-omic analysis for diagnostic/prognostic markers and information on disease mechanisms (Thadikkaran et al. 200552). Currently nearly one hundred companies are active in the field of micro-arrays (Gershon 200553). However it is also generally accepted that new insights will not be gained by simply acquiring more and more gene expression data and that it is no longer sufficient to focus on the 25,000 protein-coding genes that make up roughly 2 % of the human genome. Exploring the role and diversity of non-coding DNAs is equally important. Molecular Imaging Molecular imaging (MI) combines new molecular agents with traditional imaging tools to cre-ate targeted, tailored therapies with the ability to simultaneously find, diagnose and treat dis-ease. Currently being investigated for numerous applications - including oncology, cardiology and neurology -- molecular imaging offers significant benefits over standard diagnostics and treatments (www.mi-central.org). For example diagnostic peptides (5-15 amino acids) can be used to specifically bind to receptors at the surface of tumours. This allows the exact determi-nation of solid and metastatic cancers followed by a surgical, chemotherapeutic or radiologi-cal therapy (Zitzmann et al. 2005)54 DNA-based testing DNA-based testing has a long tradition for differential diagnostic of infectious disease. The most prominent technique in this context is PCR technology as described in chapter 1.2. In clinical diagnostics, a specimen of genetic material weighing only one-trillionth of a gram can be repeatedly copied by PCR to provide sufficient material to detect the presence or absence of a virus as well as to quantify its levels in the blood. PCR tests were the first that could accurately measure the amount of HIV in a patient’s blood. This provides reliable information on the disease course and shows when changes are needed in a patient’s medication. DNA-based testing opened the horizon to genetic testing. Genetic testing The wealth of genomics information made available by the Human Genome Project is greatly assisting doctors in diagnosing hereditary diseases. There are currently over a thousand hu-man hereditary diseases that can be identified using genetic tests (EuropaBio 200555). The majority of these tests detect the presence of mutations in a single gene that can cause monogenic (single gene) disorders, most of which are relatively rare. These tests can also identify patients with a genetic propensity to develop diseases caused primarily by environ-mental factors or diet, giving patients an opportunity to prevent the disease by avoiding the

51 Schmitt, B. and Henderson, L.: Diagnostic tools for animal diseases. Rev sci tech Off int Epiz, 2005, 24 (1), 243-250. 52 Thadikkaran L., Siegenthaler M.A., Crettaz D., Queloz P.A., Schneider P., Tissot J.D. (2005): Recent advances in blood-related proteomics. Proteomics. 2005 Aug;5(12):3019-34. 53 Gershon, D. (2005): More than gene expression. Nature. 2005 Oct 20;437(7062):1195-8. 54 Zitzmann, S. Knapp, E.-M. Mier, W. (2005): Spezifische Darstellung und Therapie von Krebserkrankungen. Laborwelt 6: 21-23. 55 www.europabio.org



environmental triggers. Genetic testing is also critical to the development of pharmacogene-tics, which uses biotechnology-based diagnostics to better diagnose disease and provide new ways to match medicine doses and treatments to the individual (EuropaBio 2005). Up to now only few examples such as the determination of the Herceptin receptor status in breast can-cer patients are clinically widespread. Other applications such as the analysis of the TPMT status (thiopurine methyl transferase) are developed and could be used in the clinic. Due to various reasons as discussed into more detail in section 3.1.2.1 the diffusion into daily clinical practice is still missing. 3.2.5 Vaccines Immunization is rightly regarded as one of the great medical successes of the 20th century. Recent outbreaks of West Nile Virus (WNV), severe acute respiratory syndrome (SARS), avian influenza and monkeypox, as well as threats from bioterrorism, have increased the in-terest in developing new vaccines. Vaccines can also play a role in eradicating HIV, although this requires overcoming serious problems due to the high degree of antigenic variability among HIV strains (Smith and Renaud 2003). Traditional vaccination employed whole, attenuated infectious agents, with the vaccine prompting an immune response to protein structures. A new approach is the development of vaccines based on the carbohydrates on parasite surfaces instead of proteins. Some patho-gens like Trypanosoma brucei, a protozoan that causes sleeping sickness, can change their protein coat every two weeks, which makes it very difficult for the immune system to develop a sufficient immune response. Carbohydrates in the cell walls are changed less frequently, or not at all, and therefore present a much more stable target. An Australian group is currently working on vaccines based on these substances against the cause of malaria, Plasmodium falciparum (Dennis 200356). A third strategy involves DNA vaccines. Several DNA-based methods of immunization such as pure DNA, DNA conjugated to a protein allergen, and plasmid DNA, have shown promise in animal models of several disorders. Some of these DNA-based therapies have entered phase I/II clinical trials (Liu and Ulmer 200557). Promising application areas are immunothera-py for cancer (Choo et al. 200558), severe respiratory syndrome (Zhang et al. 200559), and allergic disease (Weiss et al. 200560). These applications show the close connection between traditional vaccination as a preventive measure and novel therapeutic approaches for vacci-nation. Research in vaccine adjuvants has increased in the last years and resulted in promising approaches. The field is moving rapidly. Mucosal vaccine delivery systems are specifically designed to allow vaccines to enter the body through nasal or oral mucosal surfaces, avoiding invasive vaccination techniques. Additional components are necessary to protect antigens from degradation and promote their interaction with the host tissue (O'Hagan and Valiante 200361). Products in this field of application have already entered the market. Vaccination continues to be the main approach to protecting animals from infectious dis-eases. Until recently, all licensed vaccines were developed using conventional technologies. However, the introduction of modern molecular biological tools and genomics, combined with a better understanding of not only which antigens are critical in inducing protection, but an appreciation of host defences that must be stimulated, has created new opportunities to de-

56 Dennis, C. (2003): Sweet revenge. Nature 2003, Vol. 423, pp. 580-582. 57 Liu MA, Ulmer JB.: Human Clinical Trials of Plasmid DNA Vaccines. Adv. Genet. 2005;55C:25-40. 58 Choo A.Y., Choo D.K., Kim J.J., Weiner D.B. (2005): DNA vaccination in immunotherapy of cancer. Cancer Treat Res. 2005;123:137-56. 59 Zhang D.M., Wang G.L., Lu J.H. (2005): Severe acute respiratory syndrome: vaccine on the way. Chin Med J (Engl). 2005 Sep 5;118(17):1468-76. 60 Weiss R., Hammerl P., Hartl A., Hochreiter R., Leitner W.W., Scheiblhofer S., Thalhamer J. (2005): Design of protective and therapeutic DNA vaccines for the treatment of allergic diseases. Curr Drug Targets Inflamm Allergy 2005 Oct;4(5):585-97. 61 O'Hagan, D.; Valiante, N. (2003): Recent advances in the discovery and delivery of vaccine adjuvants, in: Nature Reviews Drug Discovery 2003, Vol. 2, No. 9, pp. 727-735.



velop safer and more effective vaccines (Rogan and Babiuk 200562). The last ten years have seen the development of rDNA vaccines, which, when used in association with appropriate diagnostic kits, make it possible to distinguish vaccinated from infected animals. In this con-text it is important to distinguish between DNA-based vaccines and live recombinant vac-cines, that are based on a mutant strain (e. g. marker vaccine against Aujeszky’s disease). Protein-based vaccines and diagnostic systems might be superseded by the DNA-based systems by the middle of the 21st century (McKeever, Rege 199963). Different applications include vaccines against Aujeszky’s disease, classical swine fever, ra-bies, avian diseases and rinderpest. DNA vaccines constitute a revolution in the concept of vaccination, which was previously based on the injection of a protein or a medium expressing a protein. It is now possible to induce immunisation by direct injection of the gene that codes for the immunogenic antigen (Vannier and Martignat 200564). The example of coccidiosis shows how the understanding of the gene functioning can lead to a concerted action of nutritional and vaccination strategy. Coccidiosis is a ubiquitous intestinal protozoan infection of poultry seriously impairing the growth and feed utilization of infected animals. Conventional disease control strategies rely heavily on chemoprophylaxis, which is a tremendous cost to the industry. Existing vaccines consist of live virulent or attenuated Eimeria strains with limited scope of protection against an ever-evolving and widespread pathogen. Recent progress in functional genomics technology facilitates the identification and characterization of host genes involved in immune responses as well as parasite genes and proteins that elicit protective host responses. This allows the design of nutritional interventions and development of vaccination strategies (live and recombinant vaccines) against coccidio-sis (Dalloul and Lillehoj 200565). Vaccines against veterinary helminths have focussed in the past on identifying protein anti-gens. Notable successes have been achieved for some cestode parasites, where recombi-nant proteins have been developed into highly effective vaccines. Increasing evidence suggests that parasite glycan moieties may provide an alternative source of vaccine antigens, and increased attention is now being given to this class of compounds. In addition to identi-fying candidate protective antigen(s), an increased research effort is needed to develop appropriate strategies for the formulation and delivery of helminth vaccines. (Hein and Harri-son 2005)66 3.2.6 Barriers for the application of biotechnology in the medical and pharmaceutical

sector In contrast to many other application areas, healthcare biotechnology has a broad public acceptance. Scientific-technical barriers due to a very early stage of development are present in some application areas such as metabolomics and proteomics. According to expert opinion, public research could benefit from greater unification of research efforts and more infrastructure funding. Public research is especially limited in fields that require large-scale equipment and high speed computer technology. Stem cell research and gene therapy are both application areas in an early developmental stage. They are characterized by scientific-technical barriers such as a lack of understanding of differentiation for the rational use of stem cells and tissue engineered products and the non-directed integration of vectors in the case of gene therapy. Both topics are subject to intensive research.

62 Rogan, D. and Babiuk, L.A. (2005): Novel vaccines from biotechnology. Rev sci tech Off int Epiz, 2005, 24 (1), 159-174. 63 McKeever, D.J. Rege, J.E.O. (1999): Vaccines and diagnostic tools for animal health: the influence of biotechnology. Livestock Prod Sci 59: 257-264. 64 Vannier, P. Martignat, L. (2005): Nouveaux vaccins et nouvelles perspectives therapeutiques d’intérêt vétérinaire issus des biotechnologies: examples d’applications. Rev Sci tech Off int Epiz 24(1): 215-229. 65 Dalloul, R.A. Lillehoj, H.S. (2005): Recent advances in immunomodulation and vaccination strategies against coccidiosis. Avian Dis. 2005 Mar;49(1):1-8. 66 Hein, W.R. Harrison, G.B. (2005): Vaccines against veterinary helminths. Vet Parasitol Sep 30;132(3-4):217-22.



A barrier for the development of novel drugs and tailor-made medicines lies in the accessi-bility of clinical data. Due to data protection laws and commercial confidentiality concerns, clinical data from private and public sector studies is often unavailable for use in applications such pharmacogenetic testing. Fundamental researchers at universities sometimes are unaware of the fact that the basic data they are generating in fundamental research may have potential for applications. Many lack information and knowledge about the innovation change and about global markets rele-vant to what they are doing. This could be changed in three ways: (1) Universities and the relevant units of the university should develop their innovation

policy. (2) The new generation of researchers, the doctoral students should be offered training

in how fundamental research can generate innovations, how to recognize commercial potential and how to protect and take forward the innovation to those who will de-velop it further and commercialize it.

(3) Fundamental researchers should work in closer proximity (intellectual and physical) with the exploiters. This can be achieved, for example, in so called centres of com-petence, where top researchers develop their research plan together with exploiters, making use of shared infrastructure.

A barrier for public sector research is access to skilled staff. In some disciplines, such as bio-informatics, systems biology and clinical research, there is a lack of skilled staff, partly be-cause the public sector has to compete with the private sector. Another barrier is the limited availability of early-stage and start-up venture capital, which can prevent the establishment of new firms. After over-investment in the late 1990s and 2000, culminating in the collapse of many new technology start-ups, venture capital has shifted in-creasingly into late-stage investment. The peak in seed and early-stage venture capital in 2000 has been followed by a continuous decline until 2003. In 2004, start-up investments in-creased again by 13 %, but still represented a small share of total investments (6 %). Seed investments still fell and represent only 0.4 % of all investments (EVCA 200567). This trend is impeding the biotech industry as a whole, but has particular relevance for the biomedical sector, given both high investment requirements and high risk. Barriers for commercialisation result from a lack of guidelines and clear regulations and the different attitude related to IPR matters in Europe, Canada and the US such as patentability of higher life forms. Administrative policies are needed to address possible conflicts and ensure research participant safety as cellular therapies progress from research laboratories to the patient's bedside. Several policies are required: to ensure minimum standards of quality for emerging products before human clinical trials, to enforce consistent reporting requirements for private and public cellular research, to minimize financial conflicts of interest and to address identified conflicts, and, in some jurisdictions, to limit private litigation. These policies would help preserve the objectivity of the review process and ultimately increase participant safety (Yim 200568). In some cases barriers for the use of biotechnological developments can be found in the lack of knowledge of users and multiplicators. As shown in a current EU study69 on genetic testing efficient biochemical tests are already introduced for many applications. Physicians who have not been trained in genetics are reluctant to use the new methods as they feel unfamiliar with the new technology and lack the knowledge to read the results and draw conclusions.

67 EVCA (2005): EVCA Final Activity Figures for 2004. Internet release 11/20/2005: http://www.evca.com/images/ attachments/tmpl_8_art_166_att_795.pdf. 68 Yim R. (2005): Administrative and research policies required to bring cellular therapies from the research laboratory to the patient's bedside. Transfusion. 2005 Oct;45(4 Suppl.):144S-58S. 69 Zika et al (2006): Pharmacogenetics and pharmacogenomics: State-of-the-art and potential socio-economic impacts in the EU. Report by JRC-IPTS.



Another barrier is the unclear reimbursement situation for many new products and therapies such as tissue engineered products and pharmacogenetic testing. The extent to which inno-vative health technologies are reimbursed by private insurance or national healthcare sys-tems is partly a political debate over the cost of health care and partly due to issues of cost-effectiveness. Only cost-efficient health technologies are likely to be reimbursed. 3.3 Biotechnology in primary production and the agro-food sector 3.3.1 Introduction Biotechnology has many applications within the agro-food sector that are largely based on using biotechnology techniques to improve breeding programmes. These include the use of marker assisted selection (MAS) (which involves the use of genomics tools such as marker gene identification, genome mapping etc) to speed up conventional breeding, and genetic modification (GM), in which a gene from one species that codes for a desirable trait is in-serted into the genetic material of another species. In addition, there are many other potential applications of biotechnology, such as the use of DNA fingerprinting and molecular diagnos-tics for identification, traceability, and food/feed safety applications. Marker assisted selec-tion/breeding may be viewed as a more complex application of diagnostics, where DNA-based markers are combined with other tools such as quantitative trait loci (QTL), genetic maps, high-throughput tools etc. in order to increase the response to conventional selection. The boundary between biotechnology applications in agriculture and that in food or industrial processing is unclear. This section is limited to the use of biotechnology to produce feed/food, fibre, or industrial feed stocks for use in foods and in food and industrial processing. It does not discuss the application of biotechnology in food processing itself, such as the use of en-zymes produced through GM bacteria in cheese manufacture. Broadly defined, agricultural biotechnology covers all biotechnology applications to food, feed, and fibre. This includes six main areas of application; clustered in three groups. Firstly, crop production, horticulture and silviculture (forestry), secondly, animal husbandry, fisheries and aquaculture and insects and finally molecular farming. Each area of application has distinctive characteristics, is of different relevance to the EU, and faces different barriers to adoption within Europe. 3.3.2 Animal husbandry, fisheries and aquaculture, and insects 3.3.2.1 Animal husbandry Biotechnology has applications in animal breeding, in feed (part of plant biotechnology) and other additives production, molecular pharming, animal health, and DNA fingerprinting for food safety or tracing GM use. Both Marker Assisted Selection (MAS) and GM can be used to improve animal strains and breed animals with greater precision than conventional breeding method alone. Also, the use of genomic technologies to identify genes involved in serious inherited diseases can help animal breeders select the unaffected animals and improve the characteristics of their stock. A possible application of GM breeding (or marker assisted se-lection) is to develop dairy cows that produce more nutritious milk. This is currently in the re-search stage. One of the first applications of GM technology was the development and production of bovine somatotropin (bST) to increase milk production in the dairy industry. Injections of recombinant bST in dairy cows increases milk yield, productive efficiency (milk/feed), and decreases ani-mal waste. rBST is used commercially in 19 countries worldwide, but is not approved for use in Europe. A second development is porcine somatotropin (pST) for the swine industry, which increases muscle growth and reduces body fat deposition, resulting in pigs that are leaner and of greater market value. In the US, pST is undergoing testing for FDA evaluation. pST is currently approved for commercial use in 14 countries. As described in chapter 3, bio-pharmaceuticals, vaccines, and diagnostics have many appli-cations to animal populations, both for disease prevention and for treatment. One example is a monoclonal antibody (MAb)-based diagnostic test for brucellosis in cattle, a bacterial dis-



ease which often causes cows to abort pregnancies and which can infect farmers and people who drink milk from an infected cow. Brucellosis vaccines can protect animals from abortion but vaccinated cows can still carry the disease. The MAb test can distinguish between cattle that carry the brucellosis bacterium and those that have only been vaccinated, whereas con-ventional tests cannot distinguish between the disease-causing microbe and the vaccine. Several technologies are in use for animal breeding, including in-vitro fertilization (IVF) and embryo transfer. The latter is most frequently used in cattle to increase the production of off-spring from cows with desirable traits (it is not cost effective as a means of increasing the size of average stocks). Embryo transfer is largely a traditional biotechnology, with the first successful use in 1890 in rabbits and the first use in cattle in 1949. It requires minimal training, with ranchers being able to use this technology without expert assistance. However, embryo transfer can be combined with more modern biotechnologies to improve outcomes or efficiencies. For example, it can be used with sex selection technologies to improve output of a more economically valuable sex, or combined with embryo bisection to further increase the number of offspring. In the future, embryo transfer could be combined with nuclear trans-plantation to produce clones, if technical difficulties with cloning are solved. Embryo transfer, by producing multiple offspring, can also be used to identify genetically inferior breed stock70. Animal applications of biotechnology are highly relevant to the EU, due to the economic im-portance of animal husbandry in European agriculture. These applications provide potentially substantial savings in feed inputs and in healthcare and stock management costs, particularly in intensive dairy, pork, and poultry production. Improved breeding programmes also offer benefits through an improvement in the quality of animal products. 3.3.2.2 Fisheries and aquaculture Three types of biotechnology are currently in use in fisheries and aquaculture: recombinant DNA biotechnology is used to develop GM varieties of fish for aquaculture, marker technology is used to improve breeding programmes in aquaculture, and DNA fingerprinting is used for the management of wild fish stocks (including traceability)71. The main applications are for faster growing fish species, controlling pests, and for fish stock management. Pharmaceutical production is a small area that is covered in section 3.2.4. GM methods have been used to increase growth rates and food conversion efficiency in At-lantic salmon by inserting a Chinook salmon growth hormone gene that is switched on year-round, thereby fostering growth year-round, rather than mainly in the summer. The variety, marketed as AquaAdvantage® salmon, can reduce marketable growth times by half. The product is not yet available within Europe or elsewhere as it is awaiting regulatory approval. A second major application is to increase the immunity of fish and shellfish to pests, such as bacteria and viruses. Research in this area has developed new GM strains of molluscs with improved disease resistance. DNA fingerprinting can also be used to identify fish diseases and parasites in farmed populations and distinguish between harmful and benign diseases. For example, oysters can be affected by diseases that are difficult to distinguish. Some cause high mortality rates and require the closure of the affected oyster farm, while others are rela-tively harmless. DNA fingerprinting has several applications for the management of wild fish populations, such as distinguishing between different stocks of migrating fish. A fishery can be closed if an en-dangered stock is discovered swimming with another stock. DNA fingerprinting can also be used to determine the factors that improve the survival success of wild species that are re-leased from hatcheries. For instance, survival can vary by age, location, and conditions at time of release. 70 See Hasler JF. Factors influencing the success of embryo transfer in cattle, 23rd World Buiatrics Congress, Quebec City, 2004; Betteridge KJ. A history of farm animal embryo transfer and some associated techniques. Animal Reproduction Sciences 79:203-244, 2003. 71 Future Fish: Issues in Science and Regulation of Transgenic Fish, PEW Initiative on Food and Biotechnology, Washington DC, January 2003.



There are several potential applications of biotechnology for European aquaculture, particu-larly for salmon and molluscs (clams and oysters). The relevance of biotechnology for managing European wild fish stocks partly depends on the economic value of migratory fish stocks that are subject to mixing of different sub-populations. In this context, biotechnology applications also include traceability in terms of distinguishing between farmed and wild-har-vested products and the prevention of illegal over-fishing. 3.3.2.3 Insects The main use of biotechnology is to develop GM insects, or vectors carried by insects, for pest resistance and pest control. An exception is to develop an insecticide resistance honey-bee. All products so far are in the research stage. Research on pest resistance includes breeding programmes to develop medflies (a serious tree fruit pest) that would reduce infes-tation levels, either through male-only strains or strains that pass along a fatal trait to de-veloping offspring. Other research involves developing a symbiont of the vector of Pierce’s disease to kill the bacteria that cause Pierce’s disease in grapes, and developing GM bacteria to express proteins that block the transmission of rice stripe virus by planthoppers. The relevance within the EU of the application of biotechnology to insects is very high, par-ticularly to develop methods of controlling insect pests or insect-vectored pests for high value perennial crops such as vineyards and tree fruits and for high-value horticultural crops. The use of GM insects as a method for controlling insect pests could be an environmentally bene-ficial and low cost solution for pest management. 3.3.3 Crop production and forestry Biotechnology has a large number of applications to both food and non-food crops and con-stitutes one of the largest areas of application of biotechnology to date. There are two main applications: MAS (marker assisted selection) to speed up conventional breeding pro-grammes and the development of GM crops. MAS is probably used at this time by all Euro-pean seed firms, based on expected adoption rates in 1999. 3.3.3.1 Crop production using genetic modification Globally, the visible commercial use of biotechnology in agriculture is dominated by the appli-cation of GM technology to crops, with the use of MAS in conventional crops difficult to iden-tify. This is largely due to first generation GM applications based on a single gene insertion that confers either herbicide resistance or pest resistance (Bt varieties). Almost all GM crop use is currently limited to four crop species; soybeans, maize, canola, and cotton. This is partly due to the combination of economic and technical factors. Given the high costs of developing GM varieties, seed firms concentrated their research on major global crops with few technical barriers to GM. These provided the greatest opportunities for earning a return on their investments. Gradually, the cost of GM has declined and the technical barriers to GM modification in other major crops such as rice and wheat have been overcome. Consequently, there are now GM varieties of wheat that should shortly be available and GM has been applied to small market crops such as papayas and some horticultural crops such as lettuce. There are six main types of GM crops: grains, horticulture and vines, oil crops, fruit trees, sugar beets, and non-food crops (primarily cotton). Second generation GM crops based on improved product quality, or value-enhanced crops (VEC), are also commercially available in canola, carnations, peanuts, soybeans and sun-flowers. They account for only a very small percentage of total GM crop acreage. The product quality characteristics that have attracted the most attention concern the characteristics of oils and fats. Other product quality characteristics that are in the research stage involve the iron or beta-carotene content of rice, storage and ripening characteristics, and protein content. Many applications of biotechnology to crop production have commercial applications within the EU, particularly for sugar beet, wheat, rapeseed (Canola) and maize. Bt-maize was grown



in Europe in 2005 commercially in five countries (ES, FR, CZ, PT, DE), however on a very limited area corresponding to about 0.5 % of the total acreage. Soybeans and cotton are not as widely grown within the EU as in other countries such as the United States, South Ameri-ca, and China. 3.3.3.2 Silviculture (forestry) Biotechnology applications to forestry (excluding orchard fruits) include the use of MAS and GM in breeding programmes, and micropropagation, particularly using somatic embryogene-sis. Most biotechnology applications in tree breeding are still in the research stage and limited to identifying markers or sequencing the genome of a few genera such as populus (aspen and poplar), pinus (pine), eucalyptus and picea (spruce). Compared to breeding programmes for crop plants, tree breeding is in a very early stage, with all plantation trees for wood and fibre largely based on wild varieties. The only commercial GM tree plantation is in China. Research on GM trees covers herbicide tolerance, resistance to drought and stress, wood lignin con-tent, and pesticide resistance. A potential application of biotechnology in silviculture is to create pest resistance in important wood and fibre tree varieties (pine) and in ornamentals and street trees (elms, chestnuts, California oaks) that have been damaged by introduced pests. For example, the gene coding for Bt has been experimentally introduced into poplar varieties to control leaf-eating insects. Faster growing species is an important goal, but so far GM for this purpose is in the early ex-perimental stages and based on higher efficiency of nitrogen assimilation and modification of gibberellins synthesis. A major use of wood is in paper production and as a source of energy. Biotechnology can potentially reduce costs by producing varieties with modified lignin that is more suitable for paper manufacture, or types of wood that are suited for specialty papers, such as for high quality colour printing. An alternative is to reduce paper costs (both economic and environ-mental) by developing better ligninolytic enzymes to break down lignin. For bio-energy, high lignin tree varieties are preferred as they produce more energy per unit weight. Micropropagation covers in vitro methods of vegetative multiplication of large numbers of clones through root cuttings, organogenesis, and somatic embryogenesis. Root cutting tech-niques are widely used for angiosperms (broadleaf trees) but are commonly viewed as part of modern biotechnology. It is more difficult to use this technique for conifers, where somatic embryogenesis (SE) has attracted a lot of research attention, although not all technical prob-lems have been solved. A major potential use of SE (with or without MAS) is to speed up tree breeding programmes. Tree varieties often need to be grown for six or more years before it is known if desirable traits are expressed, resulting in 15 to 20 years to develop a new variety, compared to about 8 years for an annual crop plant. At six years of age, the tree is too old for use in vegetative propagation. Different varieties developed by SE can be both grown and some clones frozen. The clones for the successful varieties can then be thawed and propa-gated, significantly reducing the time required for developing a new tree variety. The relevance of biotechnology to forestry within the EU is limited by several important eco-nomic constraints. The main future growth area for wood and fibre is in the tropics and semi-tropics, where biomass production is many times greater than in the temperate forest zones of the EU. As an example, one hectare of plantation in the tropics produces 40 cubic metres of wood per year, with a harvest age at six years. In contrast, a hectare of forest in Sweden produces 2 cubic metres per year with a harvestable age of 60 years. Not surprisingly, there is far greater interest in breeding new varieties of fast-growing short rotation trees for wood and fibre in high growth tropical and sub-tropical zones. Second, Europe currently has a sur-plus of wood, with annual removal only 60 % of annual growth. This reduces incentives to invest now in new plantations, although the balance should turn negative by 2050. The net result is that there has been very little private sector interest in using GM or MAS biotech-nology to develop new wood and fibre tree varieties for temperate Europe. It is possible that



once current temperate forests have been fully exploited, most production will shift to warmer countries. The main relevance of biotechnology to European silviculture is therefore for product quality rather than quantity and for pest resistance in ornamentals and street trees. 3.3.4 Molecular farming Plants and animals can be modified to express complex molecules such as spider silk or pharmaceuticals for human and animal use. The most developed application is for pharma-ceuticals, where there are three main uses: production of vaccines, diagnostics, and large molecule bio-pharmaceuticals. In most cases the pharmaceuticals need to be extracted from the plant or animal (such as from goat milk) in order to be useful. A possible exception is transgenic plants that produce vaccines that could be directly consumed by humans. Re-search in this area is still in the laboratory stage. For diagnostics, researchers have produced transgenic tobacco plants that express the Hepatitis B core antigen. This antigen is used to screen blood for Hepatitis B. Corn, rice ca-nola, tobacco, tilapia, and goats have been genetically modified to produce specific human proteins with therapeutic benefits. Examples include the anti-coagulant Hirudin, produced in transgenic canola, and human clotting factor VII, produced in the fish species tilapia. 3.3.5 Barriers for the application of biotechnology in the agro -food sector Animal applications of biotechnology within the EU faces serious ethical barriers, especially for GM animals for food uses (GM animals for molecular pharming is less controversial) and for GM supplements, particularly bST, and xenotransplantation. These raise ethical concerns about the impact of biotechnology on animal welfare and rights. Public acceptance of GM animals for food is likely to take much longer to achieve than public acceptance of GM crops. For the foreseeable future, the application of biotechnology to animal breeding is likely to be based on MAS rather than on GM technology. The main barriers to the adoption of biotechnology to European fisheries include environ-mental, public health, and economic factors. The major environmental concern is the escape of GM fish from open-water pens into surrounding waters. This could reduce the genetic di-versity of the wild population if the farmed fish mate with sexually compatible wild fish, or es-caped transgenic fish could become an invasive species that replaces wild fish stocks. Public health concerns include accidental changes to the edibility of GM fish and other marine ani-mals due to increases in allergens, toxins, or hormones due to the change in the genetic make-up. The main barriers for the application of GM technology to insects are environmental, techni-cal, and economic. Because their additional traits remove some of the biological boundaries that differentiate them from their non-GM counterparts, GM insects could become agricultural or environmental pests. As an example, an insecticide resistant honeybee could be a disaster if the honeybee interbred with aggressive varieties of bees with little agricultural value and if resistance was to a broad-spectrum of insecticides. The main technical barriers concern differences between laboratory results and field results, particularly because it is more difficult to control a field release of a GM insect than a GM plant. Consumer resistance to GM crops is the single largest barrier to the adoption of GM crops within Europe. There is no visible opposition to the use of MAS in plant breeding pro-grammes, however. The main economic problem raised by consumer resistance is likely to be experienced by the dwindling number of small seed firms that lack subsidiaries in countries such as Argentina, the United States, China, or Canada where GM crops are widely grown. The larger European seed firms (BASF, Bayer, Syngenta, KWS and Limagrain) are less affected because they have extensive research and marketing operations in countries where GM crops are permitted. As long as CAP distorts farm gate prices and as long as the financial benefits of GM crops are low, there are unlikely to be significant economic effects of GM crops on the European agricultural sector.



The main barriers to adoption of biotechnology to forestry concern gene flow and tri-trophic effects, where GM forest plantations could have a negative impact on the food chain. Gene flow is of greater concern for some forestry species than in agriculture because of the size of forest plantations and the distance where wind spread pollen is viable- 600 km for some pinus species. Another environmental concern has economic implications. New tree varieties are likely to be grown in large, mono-culture plantations. An error, such as an unknown suscepti-bility to a pathogen or an undesirable phenotypic trait, can lead to plantation failure, where the entire tree crop is lost or damaged. Depending on the harvest cycle, this can take many years to show up, increasing economic risk. As noted above, the main economic obstacle is that the future growth area for wood and fibre is in the tropics and semi-tropics, where biomass pro-duction is many times greater than in the temperate forest zones of the EU. The barriers to ‘molecular pharming’ include concerns over bio-pharmaceuticals entering the food supply (a problem that could be avoided by not using food plants such as canola, corn or rice) and economic competition from alternative methods of producing large molecules. At this time, it is not clear which method of producing bio-pharmaceuticals will have a cost advantage over the long term. Finally, many applications of biotechnology in agriculture, including both GM and non-GM biotechnologies, must compete with alternative technologies to achieve similar ends. Conse-quently, a major barrier consists of competitive alternatives, such as the ability to develop conventional crop varieties with desirable traits at less cost than developing varieties using MAS or GM, or using feed additives instead of new varieties of feed crops to provide livestock with trace nutrients. 3.4 Biotechnology in industrial manufacturing, energy and environment 3.4.1 Introduction Industrial biotechnology uses biotechnological processes, mainly based on fermentation and biocatalysis (enzymatic processes), to produce a large variety of products. The distinction between traditional biotechnology and modern biotechnology is especially in this sector hard to make. Natural processes like fermentation are being improved, optimised by all kind of technologies, including new key biotechnologies. Nowadays fermentation has become a fully controlled and highly efficient and modern process; though it is still fermentation. The industrial biotechnological production chain starts with the raw materials. These are crops and/or organic by-products from agricultural sources and households that are first converted into sugars. During the production process ‘green’ raw materials (or biomass) are converted by tailor-made microorganisms, cell lines or isolated enzymes into the desired products: chemicals, biomaterials and bio-fuels. These are discussed in more detail in section 3.4.2. Enzymes are an important group of products of industrial biotechnology. A number of en-zymes are directly available in consumer products such as detergents, but a large number of enzymes are used as biocatalysts in downstream industries. This is also part of the industrial biotechnology chain (see figure 3.1) and is discussed in section 3.4.3. The use of biotechnology in production as discussed in sections 3.4.2 and 3.4.3 can lead to cleaner production processes (less use of chemicals and raw materials, less emissions of chemicals including CO2) and higher energy efficiency. Except for these process-integrated clean biotechnologies, also the so-called ‘end-of-pipe’ use of biotechnology – i. e. bioreme-diation - is addressed in this chapter. The treatment of air, effluent gases, soil and land, waste-water and industrial effluents, solid wastes and the use of biosensors for bioremedia-tion are presented in section 3.4.4. Biotechnology is often qualified as a sustainable alterna-tive for chemical processes. Although, the chemical industry has been very successful in de-veloping sustainable chemical solutions, the uptake of biotechnology has met some serious barriers; these are discussed in section 3.4.5.



Figure 3.1: The Industrial Biotechnology Production Chain

Source: Enzing and Kern (2004)72

3.4.2 Biotechnology for fuels, chemicals and materials 3.4.2.1 Biofuels Biodiesel and bioethanol are already on the market in a number of countries around the world, although in most cases tax credits are applied in order to achieve competitive market prices. Growth volumes of 9 to 10 % are expected the coming ten years due to the imple-mentation of EU regulations. Biodiesel – an equivalent to petroleum distillate - is derived from de-esterification and methy-lation of plant and animal oils and fats. New sources like algae are under investigation. Bio-ethanol is mostly made from sugar cane, corn and other starch crops. Biogas or methane is produced by the fermentation of organic matter including manure, wastewater sludge, munici-pal solid waste. Large research programs are running in order to develop high-yield low-cost bio-fuel crops, to improve the capacity of bacteria to transform sugars to ethanol, to enhance ethanol tolerant microorganisms to speed up the fermentation process of sugars into ethanol, to develop effi-cient and low-cost bioprocessing technology for ethanol recovery, and to degrade trace amounts of toxic organic compounds into harmless compounds. The advent of high through-put genome mapping and microarray analysis of gene/protein expression has provided scien-tific breakthroughs in the understanding in plant biotechnology, and of structure and function in plant systems for biofuels and chemicals production. A specific problem that has to be 72 Enzing, C.M. and S. Kern (2004): Industrial Biotechnology in the Netherlands. Economic Impact and Future Developments (in Dutch), Delft.

Production and preparation of biofeed stocks (vegetable oils,

sugars, etc)

Chemicals (enzymes,

vitamins, acids, antibiotics,

steroids etc)

Biofuels (bio-ethanol,

biodiesel)

Food and Feed

Textile and

Pulp and Paper

Others

Consumers

Specialised and supportive products and services (consultancy, instruments, software, recruitment, etc)

Biomaterial (biopolymers, etc)



tackled deals with the plant cellulose. By weight, plants consist of 70-80 % of cellulose (cell walls). It is very expensive (partly due to the cost of the enzymes used in this process) to make the plant cellulose - especially the lignocellulose and hemicellulose - available for fer-mentation processes. Biotechnological research is currently developing cheaper enzymes and new bacterial strains in order to make the cellulose sugars better available. Bioconversion technologies are under development for the production of liquid fuels such as ethanol from synthetic gas. Synthetic gas is produced through partial oxidation of carbon containing materials and contains CO, H2 and CO2. Traditionally, wood was the main source of synthetic gas, but agricultural, municipal, and paper waste are now being used. Some types of biomass are also specially grown for this purpose. The application of biotechnology in the fuels sector also includes the microbial desulphurisa-tion of fossil fuels. Sulphur has to be removed from fossil fuels because the combustion of sulphur molecules in coal and petroleum leads to the production of sulphur oxides, which have a very negative environmental impact. Through genetic and metabolic engineering, new microbial strains have been produced that have a desulphurisation activity 100 times higher than wild strains. Biotechnology is being used for recovering additional oils from in-ground crude oil-formations. This includes the use of biosurfactants: bacteria injected in the crude oil-formations secrete surfactants that solubilise oil that was not released in the initial pumping operation. Other applications of more traditional biotechnology are the decentral biogas powerplants and the application of bacteria for purifying oil and coal for more efficient power generation will not be addressed. 3.4.2.2 Chemicals Biotechnology is increasingly penetrating the chemical industry as some chemical processes are replaced by bioconversion processes (fermentation through microorganisms or cell lines) and as biocatalysts (enzymes) replace chemical catalysts. However, a large number of products of the chemical industry traditionally always have been produced through bioconver-sion processes, including enzymes, antibiotics, amino acids, vitamins and fine chemicals such as chiral building blocks for the pharmaceutical industry73. The microorganisms (e. g. bacteria, moulds, fungi, and yeasts) or cell lines from animal or human origin that perform the bioconversion processes in the chemical industry can be con-sidered as mini-production plants based on the metabolism of the microorganism or cell. Re-search is constantly focussed on improving these processes by up-grading the enzymes in-volved in the metabolic reactions. The genomes of most industrial organisms that are used for a wide range of biotechnological production processes in the chemical industry have already been sequenced. Genomics-based research strategies will lead to in-depth knowledge of the microbial activity of these organisms. On the basis of this knowledge, genetic, protein and metabolic pathway engineering tools are and will be used to optimise the industrial organisms in order to achieve more efficient, cheaper production processes with higher yields and to develop production processes for new enzymes and other products. New enzymes can also be found in microorganisms that live in difficult environments (extre-mophiles living in the deep sea, on geothermal vents, or on heavily polluted sites). As some of the features of the novel natural enzymes are undesired when removed from their natural habitats into the industrial context, engineers use the ‘directed evolution’ technique in combi-nation with the ‘DNA shuffling’ technique for modifying the properties of natural enzymes or proteins in order to create desirable properties. Examples of production processes in the chemical industry in which one or more chemical production steps have been replaced by bioconversion steps and biocatalysis are the produc-tion of Vitamin B2 (riboflavin) and of Cephalexin and Amoxicillin (both antibiotics). In the Vi-tamin B2 production process, the eight-step chemical process has been replaced by a one-

73 Gavrilescu, M. and Y. Chisti (2005): Biotechnology – a sustainable alternative for Chemical Industry. A research review paper. Biotechnology Advances 23, pp 471-499.



step fermentation process (moulds and yeast are used). The 10-step (bio)chemical synthesis process of Cephalexin has been replaced by a biotechnological process including fermenta-tion and enzymatic reactions. The new all-enzymatic production process of Amoxicillin has replaced a partly chemical synthesis process that created problems (colouring of product)74. A somewhat different application of bioconversion in the chemical sector is the use of bacteria in the mining process of medium and high-value chemicals, such as copper, zinc and cobalt. Bioleaching (with bacteria) and extraction from sulphide ores through bio-oxidation are being used in this sector. 3.4.2.3 Bio-based polymers A separate category of chemicals consists of biomaterials. This includes the production of bioplastics from starch (corn, potatoes). Biotechnology research is focussed on the develop-ment of new metabolic pathways in microorganisms that produce polymers or polymer building blocks with specific characteristics. Cahill and Scapolo (1999)75 expect that in 2010 10 to 20 % of the world production of chemical materials will be replaced by biomaterials. However, a more recent study states that the future markets share of bio-based polymers in Europe will stay relatively small: 1 to 4 % in 2020 (IPTS, 2004)76. Poly-lactic acids (PLA) have been synthesised for more then 150 years from biomass, but it had some major disadvantages: unstable under humid conditions. In 2002, a bioconversion process using a bacterium was developed that converted corn sugar into mono-lactic acid molecules. By heating these mono-lactic acid molecules, a bio-degradable PLA for use in a large variety of plastics, including polyesters, was developed. The polymers are used for clothing, packaging materials and electronic goods. Other developments in this field include the development of fibres on the basis of 1,3-pro-panediol, with properties better than polyesters and nylon. A pilot plant for the production of 1,3-propanediol will come into production in 2006. On the basis of a rational design strategy, eight genes (from yeast and from Klebsiella sp) were inserted into E.coli bacteria; additionally eighteen chromosomal genes were altered. In the final process glucose was converted through a number of steps into 1,3-propanediol. Each step produces intermediate products (1,2-propanediol, DHAP, DHA, glycerol, reuterin) that can also be used as intermediates for other products77. 3.4.3 Use of biocatalysts in down stream sectors The enzymes produced by the chemical industry are used in consumer products and in in-dustrial production processes in a number of down stream industries. The most important are food and feed, textile and leather, and pulp and paper. Enzymes are also used in the produc-tion of intermediates for the pharmaceutical industry (see above) and on a much smaller scale in a number of other sectors, including the degreasing of galvanised metal78. 3.4.3.1 Food and feed industry Biotechnology is an important tool in the food industry and one of the most important are en-zymes. Enzymes in food processing are used for enhancing processing characteristics (such as higher yields, more specific conversions, shorter production cycles), enhancing product characteristics (flavour and colour) and enhancing product qualities (better digestibility). The most commonly used enzymes in food production are amylases (hydrolyses of starch), prote-

74 OECD (2001): The Application of Biotechnology to Industrial Sustainability, Paris. 75 Cahill, E., Scapolo, F. (1999) Technology Map, Futures Report Series 11, EUR-19031-EN, Dec 1999. Published online at: http://futures.jrc.es/menupage-b.htm 76 IPTS (2005): Techno-economic feasibility of large-scale production of bio-based polymers in Europe, EU-JRC-IPTS, Seville. 77 Sasson, A. (2005): Industrial and Environmental Biotechnology. Achievements, Prospects and Perceptions, UNU-IAS Report. 78 For an overview see: IPTS (1998): Biocatalysis: state of the art in Europe, EU-JRC-IPTS Seville.



ases (processing of cheese and meat), pectinases (clarification of juices), lipases (modifica-tion of fats) and glucose isomerase (production of fructose). The latter represents the most important in terms of volume and is used in the production of High Fructose Corn Syrup HFCS. Then there is also the use of enzymes for cheese making. Chymosin, or rennet, is the milk clotting enzyme used to make cheese. It was traditionally extracted from calf stomachs, but the gene for the enzyme was cloned in microbes so that it could be produced by fermen-tation. The main food industries that use enzymes are the bakery sector, dairy industry, beer, wine and soft drinks industry, olive and edible oils industry, and the meat and fish sector. Enzymes are increasingly used to improve animal feed. They are used for the enhancement of general nutrient availability, for the degradation of non-starch polysaccharides (found in cereals and vegetable proteins), for the increased availability of dietary energy in feed and for the improvement of nutrient availability of cell-wall carbohydrates. 3.4.3.2 Textile and leather Biotechnology, in this case the use of enzymes, is becoming increasingly important in the tex-tile and leather industry. Enzymes are used in the pre-treatment of textiles (including desizing, degreasing, scouring and bleaching) and finishing (including bio-stoning of denim, bio-polishing, fibre modification such as anti-felting, depilling, improved dye uptake and sof-tening). Such as pectinases and hemicellulases for removing pectins and hemicelluloses associated with flax; pectinases, hemicellulases, proteases and lipases for cleaning raw cotton; oxidoreductases and peroxidases for bleaching fibres; catalases for removing residual hydrogen peroxide associated with the fibre bleaching process, etc. The leather industry uses enzymes for soaking, bating (improve pliability), degreasing and enzyme-assisted dehairing of skins. However, biotechnology also offers the opportunity to produce fibres with improved or novel features such as new breeds of genetically modified cotton that contains a bacterial gene that makes a poly-ester like substance. Other applications are the microbially based fermentation process for the production of fibres which is discussed in the biopolymer section 3.4.3.3 Pulp and paper Traditionally in this sector biotechnologies were mainly applied in the waste treatment pro-cess. Nowadays cleaner production is achieved also by process-integrated water treatment using biologically treated process water. Application of enzymes in the pulp and paper indus-try include biopulping, de-inking, biobleaching, reduction of fibre coarseness, improving the drainage rate of water out of the pulp material, increasing paper density and smoothness, and improving the appearance of paper products. The application of enzymes contributes to better availability of wood raw material, savings in the consumption of white carbon, surface active chemicals and chlorine; and decreases in chemical costs, cleaning frequency and the number of stops. For the latter the microbial reduction of pitch (the extractives that cause negative effects in the paper making process) is carried out by microbes or by an enzymatic method (using lipases) on refined fibres before papermaking. The lipase treatment also allows for savings in the consumption of white carbon, surface active chemicals. 3.4.4 Bioremediation Bioremediation consists of processes that use microorganisms or their enzymes to clean waste streams of industrial processes or contaminated sites from specific contaminants. Generally, bioremediation technologies can be classified as in situ or ex situ. In situ bioreme-diation involves treating the contaminated material at the site while ex situ involves the re-moval of the contaminated material to be treated elsewhere. A specific application of bio-technology are biosensors for in situ monitoring of bioremediation processes. The role of



modern biotechnologies in bioremediation is rather small and can be found mainly in biosen-sors79. 3.3.4.1 Water Bioremediation technologies have to compete, on the basis of cost-benefit analysis, with other technologies. They have proven to be the most attractive techniques for treatment of waste water containing the more common organic pollutants, and for domestic and industrial waste water. Biological treatment techniques include both anaerobic and aerobic treatment with mi-croorganisms, depending on the type of water to be processed. Effective and controlled bio-removal of nitrate and phosphate contamination from wastewater has become possible. 3.4.4.2 Air and effluent gases Bioremediation technologies that remove contaminants from air and effluent gases (odorous gases, toxic pollutants) primarily consist of biofiltration, followed by bioscrubbing and biotrick-ling filtration. These techniques are widely applied, but compete with other technologies, as they are not able to treat all types and concentrations of pollutants. 3.4.4.3 Soil The use of biotechnologies in soil treatment is still rather limited. Land farming is the use of bacteria to clean in situ contaminated sites, with minimal disruption and the degradation of pollutants to harmless substances. Old industrial sites (former refineries, and gas works, petrol filing stations) can especially benefit from biotechnological treatment methods. 3.4.4.4 Solid waste Bioremediation is used in organic waste management in a large number of countries, but it is largely limited to wastes with a high proportion of organic materials. Other applications could be the detoxification of solid waste and digestion of waste with organic content (oil, solvents). 3.4.4.5 Biosensors Biosensors are used for continuous and in situ applications in bioremediation processes, but also in groundwater surveillance. They monitoring contaminated organic media or process streams that contain mixed organic wastes. They measure the interaction of pollutants with biological systems through a biomolecular recognition capability attached to a signal trans-ducer. The sensing element can be enzymes, antibodies (as in immunosensors), DNA, or (genetically modified) microorganisms. 3.4.5 Barriers for the application of biotechnology in industrial manufacturing, ener-

gy and environment Cost of raw materials One of the main barriers in the first stages of the industrial biotechnology business chain is the price of raw materials. Sugars are currently the preferable renewable raw material for in-dustrial bioprocesses, but sugar prices are relatively high, especially in Europe - where they are kept high to protect European farmers from foreign competition. The competitive edge may come from tax credits, but also from government support for R&D-programmes that lead to new bioprocesses that use cheap feedstock, such as agricultural waste streams. The cost of producing biofuels has to compete with the lower cost of fossil fuels. The oil price has increased considerably in the last two years, which brings the break even point for bio- 79 BIO (2004): New Biotech Tools for a Cleaner Environment. Industrial Biotechnology for pollution prevention, Resource Conservation and Cost Reduction, Biotechnology Industry Organization, USA, OECD (1998) Biotechnology for Clean Industrial Products and Processes, Paris and OECD (1994) Biotechnology for a Clean Environment. Prevention, Detection, Remediation, Paris.



fuels closer to the cost of fossil fuels. Because of the important contribution of biofuels in de-creasing CO2 emissions, some governments have recently implemented tax credits and regulations to stimulate the use of biofuels. Economic and environmental competitiveness of industrial bioprocesses The barriers that prevent an increased uptake of bioconversion processes in industrial pro-cesses have been analysed thoroughly. This has been done from an economic perspective, through comparative cost-benefit analysis of bioconversion technologies with competing chemical and physical technologies. Research has also included the public good benefits of bioconversion from its environmental advantages. These are due to lower operating tem-peratures and pressures and biocatalysts are biodegradable, whereas inorganic catalysts are not. One of the main conclusions of these studies was that without external pressure, en-vironmental improvements alone are unlikely to lead companies to change their production processes. Governmental legislation, for instance by offering financial incentives for improved sustainability, as is already the case in the use of biofuels, can be a main driver for this change80. Consumers’ acceptance of GM produced enzymes Consumers’ acceptance hardly plays a role in this sector as most products are inputs for pro-duction processes, i. e. they operate in a business-to-business market. An exception is the use of enzymes in food production that are produced by genetically modified organisms. In the past, in some countries consumers and consumers organisations have expressed their resistance to this use of specific enzymes, for instance in cheese making. Also, the produc-tion of food additives such as vitamins by GM organisms is a controversial topic for some consumers. A Commission supported survey of public attitudes in Europe showed that GM enzymes for the production of environmentally friendly soaps and detergents is seen as use-ful and is supported by a majority of Europeans (France is an exception)81. Bioremediation As bioremediation takes place at the low-value, end-of-pipe part of the industrial production chain, fewer resources are available to develop bacteria or enzymes that have higher cleaning capacity or that can treat more substances then now is possible, for instance heavy metals or toxic substances. Government policies (regulations, taxes, R&D programmes) can provide incentives to increase the development and use of end-of-pipe biotechnologies. Sunk investments and sunk cultures A general barrier for all sectors covered in this chapter is that high investments have to be made in new bio-based production facilities. Notwithstanding the promises of biotechnology as a cleaner technology, the sunk investments in existing processes are a serious barrier for the introduction of bio-based processes. Introduction of biotechnology implies considerable investments in the building of new or pilot plants and equipment for treating waste water, soil or air. Optimisation of existing processes seems to be the main cost-saving strategy. Another related barrier deals with the cultural differences between chemical and petrol based disciplines and biological disciplines. As most companies that use or can use bioconversion technologies are traditionally chemical companies or chemical-mechanical engineering com-panies (in the case of remediation), it is difficult to persuade chemical engineers of the ad-vantages of bioconversion techniques. So, not only sunk investments but also sunk ex-periences and cultures work against the adoption of bio-based principles in industrial produc-

80 Ast, van J. et al (2004) Industrial Biotechnology Sustainable Tested. An investigation into the contribution of industrial applications of biotechnology to sustainable development (in Dutch), in order of the Dutch Ministry of Housing, Spatial Planning and the Environment, Den Haag. 81 Gaskell et al. (2003): Europeans and Biotechnology in 2002. Eurobarometer 58.0, A report to the EC Directorate General for Research from the project 'Life Sciences in European Society', Brussels.



tion processes82. This is one of the reasons why there is a lack of awareness within chemical companies of the pro’s and con’s of introducing biobased productions processes.

4. Concept for elaborating indicators 4.1 General approach The key objective of Task 1 is to identify appropriate indicators for assessing the conse-quences of biotechnology applications in Europe. This requires a conceptual framework for differentiating biotechnology into several stages that can be measured through indicators. Figure 4.1 summarises these stages and identifies three main types of indicators.

Figure 4.1: Conceptual framework for biotechnology indicators

Policy goals

Biotechnology

Application fields

Products

Services

Processes

input

output

impact

The focus of the analysis is on identifying indicators that can capture the development, diffu-sion and impacts of biotechnology in specific application fields. Therefore, indicators are classified into three main categories (figure 4.1). Input indicators describe capabilities and capacities in researching and developing biotech-nologies. They include the necessary knowledge to develop biotechnology applications and to apply them in various economic sectors. Output indicators evaluate the extent of adoption and use of biotechnology products, services and processes within each application field. Impact indicators assess the economic, social and environmental impacts of modern biotech-nology applications. Biotechnology inputs such as R&D can also directly affect policy goals, as indicated by the arrow on the right hand of figure 4.1, independently from its adoption by various industry sectors.

82 Enzing, C.M., B.F. Filius and R. van der Meijden (1993): Mid-term Evaluation of the Innovation Research Programme on Catalysis Research (in Dutch), TNO-STB, Apeldoorn.



The general approach to developing suitable indicators according to this framework com-prises three steps: first, to describe the phenomena to be measured by each indicator cate-gory; second, to propose suitable indicators for these phenomena; and third, to assess data availability and quality for the proposed indicators. This three-step-procedure is followed in chapters 5 to 9. 4.2 Generic, application-generic, and application-specific indicators In addition to the typology of input, output and impact indicators noted in figure 4.1, there are also generic, application-generic, and application-specific indicators. Generic indicators use comparable numerators and denominators common to all applications. An example is total R&D spending on biotechnology, or the share of biotechnology patents out of all patents. Many generic indicators can also be created for specific applications (appli-cation-generic), such as total R&D spending in health biotechnology or biotechnology patents for industrial applications. Application-specific indicators are only available for a specific appli-cation and have no equivalent for other applications. An example is the share of crop hec-tares planted to GM varieties. Generic indicators are essential for assessing the main economic consequences of biotech-nology. However, the focus on the consequences of biotechnology means that it is essential to be able to construct application-generic and application-specific input, output and impact indicators. Application-specific indicators are required because many of the expected eco-nomic and social benefits of biotechnology are due to conditions that cannot be generalized across sectors. The use of GM crops, for example, should reduce employment in the agro-food chain whereas biotechnology applications in therapeutics could increase employment in the pharmaceutical sector. Many application-specific indicators are also of relevance to the social and environmental consequences of biotechnology. Table 4.1 gives examples of the types of biotechnology indicators used in this report. Of note, three of the nine cells are largely empty, with very few relevant indicators.

Table 4.1: Typology of biotechnology indicators with examples

Generic Application-generic Application-specific

Inputs Total biotechnology R&D/ total R&D

Total biotechnology health R&D / total bio-technology R&D

--1

Outputs --2 --2 Hectares planted with GM maize / total hec-tares planted with maize

Impacts Total value-added of biotechnology goods and services / total GDP

Total value-added of health biotechnology goods and services / health sector value-added

Number of DALYs gained per year from bio-therapeutics per capita

1: There are few, if any inputs that are only application specific (financial inputs, researcher FTEs, publications, patents, etc).

2: Largely empty cells, as most outputs are application-specific. Two examples of generic output indicators are the number of biotechnology products on the market, which has very poor comparability across application fields, and the number of firms using at least one biotechnology in production.



4.3 Data evaluation Four main criteria are used to evaluate data availability and comparability across countries or application fields: • Data coverage (share of countries and/or application fields for which data are available at

a reasonable cost and effort) • Data source • Definition (how biotechnology is defined) • Timeliness (latest available year, period covered)

Data availability can be determined after the data gathering exercise by evaluating data avail-ability by country or application field. An assessment of comparability requires information on the data source, definition, and timeliness. For all indicators, metadata83 on these criteria need to be collected for each country. As an example, the metadata on business R&D expenditures would need to include, for each country, the source of the data, the definition of both biotechnology and a biotechnology firm, and the reference date for the indicator. All three characteristics can affect the comparability of data across countries. Of note, full comparability across countries is an unrealistic goal at this time. In most cases, data for the denominator for indicators will come from highly reliable, official sources, such as population data (per capita, working population, number of researchers). In a few cases data for the denominator will be obtained from a survey and will require meta-data. An example is the indicator biotech revenues/total revenues. The data on total revenues of biotechnology firms will also come from a survey. Data source: The most reliable indicators are obtained from complete population data, such as for patents, field trials, or the number of bio-pharmaceuticals with marketing approval; followed by data from official national surveys based on samples (such as R&D data), data from public research groups and consortia84, and lastly from data from surveys run by con-sulting firms85 (number of core biotechnology firms or biotechnology venture capital). Wher-ever possible, official national data and data from public research groups are preferred over data from consulting firms. However, some types of indicators are only available from con-sulting firms or from a range of eclectic sources. Definition of biotechnology: The OECD list-based definition of biotechnology (see chap-ter 2) has been adopted by several countries and will therefore improve comparability for these countries. Other definitions are also in use. For example, Japan includes traditional food fermentation in biotechnology, although the OECD list-based definition excludes this form of traditional biotechnology. An issue that is particularly relevant to biotechnology statistics is differences in the definition of the population of ‘biotechnology’ firms, excluding public research organisations (PROs)86. Three main definitions are in use: 1) all firms with some biotechnology activities, 2) dedicated

83 Metadata refers to all information on the data source, such as survey quality, plus other relevant information, such as differences in the wording of a survey question. 84 See for instance, Reiss et al. (2005), Enzing et al. (2005). 85 The main disadvantage of data from consulting firms is that the data provider rarely gives details on the size of surveys and response rates and the data sources. This makes it impossible to assess the quality of the data. In addition, consulting firms are usually in the business of promoting the biotechnology sector. This could be one reason why consulting firm estimates for employment or revenues are often higher than estimates obtained from official surveys. 86 A PRO is an organisation performing research of which the main source of funds comes from other public organisations, and which is in public ownership or control. Research organisations of officially recognised charities or foundations, which raise the majority of their funds from the general public, are also considered as PROs. (Definition from EU funded BioPolis project).



or ‘core’ biotechnology firms where biotechnology is central to the firm’s activities or business strategies, and 3) small core or dedicated biotechnology firms with less than 250 or 500 em-ployees (the size cut-off varies by country). The second and third definitions are more widely used than the first definition because of diffi-culties in identifying the biotechnology activities of large firms. Studies based on the third definition of dedicated biotechnology firms usually assume that all employment or R&D is ‘biotechnology related’. This is already a heroic assumption for small firms, but it is completely untenable for large multinational firms for which biotechnology could be only a small part of their total R&D, employment, or sales. For this reason, it is crucially important that metadata on the definition of biotechnology firms be collected with all data on biotechnology inputs (employment, R&D, revenues etc). Furthermore, input indicators for the business sector can only be compared across countries when based on the same definition of biotechnology firms. Reference date: Biotechnology is a rapidly changing field. Therefore, the latest available reference date for each indicator is very important and will influence comparability across countries. For example, biotechnology R&D data for 2000 in country x is unlikely to be com-parable with biotechnology R&D data for 2003 for country y. In order to evaluate trends, in-formation should also be collected on the number of years for which comparable data are available. Time series: Time series data are very useful for many purposes, such as determining the rate of adoption of biotechnology, extrapolating trends into the future, or evaluating changes over time in research fields (for instance using patent data). Consequently, information on time series data should be gathered. This includes the first year of data availability and infor-mation on breaks in time series, such as a change in the definition of an indicator. Unfortu-nately, many useful indicators, such as R&D investment, are only available for a few years, with frequent changes to the indicator definition. 4.4 Indicator construction Constructing indicators first requires collecting statistics on biotechnology. A statistic is a simple data point, such as the number of biotechnology firms, or the total amount of public sector expenditures on biotechnology R&D. An indicator places a statistic in context, such as the share of all public R&D expenditures spent on biotechnology. Each statistic can be used to construct a number of indicators by varying the denominator. For example, a statistic on the number of biotechnology patents can be used to construct indicators for biotechnology patents per capita, per 1,000 researchers, or per 1,000 employees. Traditional science and technology indicators are based on national data. For example, data on business expenditures on R&D are obtained from national surveys of firms and patent data are based on an analysis of patent records by the nationality of the applicant or inventor. Similarly, national data sources are required for many biotechnology input indicators, such as for R&D, patenting, or revenues from the sales of biotechnology products. In contrast, many biotechnology indicators for outputs and impacts can be constructed from a mix of national and non-national data sources (Arundel 200287). The latter can include one-off surveys or the results of scientific studies in a single country. As an example, many large molecule bio-pharmaceuticals have been approved for the treat-ment of orphan diseases, with evidence on efficacy or improvements in disability adjusted life years (DALY) based on a limited number of epidemiological studies in a few countries. This data can be combined with national estimates of the affected population to estimate the health benefits in terms of DALYs at the national level. It is also possible to estimate health benefits without national data on the size of the affected population by using data on disease prevalence rates in countries with a similar genetic population.

87 Arundel, A. (2002): Agro-biotechnology, innovation and employment. Science and Public Policy 29: 297-306.



A second example is to combine field study data on the effect of different types of GM crops on pesticide use, yields and farm income88 with national level data on the number of hectares under cultivation with non-GM varieties of the same crop. In the absence of national data, the benefits (or costs) from switching to GM varieties can be estimated by assuming similar changes in pesticide use and yields as observed in other countries after non-GM varieties were replaced with GM varieties. 4.4.1 Composite indicators There are three main types of composite indicators. First, several composite indicators are constructed from aggregating data measured in the same units. National GDP is an excellent example. Given data availability, it might be possible to construct a composite indicator for total value-added from biotechnology by adding estimates of biotechnology value-added in each application area. This type of composite indicator is discussed in chapter 9. The second type of composite indicator evaluates data measured in different units. The classic example is cost-benefit analysis, where the costs are measured in economic units but the benefits are measured in other units, such as DALYs (in health applications) or a reduc-tion in tonnes of greenhouse gases (in environmental applications). Similar indicators are in-cluded in chapter 6 on health applications of biotechnology and in chapter 7 on agricultural applications. The third type of composite indicator produces an index that summarizes a range of data measured in different units. These are widely used in comparing national performance across countries. For example, it would be possible to construct a ‘biotechnology impacts per-formance index’ for the EU, the United States, and for other countries. The construction of these types of indicators is beyond the remit of this report. 4.5 Input indicators To date, the majority of available biotechnology indicators cover inputs such as R&D invest-ments, employees active in biotechnology firms, scientific publications, and patents. Patents measure inventions, but as many of these will never be directly commercialized, they are closer to an input than to an output measure. Input indicators can be grouped into the following categories: R&D, industry-firm knowledge transfer (a secondary R&D measure), knowledge transfer from universities (a secondary R&D measure), employment, education, venture capital, firm counts, publications, and patents (see chapter 5). Many of these are only available as generic indicators that aggregate across application fields. The number of employees active in biotechnology research is one of the best available input indicators, but it is often not completely available due to difficulties in obtaining employee counts from large firms. A widely used alternative is firm count data, but this suffers from poor comparability across countries, due to potentially large differences in average firm size. 4.6 Output and impact indicators There are two classification issues: the boundary between input and output/impact indicators, and the boundary between output and impact indicators. A problem for defining the boundary between input and output indicators concerns biotech-nology adoption. Indicators of the adoption of biotechnology research methods or capabilities in developing biotechnology uses are defined as input indicators. Conversely, indicators for

88 Brookes and Barfoot (2005a, 2005b) have summarized the results of studies on the effect of GM crops on pesticide use, farm incomes, and yields for four crops (soybeans, maize, cotton and canola) in the United States, Canada, South America, Mexico India, China, and Australia.



the actual use of biotechnology to produce products or in production processes are defined as output indicators. The outputs and impacts of biotechnology are often closely linked and difficult to classify. Al-most all useful (see footnote 2 to table 4.1) output statistics and indicators are application specific rather than generic. They include, at the national level, indicators such as the number of bio-pharmaceuticals developed by national firms, the percentage of all agricultural land planted with GM crops, and the percentage of paper produced using biotechnology-based processes. In industrial biotechnology, this could include indicators for the number of bio-en-zymes produced and, because these enzymes are used in down-stream industries, the num-ber of products of these industries. Generic impact indicators are based on employment, value-added, or revenues that allow comparisons across application fields. They measure the economic impacts from the use of biotechnology. Examples include the retail sales value of bio-pharmaceuticals, GM seeds, industrial bio-enzymes, bio-detergents, or bioremediation services. Many impact indicators for the benefits and costs of biotechnology for the environment and for the quality-of-life are application specific. Many of these can be transformed into generic indi-cators with the same denominator, as when the environmental costs or benefits of a change in pesticide use, or the benefits of improved quality of life due to bio-pharmaceuticals, are assigned economic values. However, these types of indicators require intensive analysis and many assumptions that are often specific to particular countries and circumstances89, which defeats the purpose of developing internationally comparable indicators. The alternative is to develop first-level environmental and quality-of-life impact indicators that are sector specific. Examples include the effect of a GM versus non-GM crop on pesticide use (in toxic equivalents) per hectare, the share of national morbidity days treatable with bio-pharmaceuticals, the additional DALYs due to bio-pharmaceuticals (either for disease-specific treatment or for all bio-pharmaceuticals as a class), or the annual change in greenhouse gases from bio-fuels. Most output and impact indicators are not collected by national statistical offices90 and will need to be developed using an eclectic range of data sources.

5. Input statistics and indicators The main inputs to the development of biotechnology products and processes include R&D investments, research collaboration, skilled employees, capital investment in new biotechnol-ogy firms, including venture capital, and specialized knowledge, as measured by scientific publications, and patents. Patents also measure inventions, but as many of these will never be directly commercialized, they are closer to an input than to an output measure. Since most generic indicators are obtained from national surveys, it is a comparatively simple task to link them to a wide range of denominators. For this reason, this chapter focuses on the availability of biotechnology input statistics by country. For each statistic, it is possible to con-struct several indicators using denominator data from publicly available sources such as the OECD MSTI database or Eurostat’s NewCronos database. For example, statistics on business expenditures on biotechnology R&D can be turned into indicators for the biotechnol-ogy share of total business expenditures on R&D (using Eurostat data on total BERD), bio-technology R&D expenditures per capita (using population data), or the share of all biotech-nology R&D performed by businesses (using data on private sector R&D).

89 For instance, the estimated value of one additional DALY depends on expected income levels and retirement ages, which vary by country, whether or not health care costs are covered by the state or the individual, etc. 90 An exception is Canada, where the Government collects data on revenues from biotechnology products.



Almost all available biotechnology input data are limited to generic indicators, with only a few application-generic indicators available. The best coverage by country is for generic biotech-nology inputs in the private sector. An example is the total number of firms with biotechnology activities. There are far fewer input statistics and indicators by field of application and very few input indicators for public sector activities. For both generic and application-generic statistics, we evaluate the types of data available from four sources:

1. Business sector statistics from official surveys or reports. 2. Public sector statistics from official surveys or reports. 3. Database statistics (patents, publications, and citations). 4. Consulting firm statistics (Ernst and Young reports, venture capital associations, etc).

Comparability is a serious problem for many of the input indicators derived from firm surveys, such as for employment and R&D investment, due to differences in how biotechnology is de-fined and how a biotechnology firm is defined. For this reason, information on data sources for input indicators are given in this chapter in table 5.1 through table 5.3, along with a dis-cussion of the problems. (In contrast, the data sources for many application indicators identi-fied in chapters 6-9 are given in an annex.) 5.1 Generic input statistics In contrast to many application-specific indicators, generic indicators are largely available from a variety of national surveys, either from official sources or by consulting firms. To date, this information has not been assembled in one publication91, which makes it very difficult to determine sources, data availability and the latest year and time period. Due to the complexity of input data, detailed information for specific countries is given in tables 5.1 through 5.3. 5.1.1 Business sector statistics Table 5.1 gives data availability for generic input statistics on the business sector, derived from official surveys or reports for 15 countries: four non-EU countries and eleven EU coun-tries. The results are limited to statistics after 2001. Given the rapid rate of change in biotech-nology, statistics from before 2001 will not be comparable with more recent statistics. The most recent national data are for 2003, with some countries reporting results for 2004. The indicators presented in table 5.1 are given without denominations because most of them are obvious (such as total number of firms, total R&D expenditure, total employment) and pro-vided by official statistics. To the best of our knowledge, comparable national data are not available for fourteen EU countries: Austria, Czech Republic (some data should be available in 2006), Cyprus, Estonia, Greece, Hungary, Luxembourg, Latvia, Lithuania, Malta, Poland, Portugal, Slovakia, and Slo-venia. The statistics for four countries, the UK, Ireland, Netherlands, and Spain, are from con-sulting firms. Government surveys were conducted in 2004 in Ireland, Spain, and Poland, but the results were of very poor quality and therefore unusable92. There are few problems of comparability based on the definition of biotechnology. Eleven of the 14 countries use the OECD definition of biotechnology, three use another definition limited to modern biotechnology (OTM), and biotechnology is undefined in Spain and Sweden (UD), although in both cases the definition is likely to be limited to modern biotechnology. In most countries, biotechnology firms are also limited to those that perform biotechnology R&D and exclude equipment suppliers. An exception is Ireland, which includes both suppliers and firms that do not perform R&D. It is not clear if suppliers are included in the results for Spain. The German survey also provides separate data for equipment suppliers.

91 A forthcoming OECD publication will provide biotechnology indicators for the OECD member countries. 92 Data for Japan are not comparable while there are no relevant data for Singapore and South Korea as of February 20th, 2006



There is greater variation in the definition of a biotechnology firm. A major comparability problem is between studies that are limited to dedicated or ‘ core’ biotechnology firms (DBFs), which are almost always limited to SMEs, and studies that include all firms with biotechnology activities, both DBFs and large firms (DBF + L). Seven countries obtain data from R&D sur-veys that will capture all firms, of any size, that perform some R&D on biotechnology, while another seven countries use a range of data sources to identify both DBFs and large firms active in biotechnology. These results should be comparable. In contrast, the results for the UK, Finland (with the exception of the R&D statistics), and Sweden (which excludes large pharmaceutical firms) are mostly limited to DBFs. Comparisons of basic statistics on R&D and employment between these two groups will be unreliable, because the first group includes the activities of very large firms, while the latter will not. For example, table 5.1 shows that data on the number of R&D employees in biotech-nology for Canada and the United States includes both employment by both DBFs and large firms, while the R&D employee results for the UK are limited to DBFs. This makes cross-country comparisons between the UK and Canada and the United States unreliable, with the UK results underestimating R&D employment. In some countries, as in Spain, the R&D and employment of a few large firms is several times greater than the combined R&D and em-ployment of all DBFs, which highlights the size of potential comparability problems. The best data coverage is for the number of firms, the size distribution of firms, biotechnology R&D, biotechnology R&D employees, and total employment among biotechnology firms. When biotechnology employment data are missing (either R&D or biotechnology active em-ployees), firm count data by size can be used to estimate total employment in biotech firms. Firm count data alone is not very useful because of differences in the average firm size. For example, the average American biotechnology firm has 1,100 employees, versus 18 em-ployees in Denmark. The number of ‘biotechnology active’ employees, which includes research, production, mar-keting, and other employees with biotechnology-related responsibilities, provides a measure of the impacts of biotechnology on employment. However, in most countries with data on both biotechnology active and biotechnology R&D employment, a substantial share of all biotech-nology employees are involved in R&D. For this reason, the biotechnology employment data are better suited as an input indicator than as an output indicator, particularly because only a few countries collect data on biotechnology active employees. Five countries collect data on collaborative activities, particularly for R&D, but there is little consistency in the types of collaboration indicators across countries. There is very little data on the amount of capital raised, and slightly more on the contribution of venture capital (VC). Consequently, data on the ability of biotechnology firms, particularly DBFs, to raise capital must rely on venture capital associations. The main disadvantage is that this data does not give a measure of the importance of venture capital compared to other funding sources. The venture capital share of all capital raised was 13 % in Denmark, 22.5 % in Canada, and 44.5 % in the UK.



Table 5.1: Business sector input indicators from government surveys (consulting firms when no official data)

Definition of: Available indicators Biotech

R&D Employment Country Latest

year Biotech Biotech

firm10 #

Firms #

Firms by size

Total R&D

$ Empl. Total empl. Biotech active empl.

Collabora-tion

Capital raised

VC

Canada 2003 OECD11 DBFs + L Switzerland3 2003 OECD R&D United States 2002 OECD DBFs + L -6 - Iceland4 2004 OECD R&D Belgium 2003 OECD DBFs + L - - - Denmark 2003 OECD R&D - Finland2 2003 OECD DBFs + (L) - - - - France 2003 OECD R&D - - - - - - - Germany7 2004 OECD DBFs - Italy 2003 OECD R&D Ireland1,5 2003 OTM12 DBFs + L Netherlands8 2005 OTM DBFs (+L)9 Spain1 2003 UD13 DBFs + L - Sweden 2003 UD DBFs + (L) - - - UK1 2003 OTM DBFs - - - 1: Survey or data collection by a consulting firm. 2: Large Finnish firms are included for R&D data only, otherwise results limited to 181 DBFs with less than 250 employees. 3: Results for Switzerland are expected in Spring 2006, so it is not clear what data will be provided, other than total biotech R&D. 4: Results for Iceland should be available in spring 2006. 5: One-third of the Irish firms are not active in R&D, but as the report combines data for Northern Ireland and the Republic of Ireland, we don’t know if this ratio holds for the

Republic. Some results, such as firm counts, can be identified for the Republic of Ireland only. 6. Ernst and Young provides estimates for the United States. 7. Data for large firms active in biotechnology are provided separately. 8. Data were collected under the responsibility of BioPartner, an organisation set up by the Dutch Ministry of Economic Affairs, with a mission to set up 75 new DBFs in 5 years

(2000-2004). The data were collected by the staff of BioPartner Network. 9. Data about large firms active in biotech (diversified firms) have been collected for 2002 only. 10: DBF = dedicated biotech (or ‘core’) firm, L = large firm, R&D = firm that performs biotech R&D, as identified in an R&D survey. All indicators in the row refer to the definition

of the biotech firm. For example, ‘total employees’ for Canada refers to the total employees among biotech firms, defined as DBFs plus large firms active in biotechnology. For the UK, total employees only refers to employment in DBFs. For further details, see section 5.1.1.

11. OECD: OECD definition of biotechnology 12 OTM: Other definition of modern biotechnology 13. UD: Biotechnology undefined



Table 5.1 data sources: Canada Canadian Trends in Biotechnology, biotech.gc.ca, 2005 France Unpublished preliminary data Switzerland Van Beuzekom, B. Biotechnology in OECD Member Countries: An

inventory. STI Working Papers 2004/8, OECD, Paris. Germany DE Statistics: Unternehmen der Biotechnologie in

Deutschland, 2005 United States

Survey of the use of biotechnology in US Industry, Dept of Commerce, Nov 2003

Italy Van Beuzekom, B. Biotechnology in OECD Member Countries: An inventory. STI Working Papers 2004/8, OECD, Paris.

Iceland Van Beuzekom, B. Biotechnology in OECD Member Countries: An inventory. STI Working Papers 2004/8, OECD, Paris.

Ireland InterTradeIreland. Mapping the Bio-Island, Newry, 2005.

Belgium The biotech industry in Belgium: National Report to the OECD TIP case study on biotechnology, April 2005

Nether-lands

The Netherlands Life Sciences Sector reports 2001, 2002, 2003, 2004, 2005 Enzing, C.M. and S. Kern (2004) Industrial Biotechnology in the Netherlands. Economic Impact and Future De-velopments (in Dutch), in order of the Dutch Ministry of economic Affairs, TNO-report, STB-04-36, Delft., Enzing C.M., A.M. van der Giessen en S.J. Kern (2002) Life Sciences in Nederland: Economische betekenis, tech-nologische trends en Scenario’s voor de Toekomst, TNO, Delft.

Denmark Biotechnology in Denmark: A preliminary report, Carter Bloch, Danish Centre for Studies in Research and Researcy Policy, Working Paper 2004/1, April 2004. Uses results of R&D survey and various sources on biotech firms.

Spain Genoma Espana, Spanish biotechnology: Economic im-pacts, trends and perspectives, June 2005.

Finland Hermans R, Kulvik M, Tahvanainen A-J. ETLA 2004 survey on the Finnish Biotechnology Industry. ETLA Discussion Paper 978, April 22, 2005 (ETLA); Biotechnology, Ch 13 in Science and Technol-ogy in Finland 2004, Statistics Finland March 2005 (SF).

Sweden VINNOVA, Nationella och regional klusterprofiler, 2004; Unpublished preliminary data

UK DTI, Comparative statistics for the UK, European and US biotechnology sectors, analysis year 2003. February 2005.



5.1.2 Public sector statistics Table 5.2 summarizes the available data on public sector inputs for biotechnology. Data are only available for seven countries, including five EU Member States. Six of the seven countries collect ex-penditure data on biotechnology R&D in the public sector. This can be combined with public data on total public sector R&D to produce an indicator on the share of all public sector R&D spent on biotech-nology. This equals 12.6 % in Canada and 6.7 % in Finland. Only Spain and Denmark provide count data on the number of researchers in the public sector that are working in biotechnology. As an addi-tional input measure the number of PhD graduates in biotechnology could be used. However, such data is only available for life sciences in general and not for biotechnology. The life sciences data are provided by the OECD Education database and covers most EU countries as well as the USA and Japan. Table 5.2: Public sector input indicators

Biotech R&D

Public sector spin-offs

Biotech researchers

Subsidies of private sector biotech R&D (US$)

Canada United States Denmark Finland UK Spain Sweden1 ( )

1: Higher education sector only. Table 5.2 data sources:

Canada Canadian Trends in Biotechnology, biotech.gc.ca, 2005 Denmark Biotechnology in Denmark: A preliminary report, Carter Bloch, Danish Centre for

Studies in Research and Researcy Policy, Working Paper 2004/1, April 2004 Finland Science and Technology in Finland, 2004, p 280-281 UK Personal communication from Steve Churchill Spain Genoma Espana, Spanish biotechnology: Economic impacts, trends and perspec-

tives, June 2005 Sweden Unpublished data Canada and Finland also provide data on the number of biotechnology firms that were created as spin-offs from public universities or research institutes. This is a very good indicator for measuring the commercialisation of public research – one of the main goals of the Lisbon Council. A second measure of the contribution of public support for the commercialisation process is the amount of business R&D expenditures financed by the public sector (subsidies of private sector R&D). We have only found relevant data for this for Spain and the United States. Table 5.2 shows that coverage of public sector biotechnology activities is generally very poor. This is basically due to the fact that existing statistical systems are not designed to capture public sector in-vestments in biotechnology, which would require a specialised survey. Budget allocations to public sector R&D93 are divided into different fields of science categories using 13 NABS94 categories, but these do not contain enough detail to separate out biotechnology R&D within each of many different categories that contribute to biotechnology.

93 Part of government budget appropriations or outlays for R&D (GBOARD), which can include both funding of private sector and public sector research. 94 Nomenclature for the analysis and comparison of scientific programmes and budgets.



In 2006 the results of the BioPolis project will be published95. The report will include public expendi-tures on biotechnology research, technology transfer and commercialisation for all 25 EU Member States, four candidate countries and Iceland, Norway and Switzerland, through government programs (dedicated biotechnology programmes and general programmes that also address biotechnology). Expenditures will be broken down by main sub-areas of biotechnology (including plant biotechnology, animal biotechnology, health-related biotechnology and industrial biotechnology). Data will be pro-vided for the period 2002-2005. 5.1.3 Database statistics Public and private databases provide statistics on biotechnology patents and bibliometrics (publica-tions and citations to publications). Patent data are available free of charge from the EPO, the USPTO, and other jurisdictions. Bibliometric data are available, usually at a fee, from private firms, such as the ISI database, managed by Thomson. The OECD publishes data on biotechnology patents in its Main Science and Technology Indicators (MSTI) series. The most recent data includes biotechnology patent applications at the EPO for 2001 and patent grants at the USPTO for 1999. Both are becoming increasingly out-of-date, although they should be updated in the Spring of 2006. The biotechnology patent counts are based on a validated list of patent classes that consist of a substantial proportion of biotechnology patents. Nevertheless, the classification system includes an unknown level of error, from biotechnology patents assigned to non-biotechnology classes and from non-biotechnology patents within the biotechnology classes. To the best of our knowledge, there are no regularly produced, comparable international bibliometric statistics. This work is usually done by academics or consultants on a one-off basis96. The most common national indicators are the absolute number of biotechnology publications, the share of global publications, and the mean citation rate. Patents are more relevant than bibliometrics for research on the social and economic effects of bio-technology because they measure activities that are closer to the market than publications. Biblio-metric data, on the other hand, provide good information on scientific activities in biotechnology or re-lated to biotechnology, thereby measuring an important facet of biotechnology capacities at the re-search end. 5.1.4 Consulting firm statistics As shown in table 5.1, the only available national statistics for several EU countries are from con-sulting firms. In some cases, these reports are funded by government offices, such as the report for the UK. Ernst and Young (E&Y) provides near global coverage of biotechnology inputs, although not all results are broken down to individual countries, but are combined into regions (Europe, Asia-Pacific)97. The main E&Y indicators cover the activities of "biotechnology firms" and include: revenues (plus net profit or loss by region), R&D expenses, number of employees, number of publicly-traded firms, number of privately-owned firms, cash flow, and equity financing. There are two main disadvantages with using private consulting reports to measure biotechnology inputs: little or no information is given on sampling methods or how firms are identified, and the defini-tion of a ‘biotechnology firm’ is unclear. In E&Y reports, a biotech firm is usually defined as a DBF with less than 500 employees. But, the biotechnology activity itself is undefined, which means that the firms could be included because they claim to be active in biotechnology.

95 Enzing, C.M. et al. (2006): Inventory and analysis of national public policies that stimulate research in life sciences and biotechnology, its exploitation and commercialisation by industry in Europe in the period 2002-2005. 96 Recent examples include Campbell et al., Scan of Canadian Strengths in Biotechnology, Science-Matrix, Montreal, 2005, Genoma Espana for Spain; Reiss et al. Performance of European Member States in biotechnology, Science and Public Policy 31, 344-358, 2004; and the ongoing EU-BIOPOLIS project. This project aims at gathering public sector-specific biotechnology R&D expenditure data for all Member States. We are in the process of determining if the NSF of the United States obtains comparable bibliometric statistics for biotechnology. 97 Ernst and Young (2005): Gaining Momentum.



As an illustration of the problems, the number of identified biotechnology firms in E&Y publications can differ substantially from official data. For instance, E&Y reports 278 biotechnology firms in France, versus official statistics that identify 755 firms with R&D activities in biotechnology98. These differences could have large impacts on the estimated amount of private sector biotechnology R&D, since the E&Y reports do not capture R&D spending in biotechnology by large firms or firms that do not report that biotechnology is their core business. For these reasons, E&Y reports (plus other consulting re-ports using a similar methodology) provide a poor estimate of total biotechnology inputs and should only be used when official survey data are unavailable99. Venture capital associations such as EVCA in Europe provide data on biotechnology venture capital, but this is mixed with venture capital in health fields, so it is more accurately ‘health/biotechnology’ venture capital. The OECD’s 2005 Science, Technology and Industry Scoreboard gives results for many target countries for 2003. 5.2 Generic application statistics for inputs Generic input statistics by application field are available from official data sources for the private and public sectors. Some statistics are also available from private consulting firms (not discussed here). To date, there are very few application-generic statistics for patents and bibliometrics because of the diffi-culties in determining the application for biotechnologies that are relevant to many different fields100, but it is possible to tailor patent and bibliometric analyses specifically to different applications. Such work has been done or is underway by academic groups within specific research projects101. 5.2.1 Business sector statistics Table 5.3 replicates table 5.1 for generic input indicators that are available by application field102. Table 5.3: Availability of business sector input indicators by application field.

# of Firms

Total R&D expenditures

Biotech R&D expenditures

Biotech R&D

employees

Total employees

Biotech active

employees

Capital raised

Canada Switzerland United States Belgium1 ( ) ( ) Denmark Finland France2 ( ) ( ) Italy Ireland Netherlands Spain Sweden UK

Notes: see table 5.1 for definitions and sources. 1: Application fields in Belgium for R&D employees and biotech active employees limited to pharmaceutical/ non-

pharmaceutical. 2: Application fields in France limited to NACE sectors.

98 Other differences are as follows, with the E&Y estimate given first: Sweden (178 versus 154), Denmark (80 versus 267), Finland (69 versus 123), Belgium (70 versus 73). 99 This is not a criticism of E&Y reports, which are written to meet the needs of investors in biotechnology firms. These investors are largely interested in small DBFs and not in large firms with some biotechnology activities. 100 An exception is by King J and Schimmelpfennig D, Mergers, acquisitions and stocks of agricultural biotechnology intellectual property, AgBioForum 8:63-88, 2005. They give the total number of USTPO agricultural patents between 1976-2000. 101 e. g. Reiss et al. (2004): Performance of European Member States in biotechnology, Science and Public Policy 31, 344-358, 2004; and the ongoing EU-BIOPOLIS project. 102 For the latest year, definitions, and source, see table 5.1.



Unless marked otherwise, all application data in table 5.3 are available for health, agro-food, and in-dustrial fields. International comparability is generally good, although there are small variations in how each application is defined, particularly in agro-food. Some countries include silviculture and acqua-culture in this field, while others do not. The main problem is that the number of internationally comparable input indicators by application field is limited, both by the number of countries for which such data are available and the number of indi-cators that can be disaggregated by application. Two key input variables are employees and R&D, but these are not consistent across countries. In particular there are three different measures of R&D: total R&D in biotech firms, biotech R&D, and biotechnology R&D employment. None of these three indica-tors are consistently available. The most consistent option is biotechnology R&D expenditures by application field, which is available for four countries. 5.2.2 Public sector generic input statistics by application field Only Canada and Spain provide R&D expenditures in biotechnology in the public sector by field of application. No other disaggregated indicators are available for inputs. The BioPolis report to be published in early 2007, will also provide figures on public R&D spending through governments pro-grams on the sectoral level (plant, animal, food, health, industrial, environmental). 5.3 Recommendations for future data collection The availability of input indicators is almost inversely proportional to the value of the indicator for assessing inputs into biotechnology and potential outputs. Availability is greatest for basic firm counts, which is a highly misleading indicator, and lowest for biotechnology R&D investment and biotechnol-ogy employment by field of application, which are possibly the two most useful indicators for inputs. With the exception of input indicators using patent or bibliometric data, all of these indicators are based on surveys of firms or public sector organisations and are consequently difficult, expensive, and time-consuming to collect. It will therefore be necessary to focus future data gathering exercises on a few high-value indicators. As part of this process, table 5.4 summarizes the strengths and weaknesses of 23 input indicators, in 7 categories, for evaluating inputs. The 23 indicators are drawn from both the examples of data sources given in tables 5.1, 5.2, and 5.3 and include additional indicators, for example on patents and bibliometrics. The most valuable indicators in table 5.4, and which should be included in a future survey of European biotechnology firms, are R&D expenditures in biotechnology and the number of employees active in biotechnology (the latter can also serve as an indicator for economic impacts). Of course, collecting these indicators should automatically provide other low value data, such as on firm counts. In both cases, the most useful data would be expenditures on biotechnology R&D and biotechnology active employment by field of application. Both can usually be estimated by the firm’s major area of biotech-nology activity, rather than asking for a breakdown of biotechnology R&D or employment into specific fields. Data on total R&D or employment is only worth collecting if the survey also collects data on biotechnology R&D and biotechnology active employment.



Table 5.4: Value of input indicators for assessing investments in biotechnology and future potential outputs

Description of indicator Strengths & limitations Value Last year

Data Avail-ability

Data Quality

1. Firm counts 1a Number of firms active in biotech-

nology. Strengths: None, unless no other data on biotechnology activity is avail-able. Limits: Large differences in how a biotechnology firm is defined, plus a highly misleading measure of impacts.

VL 2003 H M

1b Number of spin-offs or recently created firms.

Strengths: Gives an idea of commercial opportunities plus availability of start up capital. Limits: New firms and spin-offs could more closely reflect availability of risk capital than commercial opportunities.

L - L L

1c Counts of firms by size. Strengths: Can estimate total employment. Limits: Crude estimate, cannot estimate biotechnology active employ-ment.

L 2003 M M

2. R&D 2a Total R&D by biotech firms. Strengths: Combined with biotech R&D, can estimate the share of firm

research for biotech. Limits: By itself, without additional data on biotech R&D, it can be mis-leading, but easy to collect.

H 2003 M H

2b Biotech R&D by biotech firms. Strengths: Good indicator for private sector investment in biotech R&D. Limits: R&D is not a measure of potential outputs, long lag between re-search and commercial results

H 2003 H M

2c Biotech R&D by biotech firms by application field.

Strengths: Best indicator for private sector investment in biotech R&D. Limits: R&D is not a measure of potential outputs, long lag between re-search and commercial results.

M 2003 L M

2d Public sector investments in bio-tech R&D.

Strengths: Good indicator for public sector investment in biotech re-search. Limits: Very difficult to obtain from existing data collection methods.

I 2003 L M

2e Public sector investments in bio-tech R&D by application field

Strengths: Best indicator for private sector investment in biotech R&D. Limits: Extremely difficult to obtain from existing data collection methods. Needs detailed follow-through study to pinpoint specific technologies; long lag between research and commercial results.

I 2003 L L



Table 5.4 continued


Data Avail-ability

Data Quality

2f Public sector subsidies of business R&D.

Strengths: Measures dependence of firms on public support, particularly if there is a lack of private capital sources. Limits: Long way from commercialisation.

M - L L

3. Employment 3a Total biotech firm employment. Strengths: Combined with biotech employment, can estimate the share

of firm effort in biotech. Limits: By itself, without additional data on biotech employment, it can be misleading, but easy to collect.

M 2003 H M

3b Total biotech employment by application.

Strengths: Combined with biotech employment, can estimate the share of firm effort in biotech and disaggregation by application provides better information on consequences. Limits: By itself, without additional data on biotech employment, it can be misleading, but more difficult to collect than disaggregated total em-ployment.

H 2003 H L

3c PhD graduates in biotechnology, including by application field

Strengths: Good indicator for capacity of the education system. Limits: Presently only available for life sciences, not for biotechnology.

M 2004 H H

3d Biotech active employees Strengths: Good indicator for employee inputs into biotechnology and can also serve as an impact measure. Limits: Requires careful question design to collect.

H 2003 L M

3e Biotech active employees by application.

Strengths: Best indicator for employee inputs into biotechnology and can also serve as an impact measure. Limits: Only feasible to collect if all employees in a firm are assigned to a specific application.

H 2003 L L

3f New biotechnology active em-ployee hires as a percentage of total hiring

Strengths: Medium term plans of the industry, especially when data available by sector. Limits: Need data on the expected job function of new hires, i. e. In re-search, production etc.

M - L L



Table 5.4 continued


Data Avail-ability

Data Quality

4. Collaboration 4a Can refer to R&D, marketing, or

other types of collaboration. Strengths: Can serve as an indicator of knowledge inputs or of pre-paredness for commercialisation. Limits: No consistency in how collaboration is defined.

L 2004 M L

2c Number of collaborations between large firms and DBFs by applica-tion field.

Strengths: Flow of knowledge between small and large firms, particularly useful if for late stage product development and marketing. Limits: Data quality, not all collaborations can be identified.

M - M L

2d Number of collaborations between large firms/DBFs and public re-search organisations.

Strengths: Flows of knowledge between public research sector and firms. Limits: Often far from commercialisation.

M - M L

5. Capital raised 5a Total capital raised in the previous

year. Strengths: Measures ability of firms, particularly SMEs, to raise capital. Limits: To be useful, would need data on the purpose of the capital, i. e. for research or to take a product to the market.

L-I 2003 L L

6. Patents 6a Number of patents in defined bio-

technology patent classes. Strengths: Intermediate measure between inputs in terms of R&D and outputs in terms of commercial inventions, also a measure of knowledge base in biotechnology and national research capabilities. Can be tailored specifically to application fields. Limits: Data will both over- and underestimate biotechnology patents, probably the majority of biotechnology patents will never be commer-cialised. Time lag in data availability.

H-NS 2002 H H

6b Number of patents by field of application.

As above. H-NS 2002 H H



Table 5.4 continued


Data Avail-ability

Data Quality

7. Bibliometrics 7a Number of biotechnology publica-

tions or citations to biotechnology publications.

Strengths: Indicator for early stage research, also a measure of knowledge base in biotechnology and national research capabilities. Limits: Far from commercialisation.

H-NS 2004 M H

7b Biotechnology publications or cita-tions by application field (per million inhabitants or thousand researchers)

Strengths: As above, also early stage efficiency indicator. Limits: Far from commercialisation.

H-NS 2004 M H

Value: H = high, priority for future data collection, M = moderate, only worth collecting if existing availability is High or Moderate in order to complete data sets, L = low, VL = very low, I = data collection difficult so indicator value is impractical. Last year: Last year for which comparable data are available for several countries. Data availability: Complete = all countries covered in study; high = 10 or more countries, medium = 5-9 countries, low = < 5 countries. NS = no survey required. Data quality: H = assessed as generally high quality, M = medium quality, L = low quality.

Data sources: Firm counts: Official OECD surveys, industry trade associations, consulting reports. R&D: Official surveys, both of biotechnology sector alone and national R&D surveys that include a question on biotechnology R&D. The OECD education database provides internationally comparable data on the supply of new PhDs in the life sciences. Employment: Official surveys. Collaboration: Official surveys, also some coverage by private data sources such as the MERIT CATI database and the private survey consultant bioportfolio (www.bioportfolio.com). The latter covers the US and some of the European countries, but neither the biotechnology definition underlying the studies nor the coverage of countries chosen for the different studies are identical or follow OECD standard. Thus the coverage of data and the quality are assessed as medium. Capital raised: Official surveys, venture capital associations. Patents: Patent agencies (USPTO, EPO) plus EPPATENT and WOPATENT databases. The latter cover all patent applications at the European patent office and Euro-PCT applications and are offered by the host Questel Orbit and charge according to the time used and number of hits downloaded. Data availability and quality are high. Bibliometrics: The Science Citation Index (SCI®) provides access to current and retrospective bibliographic information, author abstracts, and cited references found in 3,700 of the world’s leading scholarly science and technical journals covering more than 100 disciplines. Overall data availability and quality is assessed as good. However, searches in the SCI are not for free. Costs depend on type and volume of searches. The output and significance of the results depends critically upon the search strategy, i. e. the ability to delineate the topic to be analysed by key words.



Data on public sector R&D expenditures and employment in biotechnology would be of value, but these indicators are difficult to obtain and are not collect by current statistical systems. Therefore, we do not recommend the effort required to obtain these indicators separately, par-ticularly because public R&D spending could be further from commercialisation than private R&D expenditures. However, we suggest using the results of the EU BioPolis project, which aims at compiling public R&D expenditure in biotechnology in all Member States. The results will be published in July 2006. Patent and bibliometric data are also useful as an indicator of national capabilities. Patent and bibliometric indictors have three important advantages compared to many other indicators. First, they can be tailored specifically to different application fields by designing suitable search strategies. Second, comparability between countries is very high because exactly the same definitions are used. Third, geographic coverage is 100 % because all countries under consideration can be included. The disadvantage of patent and bibliometric indicators is that they measure capabilities that are far from the market and hence the consequences of biotechnology.

6. Medical and pharmaceutical applications: application-specific out-put and impact indicators

6.1 Output indicators 6.1.1 Description of phenomena and indicators The adoption of biotechnology in the medical and pharmaceutical sector can be described by 39 indicators for 6 phenomena, as shown in Table 6.1. The two phenomena "policy activities with biomedical background (No. 4)" and "public involve-ment in biotechnology decision making (No. 5)" explain the socio-political atmosphere for the diffusion of biotechnology innovations. These phenomena can be used to elucidate barriers for adoption and make predictions for the acceptance of new technologies within a society. A second class of phenomena such as the "use of biotechnology for the production of biopharma-ceuticals and other biotechnology derived products (No. 1)”, the "development of small mole-cules that use biotechnological processes (No. 6)" and the "diffusion of biotechnology goods and services in the health service sector (No. 3)" describe the current situation and allow direct estimations of the economic relevance of biotechnologies for the biomedical sector. Phenomenon 1 describes the adoption of biotechnology for the production of human and veteri-nary biopharmaceuticals and other biotechnology derived products on the product level. Bio-pharmaceutical output can be analysed both on an aggregated level for all products and on different types of drugs (e. g. hormones, antibodies etc.). The level of aggregation depends on the desired depth of the analysis. The phenomenon 1 is divided into two subcategories, human health and animal health. This phenomenon illustrates the extent and ability of industry to de-velop and use biotechnologies. Thus it is a criterion to describe future competitiveness of in-dustry. Though indicators for the human and the veterinary sectors are similar, data availability differs among the sectors. Whereas the human sector is well illustrated there is only limited data in the animal sector. For this, indicators for human and the animal applications are described separately in Table 6.1. In the human sector three types of indicators are suggested: economic indicators providing information on market penetration of biotech products (1-1a, 1-1b, 1-1e, 1-1g, 1-1k, 1-1m), technology-oriented indicators (1-1c, 1-1d, 1-1f, 1-1h, 1-1i) that measure the degree of biotechnology adoption in the pharmaceutical development process, and research type indicators (1-1l) which assess the way in which biotech research has been established in the sector. Most indicators are of high quality and broad coverage. Similarly, the adoption in the animal health sector (phenomenon 1-2) is measured by economic indicators (1-2a, 1-2b, 1-2c, 1-2g, 1-2h), technology-oriented indicators (1-2d, 1-2e, 1-2f) and a research type indicator (1-



2i). In contrast to the human health sector, the data for the assessment of animal health industry is less well documented and of inferior quality (for details see section 6.1.2). Phenomenon 2 describes the earliest steps in the adoption of new technologies that are ex-pected to lead to new products in the future. The transfer of basic knowledge into applied (product-related) science is a problem especially in the medical sector, as basic research- and patient-oriented research often are carried out by different disciplines and under different insti-tutional settings. The phenomenon includes four technological indicators that assess the uptake of research results into products. The overall data quality is high. Phenomenon 3 describes the diffusion of biotechnology products and activities into health ser-vices, i. e. to the user in clinics, private service companies, and medical doctors. It illustrates the openness of the health care market for biotech products and is consequently a criterion that describes future market success. The phenomenon is assessed by four economic indicators that provide information on market penetration of biotech products such as molecular diagnos-tics and biotech drugs. The overall data quality is medium. Phenomenon 4 covers two indicators for biotechnology use that are strongly dependent on health policy decisions. These activities are an indication for the awareness and the openness of policy to innovative products based on biotechnology. They evaluate the market potential for novel biotechnology products by their reimbursement situation and the attitude of policy makers towards biotechnology derived population measures such as screening programs. Data quality is poor. Phenomenon 5 covers public involvement in biotechnology decision making. This phenomenon is linked indirectly to the adoption of biotechnology through the capability of lay people to get informed and to come to a decision autonomously. However, it does not show the willingness of society to adopt biotechnology innovations, as the level of information available is not a measure of public acceptance of biotechnology. Both indicators are poorly developed and data quality is low. Phenomenon 6 describes the integration of biotechnology into the discovery, development and production of small molecule therapeutics. This phenomenon is linked to the section "biotech-nology in industrial manufacturing" (chapter 8). However it illustrates the adoption of biotechnol-ogy by the pharmaceutical industry on a process level (in contrast to the product level of phe-nomenon 1). This can be a criterion for the future competitiveness of the pharmaceutical indus-try and thus an important phenomenon in the medical sector. This is a crucial area for evalu-ating the consequences of biotechnology, since small molecule drugs account for the majority of both the number of therapeutics under development and the targeted patient populations. Un-fortunately, hardly any information is available.



Table 6.1: Output indicators for medical and pharmaceutical applications of biotechnology

No. Description of indicator Strengths & limitations Value

Last year

Data availability

Data quality

1-1 Use of biotechnology for the production of biopharmaceuticals and other biotechnology derived products for humans 1-1a Share of biotech derived drugs

in all new drugs launched. Very good, direct measure for adoption of biotechnol-ogy in the industrial drug development process, but slightly unclear definition of a biotech drug.

H 2006 C H

1-1b Domestic market prescriptions of biotech derived drugs / total domestic pharmaceutical pre-scriptions.

Good indicator of openness for innovative product, but slightly limited as it does not solely illustrate phenome-non 1 but is influenced by the legal framework.

H 2006 C H

1-1c Share of clinical studies with biopharmaceuticals related to total number of clinical studies.

Very good indicator for adoption of biotechnology in an early developmental stage but unclear definition of biotech drug.

H 2006 C H

1-1d Distribution of clinical studies with biopharmaceuticals in stages I to III / total number of clinical studies with biophar-maceuticals.

Good indicator for the time horizon of adoption in the pharmaceutical sector but slightly unclear definition of biotech drug.

M 2006 C H

1-1e Number of cell-based thera-pies on the market related to time

Direct measure for adoption of new technologies but unclear definition of cell-based therapies and small number of products, influenced by legal framework.

M 2006 C H

1-1f Number of innovative therapies (e. g. gene therapy trials, stem cell clinical trials) related to time.

Very good measure for adoption in an early develop-mental stage with broad therapeutic target but hetero-geneous approaches.

H 2006 C H

1-1g Number of gene therapy products on the market related to time.

Direct measure for adoption but currently small num-ber and influenced by the legal situation.

M 2006 C H

1-1h Number of therapeutic biotech vaccines in clinical trials / total number of biotech vaccine approaches in development.

Very good measure for adoption in an early develop-mental stage with clear therapeutic target but unclear delineation of therapeutic vaccines.

H 2006 C H



Table 6.1 continued


Last year

Data availability

Data quality

1-1i Number of products using new

vaccination strategy (e. g. car-bohydrates, DNA) / total num-ber of vaccines

Direct measure for adoption of new technologies but early stage development, kept in the industrial context confidentially, thus data availability in public databases lags behind the actual research progress

M 2006 C H

1-1k Number of recombinant vac-cines launched / total number of vaccines in development

Clear direct measure for adoption of biotechnologies in drug development

H 2006 C H

1-1l Pharmaceutical industry value added over time

Information on economic relevance of sector but indi-rect measure; not only due to biotechnology

L 2004 H H

1-1m Proportion of medicines that were first in class according to mechanism

Measure for level of innovative activities in the sector but not only biotechnological products.

M 2002 H M

1-2 Use of biotechnology in the veterinary sector for the production of biopharmaceuticals and other biotechnology derived products 1-2a Share of vet. biotech derived

drugs in all new drugs launched.

Very good, direct measure for adoption of biotechnol-ogy in the industrial drug development process, but slightly unclear definition of a biotech drug.

H 2005 M M

1-2b Number and amount of antibi-otics used in the agro sector related to number of animals and application area (therapy, prophylaxis, enhancement)

good indicator for the penetration of vet. drugs into the agro-sector but not strictly biotech derived and influ-enced by regulatory frame work, difficulties to gain the exact value of denominator

M 2005 M M

1-2c Number of biotech derived drugs used in husbandry re-lated to total domestic phar-maceutical consumption in the agro-sector

Very good indicator of openness for innovative product, but slightly limited as it does not solely illus-trate phenomenon but is influenced by the legal framework and weakness in the delineation of biotech drug

H 2005 M M

1-2d Share of biotech products in the development related to all animal drugs in development

Very good indicator for adoption of biotechnology in an early developmental stage but unclear definition of biotech drug.

H 2005 M M



Table 6.1 continued


Last year

Data availability

Data quality

1-2e Number of therapeutic biotech

vaccines in clinical trials / total number of biotech vaccine approaches in development.

Measure for adoption in an early developmental stage with clear therapeutic target but unclear delineation of therapeutic vaccines.

M 2005 M M

1-2f Number of products using new animal vaccination strategy (e. g. carbohydrates, DNA) / total number of vaccines

Direct measure for adoption of new technologies but early stage development, kept in the industrial context confidentially, thus data availability in public databases lags behind the actual research progress

M 2005 M M

1-2g Number of animal rDNA vac-cines launched / total number of animal vaccines

direct measure for adoption of biotechnologies in drug development

H 2005 M M

1-2h Number and amount of feed additives produced per year related to animals in the agro sector (categorised by animal classes)

Very good indicator of openness for innovative product, but slightly limited as it does not solely illus-trate phenomenon but is influenced by the legal framework and weakness in the delineation of feed additive

H 2005 M H

1-2i Animal health industry value added over time

Information on economic relevance of sector but indi-rect measure; not only due to biotechnology

L 2005 H M

2 Linkages between basic/applied research results and future outputs 2a number of treatable genetic

diseases related to identified gene defects

Indirect indicator for the potential effect of basic re-search on the health status, but unclear definition of tractability

M 2005 C H

2b size and coverage of gene chips

Indicator for productivity in discovery and screening, but unclear definition of „gene“ and the relevance of customized chips

M unknown H M

2c Type and number of quality assurance in molecular diag-nosis

Indirect measure for reliability and accuracy of tests and for the advances molecular diagnostics

M not available

L L

2d Number of stem cell patent applications over time

Indirect indicator of potential therapies but time lag due to 18 months period until patent is published

M 2004 C H



Table 6.1 continued


Last year

Data availability

Data quality

2e Number of biotechnology

pharmaceutical patents / total patents in pharmacy

Very good Indicator for potential economic relevance of biotechnology in the pharmaceutical sector

H 2004 C

2f number of new technologies used in human diagnostic labo-ratories (e. g. biosensors, mi-croarrays, immunoassays, PCR) related to traditional testing methods

measure for direct adoption of new technologies, but unclear definition of new technology tests and tradi-tional technology tests

L n.a. L L

2g number of new technologies used in veterinary diagnostic laboratories (e. g. biosensors, microarrays, immunoassays, PCR) related to traditional testing methods

measure for direct adoption of new technologies, but unclear definition of new technology tests and tradi-tional technology tests

L n.a. L L

3 Diffusion of biotechnology goods and services in the health service sector 3a turnover of (service) compa-

nies in the field of molecular diagnostics (e. g. immuno-assays, genetic testing)

very good measure for diffusion of new products to users, but unclear definition of molecular testing (can also act as an impact measure)

H 2004 H M

3b number of molecular diagnos-tic tests (e. g. genetic tests, immunotests) per year and inhabitant

direct measure for adoption in the clinic but unclear definition of molecular diagnostic tests

H 2004 L H

3c Biotech derived drugs on mar-ket / all prescription drugs on the market

direct measure for openness of market but need to clarify the definitions of a biotech drug. (can also act as an impact measure)

H 2002 H M



Table 6.1 continued


Last year

Data availability

Data quality

3d number of biotechnological

products such as molecular diagnostics, vaccines and bio-pharmaceutical drugs used by health institutions

direct measure for acceptance of new products by users but influenced also by legal situation (e. g. reim-bursement)

M 2002 H M

4 Policy activities with biomedical background 4a Population screening pro-

grammes using molecular dia-gnostics

indirect measure for the awareness and significance of biotechnologies to policy makers, but information only on a case-by-case level (e. g. PSA blood tests)

M 2002 M M

4b Reimbursement situation for novel treatments (e. g. number of reimbursed biotechnology derived products related to approved biotechnological products) as outlined by the policy makers and insurance companies on a general level and according to specific indi-cations (e. g. positive list, case-by case decision)

Direct measure for the relevance of new biotechno-logical products as seen by health care funders, but varies across different insurance companies/systems

H not available

L L

5 Public involvement in biotechnology decision making 5a Participatory activities per

country measure for social adoption, but no information about quality and outcome of participative process

M not available

L L

5b Provision of appropriate infor-mation to the user(patient) in conducting novel biotechnol-ogy-related medical activities

direct measure for information basis of biotechnologi-cal developments in the public but no information about quality of information

H not available

L L



Table 6.1 continued


Last year

Data availability

Data quality

6 Adoption of biotechnological process technology, production of small molecule therapeutics using biotechnological processes; 6a Industrial processes in the de-

velopment of small drug mole-cules that use biotechnological methods related to all chemical process

direct measure for diffusion of biotechnology pro-cesses in pharmaceutical production

H not available

L L

6b Share of processes that use small molecule drug develop-ment related to chemical pro-cesses for the same purpose

direct measure for diffusion of biotechnology pro-cesses in pharmaceutical production

H not available

L L

6c Use of and investment in bio-technological equipment re-lated to all production plants

direct measure for diffusion of biotechnology pro-cesses but unclear definition of biotechnology equip-ment

H not available

L L

Notes: Value: result of strengths and limitations, assessment by team Data availability: complete (C) = all countries covered in the study; high (H) = more than 10 countries; medium (M) = 5-9 countries; low (L) = < 5 countries Data quality: H: high: source = CDB (complete population database) or GS (government survey) and OECD def. and data from >2001 M: medium: source = PSC (private survey consultant) or PSA (private survey academic) and OECD def. and data from >2001 L: low: all other



6.1.2 Assessment of data quality and data availability As shown in Table 6.1 and outlined in more detail in the annex in tables A.6.1 and A.6.2, data availability and quality varies among the different phenomena. Whereas a large number of indi-cators can be constructed for the “use of biotechnology for the production of biopharmaceuticals and other biotechnology derived products (No. 1)”, hardly any data are currently available for the evaluation of phenomena 4, 5 and 6. In the following section, the various sources and alter-native approaches for data search are described. Data for most of the indicators assessing phenomenon 1 (1-1a, 1-1b, 1-1c, 1-1d, 1-1e, 1-1f, 1-1g, 1-1h, 1-1k) can be analysed using the PJB database pharmaprojects. This database tracks global pharmaceutical R&D since 1980. Over 35,000 drugs are tracked with their own profile, details of the compound’s history and progress to date. The database allows a global search for medical products in all developmental stages, from the pre-clinic to the registered product. Data is continuously updated and the quality of data is assessed as good. An online subscription costs approx. 6,200 € per year. Other sources for the assessment of phenomenon 1 are the Science Citation Index (indicator 1-1i) as described in the notes to table 5.4, the OECD STAN Database (indicator 1-1l) and the reports of the Pharmaceutical Industry Competitiveness Task Force (PICTF report) in the UK, that summarizes data from various sources such as the OECD, IMS data and Frost&Sullivan market studies (indicator 1-1m). The OECD STAN database includes annual measures of output, labour input, investment and international trade which allow users to construct a wide range of indicators to focus on areas such as productivity growth, competitiveness and general structural change. Through the use of a standard industry list at the two and three digit sector level (NACE and ISIC, third revision), comparisons can be made across countries. The industry list provides sufficient detail to enable users to highlight high-technology sectors and is compatible with those used in related OECD databases. However, a major disadvantage is that the STAN data are not available for biotech-nology because biotechnology is not a sector. The biotechnology share must be estimated from STAN data for the pharmaceutical sector. STAN is primarily based on Member countries’ annual national accounts by activity tables and uses data from other sources, such as national industrial surveys/censuses, to estimate any missing detail. Since many of the data points in STAN are estimated, they do not represent official Member country submissions. Data availability and quality is assessed as good. The Pharmaceutical Industry Competitiveness Task Force (PICTF) is a consortium formed by the Association of the British Pharmaceutical Industry (ABPI) and the Department of Health in the UK. Yearly, this group publishes a set of indicators, analysed from various sources such as OECD data, IMS data, and market studies of Frost&Sullivan, that help in monitoring the com-petitiveness of the UK relative to other countries (EU, US) as a location for the pharmaceutical industry. The data coverage and quality is medium to high depending on the indicator. Data to monitor the veterinary sector within phenomenon 1 are scarce. The private database "animal health" sold by C.F. Grass Consulting covers merely sales trends and marketing as-pects since 1999. However technological aspects are missing. Other sources to monitor the animal health sector are the national databases on approved animal drugs as complied by the national approval authorities (FDA, EMEA etc.). However within these databases it is difficult to distinguish among biotech- and non-biotech-drugs. Additionally it is difficult to determine the denominator for some indicators as it seems to be essential in terms of comparability to refer to the number of animals in the agricultural sector in a specific country. It is recommended to con-duct an expert survey and search in national database such as the Database of Approved Ani-mal Drug Products (FDA Center for Veterinary Medicine, VMRCVM Drug Information Lab; http://dil.vetmed.vt.edu/default.htm) and the Community register of veterinary medicinal pro-



ducts (http://dg3.eudra.org/F2/register/ vreg.htm). The overall data availability is assessed as medium, as only limited detailed analyses for separate countries can be conducted, data quality is assessed as medium. Indicators for phenomenon 2 can be developed using data sources that also serve other phe-nomena. Indicator 2a can be analysed by using the Science Citation Index and indicators 2d, 2e and 2f can be developed using patent databases. The quality dependents on the definition of the search terms. Data availability is good. Data availability and quality for the indicators 2b and 2c is poor. For the analysis of indicator 2b it is recommended to analyse company listings (e. g. http://www.biotech-europe.de/rubric/produkte/products_04/LjPr-04-12(A4)neu.pdf and http://www.gene-chips.com/GeneChips.html#Review %20Articles %20on %20Microarray % 20Technology) and check the scientific literature. For the analysis of indicator 2c an expert sur-vey with experts from institutions responsible for approval, with users and test producing com-panies is recommended. The data availability and quality is currently assessed as poor to me-dium. Indicators for phenomenon 3 can be analysed using governmental studies from the US (http://www.hhs.gov/news/press/2001pres/01fsgenetictests.html) and the UK (http://www. parliament.uk/documents/upload/POSTpn227.pdf), however data coverage and quality is only poor to medium as the information is rather old (for the US from 1996) and it cannot be assured that the underlying definition follows OECD standards. Additional information can be drawn from the IPTS study by Dolores Ibarreta et al. “Towards quality assurance and harmonization of ge-netic testing services in the European Union (2003). Phenomenon 4 is less well documented. Indicator 4a can be analysed only on a case-by-case basis (e. g. for blood testing in prostate cancer diagnosis) following the study on screening in the EU carried out by national experts within the EU HTA programme in 1999 and 2002. To gather more data it is recommended to conduct expert interviews with national health service representatives, policy makers and insurance companies. Currently the data availability and data quality are poor. Data for the assessment of the phenomena 5 and 6 are currently not available at all. It is re-commended to conduct expert interviews and a company survey. 6.1.3 Recommendations for sector studies A sector study for the medical and pharmaceutical sector should analyse all phenomena of the innovation process along the value chain and describe the role of the different actors in the in-novation system. We suggest collecting the following 26 key indicators to describe current and future effects of biotechnology adoption in the medical and pharmaceutical sector under a socio-economic perspective (Table 6.2) Some of the indicators are already described in the In-put chapter 5. As these input indictors describe also aspects of adoption they are mentioned in Table 6.2 in order to allow a complete overview on desirable indicators. The choice of the technological details (e. g. cell-based therapies or gene therapy) depends on the previous knowledge of the research team and the easiness of data accession. Crucial for technological indicators like 1-1f is only the fact, that an innovative technology in an early developmental stage should be observed. The methodological approach applies database searches, bibliometric and patent analysis (see chapter 5 for additional details), and experts and company surveys. Table 6.2 includes several key input indicators in addition to output indicators. The selected input indicators are also helpful for assessing future economic and social consequences of the health applications of biotech-nology.



Table 6.2: Key indicators and methods for data collection for a study in the medical and pharmaceutical sector

No Key indicator Method for data collection 1-1a Share of biotech derived drugs in all new drugs

launched pjb database pharmaprojects

1-1b Domestic market volume of biotech derived drugs related to total domestic pharmaceutical market

pjb database pharmaprojects

1-1c Share of clinical studies with biopharmaceuticals related to total number of clinical studies


1-1f Number of innovative therapies (e. g. gene thera-py trials, stem cell clinical trials) over time


1-1h Number of therapeutic vaccines in clinical trials related to total number of therapeutic vaccine approaches in development


1-1k Number of recombinant vaccines launched or in development / total number of vaccines launched or in development


1-2a Share of biotech-derived veterinary drugs in all drugs launched

national approval databases

1-2c Number of biotech-derived drugs used in hus-bandry related to the total domestic pharmaceuti-cal consumption in the agro-sector

market studies of Frost and Sulli-van, and C.F. Grass Consulting Animal Health Database

1-2d Share of biotech products in the development re-lated to all animal drugs in development

national approval database and expert interviews

1-2g Number of animal rDNA vaccines launched/ total number of animal vaccines

national approval databases and expert interviews

1-2h Number and amount of feed additives per year related to animals in the agro sector (categorised by animal classes)

national information platform and leaflets and expert interviews

2e Number of biopharmaceutical patents of industrial applicants / all biopharmaceutical applicants

EPPATENT and WOPATENT databases

2f Number of biotechnology pharmacy patents / total number of patents in pharmacy


3a Turnover of service companies in the field of mo-lecular diagnostics

governmental studies, IPTS study on genetic testing, expert and company survey

3b Number of molecular diagnostic tests per year governmental studies, IPTS study on genetic testing, expert and company survey

3c Share of biotech derived drugs in all prescription drugs on the market

governmental studies, IPTS study on genetic testing, expert and company survey

4b Reimbursement situation for novel treatments as outlined by the policy makers and insurance com-panies on a general level and according to spe-cific indications (e. g. positive list, case-by case decision)

expert interview with national health service representatives, policy makers and insurance companies

5b Provision of appropriate information to the user (patient) in conducting novel biotechnology-re-lated medical activities

expert interviews with national technology assessment boards patient organisations, medical doctors etc.

6a Industrial processes in the development of small drug molecules that use biotechnological methods related to all chemical process

company survey



Table 6.2 continued

No Key indicator Method for data collection 6b Share of processes that use small molecule drug

development related to chemical processes for the same purpose

company survey

6c Use of and investment in biotechnological equip-ment related to all production plants

company survey

Input number of new biotechnology-related SMEs / All new SMEs in a year

OECD biotechnology statistics

Input Number of biotechnology staff hired per year by the pharma sector / total staff hires

OECD biotechnology statistics

Input Number of new graduates with degrees in sciences relevant to biopharmaceutical industry

OECD education database

Input number of publications in the field of biopharma-ceutic research per 1000 researchers

Science Citation Index

Input Number of biopharmaceutical patents per million population


6.2 Application-specific impact indicators 6.2.1 Description of phenomena and indicators From the perspective of biotechnology applications in healthcare, biotechnology is one of seve-ral paradigms for developing treatment options, whereas the outcomes of biotechnologically developed treatments are generally measured with the same indicators as their conventionally developed counterparts. Application-specific indicators for biotechnological treatments are rare. Phenomena and indicators describing the impacts of biotechnology treatments are:

(1) The functioning of processes, specific indicators analyse the functioning of treatments or of the whole healthcare system, healthcare expenditures, costs for treatments etc.),

(2) The presence of symptoms for disease which can be measured by morbidity and labora-tory parameters,

(3) "Surrogate endpoints“ measured e. g. quality of life resulting from a treatment,

(4) Final outcomes measured by mortality from diseases treatable with biotech therapeutics,

(5) Health economic effects measured by composite indicators (e. g. QUALYs, cost-benefit ratios for treatments etc., which combine indicators from phenomena 1 to 4)

Some process indicators are identical to output indicators as outlined in Table 6.1. Process indi-cators such as "value of biotech products", "number of treatments receiving marketing authori-sation", "availability and application of biotechnology treatments" are covered by the output indi-cators 1-1b, 3b, 3c and 3d. These indicators with double use are extremely helpful for the evaluation of the sector from different perspectives. However, in the case of impact indicators they have no key function. For this reason they are not integrated in Table 6.3. Impact indicators for the health sector can be aggregated on different levels: - individual patient (no aggregation), - population (or other group) level. Having in mind that major European policy goals are to become the most competitive and dy-namic knowledge-based economy in the world capable of sustainable economic growth with more and better jobs and greater social cohesion and respect for the environment, the major



interest of the Bio4EU study must be the analysis of indicators at the population level rather than the individual level. Thus the Bio4EU study aims at identifying and quantifying (where fea-sible and appropriate) the contributions of modern biotechnology to achieve European policy objectives. Table 6.3 describes indicators that can be used to assess the application-specific impacts of biotechnology applications in the medical and pharmaceutical sector.



Table 6.3: Phenomena and indicators that characterize the impact of biotechnology on the medical and pharmaceutical sector

No. Description of indicator

Strengths & limitations

Value

Last year

Data avail-ability

Data qual-

ity

1 Process indicators: treatment costs 1a Costs or prices for treatments,

Different forms of costs exist: costs of production accruing for the pro-ducer; prize reimbursed by health insurance; out-of-pocket-costs for the patient; societal costs including indirect cost factors

Allow direct comparison of costs of biotech vs. conventional treat-ments; however, differences in effectiveness are only covered by composite indicators (see below). Biotech treatments are often high-cost and highly effective103. Validity dependent on type of cost under question. This indicator is not available in international sta-tistics on a level detailed enough to distinguish single or all bio-tech-related diseases. Only studies are available for some coun-tries, which would have to be identified in literature databases as the Cochrane Library104, MEDLINE105 or EMBASE106.

M un-known

H H

2 Indicators for the presence of disease symptoms 2a Prevalence: The presence of dia-

gnosed disease is a major indica-tor for individual and public health. Some diseases are strongly linked to health applications of biotech and therefore can serve as an in-dicator of their impacts.

Although the prevalence of a disease is influenced by many factors, and the reduction of diseases cannot simply be linked to specific (biotech) treatments, for some diseases quite frequently biotech products are applied (e. g. Fabry's disease; Multiple Sclerosis). No direct link between existence of symptoms and use of a specific biotech treatment. This indicator is not available in international statistics e. g. WHO-EURO European Health for All Database (HFA-DB107) or WHO Statistical Information System (WHOSIS108) on a level detailed enough to distinguish single or all biotech-related diseases. Only studies are available for some countries, which would have to be identified in literature databases as MEDLINE or EMBASE.

M un-known

H H

103 see OECD (2005): Health-technologies-and-decision-making. Paris: OECD Publishing. 104 see http://www.thecochranelibrary.com/ 105 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?holding=nlmlib 106 see http://www.embase.com/ 107 see http://www.euro.who.int/hfadb 108 see http://www3.who.int/whosis/menu.cfm?path=whosis,burden,burden_estimates,burden_estimates_2002N,burden_estimates_2002N_2002Rev_country&language=english



Table 6.3 continued



Value

Last year

Data avail-ability

Data qual-ity

2b Mortality: The rate by which pa-tients die from disease which can be treated by a biotech-based approach in all patients diagnosed with the respective disease, e. g. standardized death rates

Mortality is influenced both by prevalence of the disease as well as by effectiveness of the available treatment. As some diseases are highly linked to health applications of biotech, mortality can serve to assess the impacts of biotech applications for health. This indicator is not available in international statistics e. g. from WHO on a level detailed enough to distinguish single or all biotech-related diseases. Only studies are available for some countries, which would have to be identified in literature databases as MEDLINE or EMBASE.

M un-known

H H

3a Surrogate endpoints109: Healthy Life Years that are lost because patients suffer from a dis-ease which can be treated by a biotech-based approach

As some diseases are highly linked to health applications of bio-tech, these indicators can serve to assess the impacts of biotech applications for health. This indicator is not available in interna-tional statistics e. g. from WHO on a level detailed enough to dis-tinguish single or all biotech-related diseases. Only studies for some countries available, which would have to be identified in literature databases as MEDLINE or EMBASE.

H un-known

L H

3b Quality-adjusted life years (QUA-LYs) lost due to specific diseases treatable with bio-therapeutics

see above, only studies available for some countries. H un-known

L H

3c Disability-adjusted life years (DA-LYs) lost due to specific diseases treatable with bio-therapeutics

see above. Indicator for the burden of a disease developed by WHO, principally available in WHOSIS, but not in sufficient detail. Only studies available for some countries.

H un-known

L H

109 Burden of disease. As some diseases are highly linked to health applications of biotech, these indicators can serve to assess the impacts of biotech applications for health.



Table 6.3 continued



Value

Last year

Data avail-ability

Data qual-ity

4 Indicators for final outcomes 4a Reduction in mortality: As some

diseases are largely treated with bio-therapeutics, these indicators can serve to assess the impacts of biotech applications for health.

High validity as reduction of premature mortality is the ultimate goal of all health interventions. Data in public databases are often aggregated in larger groups and therefore in general not available for relevant diseases. Comparative trials of bio-therapeutics versus alternative treatments often limited. Only studies for some coun-tries available, which would have to be identified in literature data-bases as MEDLINE or EMBASE.

H 2005 H H

5 Cost-effectiveness indicators110 5a Cost-utility ratio Information on costs and effects of a treatment required, Particu-

larly relevant for the comparison of two or more treatment options, Highly relevant for the evaluation of new of existing treatment op-tions; increasingly used for reimbursement decisions. Only studies for some countries available, which would have to be identified in literature databases as the Cochrane Library, MEDLINE or EMBASE.

H un-known

L H

5b Cost-benefit ratio see above (5a) H un-known

L H

Notes: Value: result of strengths and limitations, assessment by team Data availability: complete (C) = all countries covered in the study; high(H) = more than 10 countries; medium (M) = 5-9 countries; low (L) = < 5 countries Data quality: H: high: source = CDB (complete population database) or GS (government survey) and OECD def. and data from >2001 M: medium: source = PSC (private survey consultant) or PSA (private survey academic) and OECD def. and data from >2001 L: low: all other

110 Relation of the outcomes of a treatment with its effects, where the effects can be measured in presence of symptoms or quality of life (resulting in a cost-effectiveness ratio), in utility values (resulting in a cost-utility ratio), or in monetary units (resulting in a cost-benefit ratio).



6.2.2 Assessment of data quality and availability for application-specific impacts Generally, data for the assessment of impact indicators are available on a country- and case-specific basis. International or national organizations, and private or academic consultants, conduct studies on varying aspects of the impact of biotechnology on the health sector. Thus currently a comprehensive analysis of specific data availability is not possible. However a general description of sources, their validity and relevance will help to determine the choice of sources for a case-specific analysis in the sectoral study. Process indicators are of high relevance for the health care system, as they allow the direct comparison of costs of biotech versus conventional treatments. However, differences in effectiveness are only covered by composite indicators (see below). Details of their use were outlined by the OECD (2005b).111 Data sources for the analysis of indicator 1a (cost of treat-ment) are (national) databases on reimbursement, which exist in all developed countries. These databases comprise different forms of costs: costs of production accruing for the pro-ducer; price reimbursed by health insurance; out-of-pocket-costs for the patient; societal costs including indirect cost factors etc. However data availability is limited as specific categories for biotechnological treatments are normally not explicitly included. The data validity depends on the type of cost under question. Phenomenon 2 (the presence of disease symptoms) can be measured by indicator 2a (mor-bidity). The major goal of all health politics as well as of the development of new treatments is to reduce or avoid morbidity. However, due to alternative treatments, there are often no direct links between symptoms and the use of a specific treatment, although some diseases are frequently treated with bio-therapeutics (e. g. Fabry’s Disease, Multiple Sclerosis). Data sources for the assessment of indicator 2a on an indication-specific basis are available through various national disease-specific registries (e. g. cancer registries) or clinical trials conducted by university academics, national institutions (e. g. Robert Koch Institute), and in-ternational institutions (IARC). Clinical trials can be searched using MEDLINE. The best re-sults would be from comparative clinical trials that evaluate the efficacy of bio-therapeutics in respect to alternative treatments. Phenomenon 3 describes surrogate endpoints. Data for the assessment of indicators 3a, 3b and 3c are available on an indication-specific basis in various national disease-specific regis-tries (e. g. cancer registries) or clinical trials (MEDLINE). The computation of these indicators requires combining the results of clinical trials with data on prevalence rates. The data quality of individual clinical trials is generally high, but can vary widely. Phenomenon 4 describes final outcomes. An appropriate indicator is the reduction in mortality from specific diseases due to treatment with bio-therapeutics (compared to alternative treat-ments, where relevant). Epidemiological data are gathered in health monitoring systems or clinical trials and available through MEDLINE. Data on prevalence rates (for estimating mor-tality reductions) are available from public databases on a national or European level (e. g. WHO European Mortality Database). Quality is normally high. Health economic analysis (phenomenon 5) is carried out using various composite indicators (5a and 5b). They are highly relevant for the evaluation of new and existing treatment options and are increasingly used for reimbursement decisions. Data sources are population statistics (national health monitoring systems) and clinical trials databases that include economic evaluation (e. g. Cochrane Library112; Health Economic Evaluations Database113; and the NHS Economic Evaluation Database (NHS EED)114. The databases cover all countries and contain together over half a million entries. However data availability is poor for biotech treat-

111 OECD (2005b): Health-technologies and decision-making. Paris: OECD Publishing. 112 Cochrane Library 2005, issue 4; http://www.cochrane.org/index0.htm 113 UK Office of Health Economics; http://www.ohe-heed.com/ 114 http://www.york.ac.uk/inst/crd/nhsdhp.htm



ments. Only few economic studies could be determined for novel biotech treatment such as tissue-engineered products115, for example. 6.2.3 Recommendations for sectoral study Information that is necessary to appraise the use and usefulness of biotechnologies in the healthcare sector include data on the sales and prescription of products, their effectiveness, and the cost that arise for the patients or healthcare providers.

• Data on sales of products: Governmental medicines agencies hold databases with sales data for the respective country that they receive from the producers; these databases can easily be analysed for biotech products. Sales data can also serve as a generic impact in-dicator (see chapter 9).

• Prescription data: Drug prescriptions are generally recorded for outpatients, but in some countries this is restricted to reimbursed drugs. A few countries or jurisdictions collect data on all prescriptions, but access can be limited due to confidentiality constraints. Data on the application of individual drugs in hospitals are far less frequently collected because drugs in the hospitals are often paid for by flat rate payments. However, data from hospi-tals would be particularly needed as many biotech healthcare products are relatively new, treat serious illnesses, and require injections, and therefore are applied more frequently in hospitals than in the outpatient setting.

• Effectiveness data: Before receiving marketing authorisation, products including biotech-nology applications have to be shown to be safe and efficacious (although sometimes only in comparison with a placebo) in clinical trials. More informative is the product's effective-ness under routine application. This is frequently not assessed even for conventional products. Efficacy data for biotechnology products exist at least for study samples; the systematic collection of the use of different products for patient groups (having a specific disease, for example, or in the form of the UK NHS General Practice Research Database (http://www.gprd.com/home/) including the use of and outcomes of biotech products would be useful.

• Cost and cost-effectiveness data: The costs for a treatment depend highly on the or-ganisation of the respective healthcare system and therefore are not easily comparable under routine conditions between countries and even not between outpatient and inpatient care or between different treatments within a country. Therefore, cost data should be collected in dedicated cost-effectiveness (or cost-utility or cost-benefit) evaluation studies or in parallel to clinical trials. Such data are already required for reimbursement decisions in many countries; treatments and particularly innovative biotech treatments which are of-ten expensive and perceived as having an unfavourable cost-benefit ratio will have to show their competitiveness in terms of costs and effectiveness with conventional products to obtain access to a larger market.

As shown in 6.2.1, current data availability is limited on a case-specific level. In order to ana-lyse the impact of biotechnology on the medical and pharmaceutical sector it is recommended to choose three to four case studies that cover the different areas and developmental stages of biotechnological applications. Examples for possible case studies include:

• novel but already established biotechnological drugs e. g. interferon

• biotechnological diagnosis e. g. molecular diagnostics for breast cancer

• cancer treatment with a biotechnological product e. g. erythropoietin

• novel treatment aspects in an early stage e. g. recombinant vaccines

115 see Bock, A.-K., Rodriguez-Cerezo, E., Hüsing, B., Bührlen, B. & Nusser, M. (2005): Human tissue-engineered products: Potential socio-economic impacts of a new European regulatory framework for authorisation, supervision and vigilance. Technical Report EUR 21838 EN. Sevilla: IPTS (available online: ftp://ftp.jrc.es/pub/EURdoc/eur21838en.pdf).



Methods for the evaluation of the case studies by the impact indicators mentioned in Table 6.3 are database search (population monitoring systems, literature databases) and in the case of unavailability of data, expert interviews with health economists, policy makers, patient organisations and insurance companies.

7. Agro-food: application-specific output and impact indicators This chapter identifies statistics and indicators that are unique to agro-food applications be-cause they are based on a denominator that is not relevant to other applications. Input indi-cators with common denominators, such as R&D expenditures or the number of researchers in each application, are covered in chapter 5, while impact indicators with common denomi-nators, such as revenues, employment, sales, or value-added, are covered in chapter 9. There are four main agro-food applications of biotechnology:

1. Veterinary products such as diagnostics, vaccines, hormones and therapeutics116; 2. Molecular diagnostics such as DNA fingerprinting / genetic identification for safety

and traceability, including differentiating wild and domesticated varieties of plants and animals;

3. Reproduction methods such as tissue culture (including micropropagation, embryo rescue, somatic embryogenesis, etc117) in plants or embryo transfer and in vitro em-bryo production for animals; and

4. The development of new plant and animal (insects, fish, livestock) varieties of existing species118.

The supply of indicators on the first three applications is very poor. Consequently, they are only discussed in sections 7.1.3 and 7.2. In respect to the fourth application, good data is re-quired for three methods of developing plant and animal varieties:

1. Varieties that include one or more genes introduced through rDNA technology or ge-netic modification (GM).

2. Varieties that do not use GM but which are developed through the use of a diverse range of biotechnologies, including marker assisted selection (MAS), and other methods (see chapter 3).

3. Varieties developed through conventional or ‘traditional’ breeding that do not use any modern biotechnologies.

The statistical framework for agricultural applications of biotechnology is generally very good for GM plants (including crop plants, industrial feedstock, and trees) and animals (including fish, livestock and insects). This is partly because countries maintain good agricultural statis-tics, such as the number of hectares planted to specific crops and their production value, and partly because most agro-food applications of GM biotechnology compete with alternative products that are already on the market. The latter provides a comparison group for esti-mating the value-added of specific GM traits. It is therefore relatively simple to develop indi-cators for the value-added of a GM plant (or animal) variety compared to a non GM variety. In practice, all commercial GM varieties are limited to crop plants119 (food, feed, and feed-stock), with the exception of a single commercial application of a GM tree in China.

116 Veterinary medicine is also discussed in chapter 6. 117 Tissue culture includes micropropagation which is a relatively simple technique for plant reproduction that is not included as part of ‘advanced’ biotechnology in some definitions. Conversely, somatic embryogenesis still creates substantial technical problems and is part of ‘advanced’ biotechnology. 118 The role of new varieties of microorganisms is covered in the chapter on industrial applications. 119 Other than crop plants, a GM fish variety is ready for commercialisation, but its commercial viability will depend on the willingness of the public to buy it. As there are probably strong doubts about this, its commercial introduction could be delayed for years.



Conversely, the statistical framework for MAS and other applications of biotechnology to the agro-food sector is extremely poor. It is almost impossible to differentiate between new plant and animal varieties developed through MAS and new varieties developed through conven-tional breeding. This could distort estimates of the value-added of GM crops because the comparison group includes both varieties that incorporate modern biotechnology and those that do not. These problems are discussed further in section 7.1.3. The agricultural applications of biotechnology are linked to both the health applications dis-cussed in chapter 6 and to the industrial applications discussed in chapter 8. Within the pro-duction chain, veterinary products are produced by the health sector and used by the agri-cultural sector. The situation between agriculture and industry is bidirectional: on the one hand, agriculture produces industrial feedstock, for instance biomass for energy or for plastics production, that are used in industry, on the other hand industrial products such as feed addi-tives are used in the agricultural sector. In both cases, the ‘output’ can be measured either among the producers or by the end users. The choice of where to measure outputs largely depends on what is less expensive, although some output indicators will vary depending on where they are measured. The situation for impact indicators differs, where the primary effects are among the end-users for health applications, but among both the producers and users for feedstocks. 7.1 Output indicators for new GM varieties There are three main unique agro-food output indicators that are currently available: field trials, hectares of GM crops, and GM product approvals. Field trial data, which are unique to GM plants, provide a wealth of data on the development of new GM varieties. All target countries compile records for GM field trials, which are re-quired by law to go through an approval process. Field trial records have some of the same disadvantages of patents. Although both provide a measure of investment by research area, not all field trials will lead to commercial crop varieties and the number of field trials for a spe-cific trait is not a direct indicator for the number of new varieties that will reach the market. For example, only 15 field trials were required to develop a virus-resistant variety of papaya, ver-sus hundreds of field trials for herbicide tolerant maize120. Field trials, however, could be a better indicator for the output of commercial GM crop varie-ties than patents are for technical innovations. This is because firms do not conduct field trials of GM varieties until they have first been tested in the laboratory and then in contained (greenhouse) trials. By the time a variety reaches an open field trial, the concept is likely to have been technically proven and the firm is unlikely to pursue field trials without some evi-dence of commercial viability. At the same time, field trials are not direct output indicators, but leading indicators of future commercial GM varieties. This is due to a lag of between five and two years between the first field trials and market approval. Field trial data can also play an important role in estimating future possible impacts from agricultural biotechnology. Field trial data includes several variables, but the main ones of interest here are the plant species, the purpose of the GM trait, the firm or institution that makes the application, and the application date. Since multiple traits can be included in a single trial, a key constructed vari-able is the trial-trait. For instance, a single trial that includes a gene for herbicide tolerance, insect resistance, and male sterility would generate three trial-trait combinations. Most indi-cators for field trials should use trial-traits, rather than counts of each trial. The purpose of the trait is of particular interest to both an analysis of the outputs and impacts of GM varieties. The United States classifies each trait into one of ten categories, although for analysis these can usefully be combined into five main categories. Other countries do not provide pre-assigned categories, but it is relatively easy to develop them121. Table 7.1 pro-vides an example of each of five main categories plus possible sub-categories.

120 Arundel A. (2002): Agro-biotechnology, innovation and employment, Science and Public Policy 29:297-306, 2002. 121 As an example, MERIT has a database with comparable category data for the EU, the US, and Canada.



GM Hectares: Data on the number of hectares planted to GM crops is compiled by the ISAAA122 by crop and by main traits such as herbicide tolerance (HT) and pest resistance (PR). This can be combined with data on total crop hectares for each crop plant to determine the percentage of hectares covered with GM versus non GM crops. This is a main output in-dicator for the use of GM. Data on GM hectares by trait are an essential prerequisite for esti-mating the environmental impacts of GM crops. GM product approvals: Counts of specific plant and animal varieties approved for commer-cial use can be constructed from a range of national data sources, including data from ISAAA. To date, there are almost no approvals outside of crop plants (for example there are almost no approvals for the commercial exploitation of GM fish, forestry species, animals, or insects). However, this type of information could be increasingly useful in the future to assess the number of applications of GM varieties. Table 7.1: Classification system for field trial traits

Main category Sub-categories

Agronomic (AG) Stress resistance, yield, growth, nitrogen fixation

Pest resistance (PR) Insect, fungus, virus, nematode and resistance to other pests

Technical (TT) Male sterility, other

Herbicide tolerance (HT) By type of herbicide

Product quality (PQ) Industrial inputs (enzymes, polymers, chemicals), pharmaceuti-cals, oils, starches and sugars, cellulose and lignin, appearance, environmental (feed quality)

Table 7.2 lists output indicators for agro-food biotechnology. Of note, all indicators are limited to GM varieties. The list of output indicators for field trials is not exhaustive. Almost all indica-tors can also be developed to provide a time series for the United States and the EU. Many other possible permutations are also possible. 7.1.2 Data quality and availability As shown in table 7.2, data availability for GM field trials, GM hectares, and GM product approvals is largely up-to-date, basically complete (C) for all target countries, and of medium to high value. The only exceptions for the completeness of coverage are a lack of data for the earlier years for GM trials in some countries or very recent data on crop hectares in some countries (FAO data on total hectares are only available for 2000). In general, the agro-food output indicators for GM products are of high data quality and many of them are of high value for assessing the consequences of GM biotechnology to the agro-food sectors. 7.1.3 Recommendations for output indicators

In general we recommend using all high value indicators listed in table 7.2. In addition, there are several key missing indicators that could provide valuable additional information about the adoption of each of the four main applications of biotechnology to the agro-food sector. As noted above, there is a near total lack of data on non GM biotechnology use in agro-food applications (apart from some data on embryo transfer-related technologies provided by the International Embryo Transfer Society123. The general consensus is that all seed firms today use non GM biotechnologies to speed up development times, but there are no data on out-puts. The first part of table 7.3 (indicators 1x) summarizes the types of data that are missing.

122 There are criticisms of the accuracy of this data, which is thought to overestimate GM crop hectares in developed countries and underestimate GM use in developing countries. 123 www.iets.org



There is currently very little data available on the outputs of GM or other modern biotechnol-ogies for non-crop plant applications, such as for silviculture, aquaculture, and insects. One explanation is that with the exception of a tree plantation in China, there are no commercial applications of biotechnology for these purposes to date. Indicator 1f in table 7.3 provides a few suggestions for obtaining relevant data on the product pipeline for these applications. Obtaining information on the intentions of current research, products in the pipeline, the num-ber of years from commercialisation, and possible outputs would require surveys of firms in aquaculture (both research and production firms) and silviculture. Some data are available for the uses of reproductive biotechnologies in livestock breeding, such as for cloning (although this technology is probably far from commercial applications in Europe) and for embryo transfer (a traditional biotechnology that can be combined with modern variants)124.

124 Data on the number of embryo transfers in livestock are available from http://www.iets.org/data_retrieval.htm. In 2004, embryo transfer was used in less than 0.5 % of cattle births in the United States.



Table 7.2: Output indicators for agro-food biotechnology/GM

No Description Strengths and limitations Value Latest year

Avail-ability

1 Field trials: General indicators 1a Total number of field trials (trial-traits) by country Basic indicator, but not very informative. M 2005 C 1b Number of field tests in each crop /

total number of field tests Good indicator for measuring research interest/investment in spe-cific crops.

H 2005 C

1c Number of field tests by major trait class / total trials (trial-trait combinations)

Good indicator for measuring research interest/investment in spe-cific traits.

H 2005 C

1d National total field trials (trial-traits) as a per-centage of all trials in target countries

Indicator for national research/investment. An alternative is field trials by domestic firms, regardless of location.

M 2005 C

1e Total field trials in US, EU, etc attributed to do-mestically owned firms / all trials in target coun-tries

Best indicator for national research/investment expertise, since many trials are conducted abroad.

H 2005 C

1f Percentage of all trials for stacked traits, particu-larly over time.

The future lies in stacked traits (more than one trait in a variety). Measures progress to increasing technical capabilities with GM.

H 2005 C

2 Field trials: Product quality (second generation) 2a Number of field trials for PQ second generation

traits / total trial-traits by country Required indicator for analysis of second generation traits. H 2005 C

2b Number of each type of PQ trait / total PQ trial-trait combinations

Provides more detail on the direction of research/investment in product quality traits. Particularly useful for assessing industrial and food processing applications.

H 2005 C

2c Number of total PQ trial-traits / total number of trail-trait field trials

National focus on product quality versus other types of traits. M 2005 C

3 Field trials: Public/private split 3a Number of field tests by public institutions/ total

field tests Involvement of public institutions versus private firms. In some countries, public institutions play a relatively important role, so this indicator is very important for policy.

H 2005 C

3b Number of field tests by public institutions per trial-trait class / total number of trial-traits per class.

Public institutions can play a more important role in developing second generation traits than in first generation traits.

H 2005 C

4 GM Hectares 4a Hectares of GM crops / total hectares of all crops,

by country Global indicator of the agricultural importance of GM, but does not adjust for differences in the economic value of each GM crop.

M 2004 C

4b Number of hectares of specific GM crops (i. e., soybean, cotton, canola, maize, squash, papaya)/ Total hectares of each crop, by country

Crop-specific estimates, more valuable than 1a as part of the first step of estimating impacts.

H 2004 C



Table 7.2 continued

No Description Strengths and limitations Value Latest year

Avail-ability

4c Number of hectares of biotech crop by trait / total hectares of each crop, by country

Type of GM trait, such as herbicide tolerance (HT), pest resistance (PT), valuable for estimates of impacts.

H 2004 C

4d Number of hectares of ‘second generation’ (product quality) GM crops / total hectares of GM crops, by country

Product quality (PQ) traits are the only type of trait that are not ‘process’ innovations. PQ traits are the only type that can create greater value-added in agriculture.

H 2004 C

4e Share of GM product quality hectares for 1) food crops, 2) animal feed, 3) biomass, 4) industrial feed stocks / total hectares for each purpose

Very high value indicator for future developments, especially for non food and animal feed uses, but to date biomass use and in-dustrial feed stock uses are very low.

H 2004 H

4f Calculate, for each country, share of global GM hectares for indicators 4a-4d

Useful for benchmarking country performance on GM, but may not be particularly relevant in terms of economic outcomes.

M 2004 C

5 GM crop products approved for commercial release 5a Number of GM crops species approved. For in-

stance, all GM varieties of maize would count as 1 approved crop species.

Variety of types of GM crops, but relevance also depends on the variety of crops grown in each country, which is difficult to address in a meaningful way.

M 2005 C

5b Number of specific types of traits approved. For example, all crops with tolerance to a specific her-bicide = one approval.

Variety of approved uses of GM, which is a good measure of the value of GM to agriculture.

H 2005 C

5c Total trait-crop approvals. Each trait in each crop species counts as one approval.

Total estimate of all approved uses of GM, but will be dominated by main traits (HT, Bt pest resistance) in multiple crops.

M 2005 C

6 Total GM approvals for commercial release 6a Approvals for all GM species, including animals,

fish, crop plants, forestry species, etc. Variety of approved uses of GM, which is a good measure of the value of GM to agriculture.

M 2005 C

6b Approvals by target species: food crops, feed crops, industrial feedstock crops, biomass crops, animals, fish, forestry, etc.

High value, but almost zero number of approvals outside of food and feed crops

H 2005 C

Sources: * GM Hectares = ISAAA, Clive James 2004125; DG Agriculture provides statistics on the value of agricultural crops in the EU and production tonnage at a good level of disaggregation (maize, potatoes, sugar beets, oilseeds, cereal crops (excluding maize), horticultural crops, other, etc). FAO Statistics available from 2004 FAO Statistical Yearbook (see http://www.fao.org/es/ess/yearbook/vol_1_1/site_en.asp?page=resources ). Sources for field trial data: Co untry Web site EU 25, Iceland, Norway http://biotech.jrc.it/deliberate/gmo.asp Canada http://www.inspection.gc.ca/english/plaveg/bio/triesse.shtml US http://www.isb.vt.edu/cfdocs/fieldtests1.cfm Japan http://www.s.affrc.go.jp/docs/sentan/eguide/edevelp.htm and http://webdomino1.oecd.org/ehs/biotrack.nsf 125 James C. (2004): Global Status of Commercialised Biotech/GM Crops: 2004. ISAAA Briefs No 32, ISAAA, Ithaca, NY, 2004.



The missing data can only be obtained from surveys of seed firms. We cannot realistically expect to match the quality and coverage of the data for GM varieties. Nevertheless, some basic data for the recommended indicators would assist in evaluating the relative importance of GM, non GM biotechnology, and conventional varieties, plus the development of biotech-nology applications outside of crop plants. One shortfall with the available data on hectares planted to specific crops (see table 7.2) is there is not sufficient information on crop use. For example, we know that only a very small percentage of total GM crop hectares are planted for crops for industrial or food processing applications. However, as this use increases over time, we will need to be able to separate crops grown for food, animal feed, industrial, and food processing. ISAAA collects data on traits at the current time, but it is not clear how detailed their data will be if the number of product quality traits expands significantly. Indicator series 2, 3 and 4 in table 7.3 cover the other three main applications of biotechnol-ogy in the agro food sector. There are two potential methods for collecting output data for application-specific output indi-cators for veterinary products. The first is to survey producer firms on the number of products of each type on the market or under development. The second is to survey final users (farmers) on whether or not they use each type of product. In the latter case, the survey can both determine the diffusion of biotech-based veterinary products by the percentage of farmers using them (potentially disaggregated by agricultural product class). The main challenge for a farmer survey is that all biotech products would need to be listed by name. Output indicators for reproduction methods can be obtained from surveys of plant seedling, livestock breeding, and aquaculture firms, etc. Indicators for DNA fingerprinting could similarly be obtained from a survey of firms that produce related diagnostic testing kits126. However, it is difficult to develop output indicators for the diffusion or use of such kits, since there are a large number of potential users in both the private and public sectors (for instance govern-ment agencies could use diagnostic kits to determine if imported grain contains GM varieties). Table 7.3: Key missing data for agro-food outputs

Description Data collection method 1 Developing new varieties 1a Hectares planted to non GM biotechnology (i. e. MAS)

and conventional varieties. Estimate from indicator 2 below.

1b Percentage of seed sales from GM, MAS, and conven-tional varieties.

Survey of seed firms.

1c Relative costs of developing GM, MAS, and conven-tional varieties.


1d Ratio of R&D investments in GM, MAS, and conven-tional varieties.


1e Hectares planted to crops for food, animal feed, indus-trial processing, and food processing.

Survey of seed firms, using sales of seeds for each use.

1f Outputs of commercial varieties based on application of biotechnologies in aquaculture, livestock breeding, silvi-culture, insects, etc.

Survey of product pipeline in aquaculture, silviculture re-search firms and breeders. Evaluation of data kept by product approval agencies in each country. Survey of potential producers – will aquaculture producers adopt GM fish?

126 Diagnostics for the development of new varieties will be subsumed under section 1 of Table 7.3 and would be relevant to both MAS and GM varieties. However, since they are inputs to the development of new varieties, they do not need to be identified separately. Conversely, diagnostics for fingerprinting constitute an end-use.



Table 7.3 continued

Description Data collection method 2 Veterinary products (including feed additives) 2a Number of biotech products on market Survey of veterinary products

firms 2b Sales share from biotech veterinary products / all veteri-

nary products Survey of veterinary products firms

2c Percent farmers using biotech veterinary products (all farmers, plus disaggregated by agricultural product)

Survey of farmers

3 Biotech-based reproduction methods 3a Share of all firms active in reproduction that use biotech-

based methods: can be disaggregated by method Survey of firms.

3b Share of total sales and final production from plant seedlings, livestock, fish fry, etc. using biotech-based reproduction methods: can be disaggregated by class (plants, fish, livestock etc.)

Survey of firms.

4 DNA fingerprinting for safety and traceability 4a Total sales of DNA fingerprinting diagnostic kits Survey of firms. 4b Number of different types of DNA diagnostic kits on the

market / under development Survey of firms.

7.2 Application-specific impact indicators On a long-term global scale, the greatest economic, social and environmental impacts of bio-technology in the agro-food sector are commonly thought to depend on GM varieties, al-though this assumption needs to be tempered by the fact that no data are available for varie-ties developed through non GM biotechnologies. The presumed value of GM varieties is due to the belief that it is the only technology that can simultaneously provide major productivity improvements and reduce environmental impacts. An example would be grain varieties that fix nitrogen, reducing the need for fertilizer, are resistant to most economically destructive pests, and are drought resistant. Veterinary products, including diagnostics, run a close second globally, but will be more important in Europe as long as GM crop varieties are little used. The methodology for assessing the environmental and social impacts of new crop varieties requires reviewing the current literature on comparative trials127 between GM and non GM crops and applying estimates of environmental and social benefits from specific traits to the number of hectares planted to GM crops with the trait. The technique can be extended by estimating benefits using different assumptions of the percentage of total hectares planted with specific crops that could be replaced by GM varieties ("what if" scenario estimates). The study by Brookes and Barfoot128 evaluates existing trials for the effect of HT varieties and in-sect resistant varieties on pesticide use for four crops (soybeans, maize, cotton and rape-seed), the environmental footprint of these four crops, and the impact of GM varieties on car-bon sequestration. The results can be used to develop relevant indicators, such as the per-centage reduction in active ingredients from pesticide use (preferably in toxic equivalents), percentage reduction in total carbon emissions, etc. The first part of table 7.4 (indicators 1 to 7a) summarizes several key indicators for the en-vironmental and social impacts that could be developed from estimating the benefits (or costs) of GM crop use in comparison to conventional crops. All indicators could be developed through a combination of a literature review of existing comparison trials plus public data on pesticide use, time budgets, carbon production, etc. Of note, almost all indicators in Table 7.4 could be replicated for non-GM crop varieties based on modern biotechnology, such as the use of MAS to develop new crop varieties. This would 127 Not to be confused with the field trials, which are entirely different. 128 Brookes G., Barfoot P. (2005): GM Crops: The global economic and environmental impact – the first nine years 1996-2004, AgBioForum 8:187-196, 2005.



require extensive research to identify these varieties, although it is possible that the most economically valuable crop varieties that are not GM are developed through the use of MAS and other biotechnologies. The same limitations apply to all of these indicators. First, the quality of the comparison trials of GM versus non GM crops is highly variable and consequently the estimates of benefits or costs will be influenced by the criteria used to select these trials. To avoid biases, clear selec-tion criteria need to be established in advance. Second, for some indicators by crops, there could be a lack of adequate trials to make a good estimate of the impacts. Third, the compari-son trials rarely distinguish between GM, conventional, and other non GM biotechnology va-rieties. Fourth, almost all results for Europe, where few GM varieties are grown, will be based on extrapolating from experience in other countries. Two versions of each indicator are provided. Version ‘a’ gives the costs or benefits from each specific GM crop-trait. Version ‘b’ calculates total national costs or benefits summed across all GM crops as a percentage of total impacts. Version b is of higher value, but version a must be computed as a first step to estimating version b. The first seven indicators listed in table 7.4 are based on known studies on each of the im-pacts. It is also possible to use the field trial data to develop qualitative estimates of future impacts, such as expectations for future savings in toxic equivalents from pesticide use or carbon production. These estimates will be substantially less reliable because they lack com-parative trials between GM and non GM crops. Indicators 7b to 9 suggest a few application-specific impact measures for veterinary products and DNA fingerprinting. No impact measures are given for reproduction methods129. Two of the veterinary measures (9c and 9d) are replicated from relevant indicators in table 6.3 for health effects. Of note, many indicators of human health effects, such as DALYs, QUALYs and morbidity measures are less relevant in agriculture. However they are of relevance as efforts in food safety and zoonosis have an impact.

129 These are probably too complex for this study and would be based on a scientific review of issues such as a possible decline in genetic diversity (a first choice could be positive indicators such as disease avoidance through the production of disease free animals, impact on rate of genetic improvement, impacts on trade and relevant competitiveness issues, animal and product uniformity (with subsequent impact on value) etc. Generic impact indicators for reproduction methods are covered in chapter 9.



Table 7.4: Impact indicators for agro-food biotechnology

No Description Strengths & limitations Value Latest year

Avail-ability

1 Pesticide use (active ingredients or toxic equivalents) 1a Reduction (increase) in active ingredients from pesticide use per hectare per

crop variety See text M NR NR

1b Reduction (increase) in active ingredients from pesticide use as a per-centage of total pesticide use by country, summed across GM crops

See text H NR NR

2 Carbon dioxide production 2a Reduction (increase) in carbon production from 1) biomass generation and

2) affect of GM crop use on tractor usage for tillage, spraying, etc See text M NR NR

2b Reduction (increase) in carbon production as a percentage of total carbon production per country, summed across GM crops

See text H NR NR

3 Time budget 3a Reduction (increase) in farmer time to attend to fields, for instance from the

use of Bt cotton, per crop See text M NR NR

3b Reduction (increase) in farmer time as a percentage of total farmer time, summed across all crops

See text H NR NR

4 Soil erosion 4a Reduction (increase) in soil erosion, for example from the use of HT crops See text H NR NR 4b Reduction (increase) in total soil erosion, summed across all crops See text M NR NR 5 Use of marginal land 5a Return (increase) of land for non-crop uses (recreation, forestry etc) from

higher yields, per crop See text M NR NR

5b Return (increase) of land for non-crop uses (recreation, forestry etc) as a percentage of all arable land, summed across all GM crops

See text H NR NR

6 Animal wastes 6a Reduction (increase) in pollution from animal wastes, due to improved GM

feed crops See text M NR NR

6b Reduction (increase) in pollution from animal wastes as a percentage of total animal waste by country, due to improved GM feed crops

See text H NR NR



Table 7.4 continued


Avail-ability

7 Change in growers’ gross margins 7a Change in percent of growers’ gross margins, compared to previous or alter-

native technology, from use of GM crops: either average or national aggre-gate

See text H NR NR

7b Change in percent of growers’ gross margins, compared to previous or alter-native technology, from use of biotech veterinary products (vaccines, hor-mones, therapeutics, diagnostics): either average or national aggregate

See text H NR NR

7c Change in final consumer prices from biotech versus non-biotech products Good impact indicator, but must be calculated for each product separately, although it can be aggregated over an application, such as the price savings to consumers from GM crops. To date, these price differentials are negligible, with most cost savings going to pro-ducers and manufacturers.

H NR NR

8 DNA fingerprinting and related diagnostics 8a Share of total stocks by variety (fish, livestock, bivalves, plants) tested using

diagnostics; for instance, the percentage of all cattle that are subject to DNA fingerprinting for genetic tracking, safety, traceability etc.

Measure of diffusion of technology, but could be very difficult to obtain good data

M ND ND

8b Financial value of stocks (fish, livestock, bivalves, plants) for which diagnos-tics are crucial to the well-being of the stock. For instance, DNA finger-printing could only be used in random checks, but the well-being of the stock could be dependent on the availability of the diagnostic tests

Measure of economic potential of tech-nology, but very difficult to define ‘ well-being’

H ND ND

9 Veterinary products 9a Share of total stocks by variety (fish, livestock, bivalves, plants) treated using

veterinary biotech products such as vaccines, therapeutics, diagnostics for diseases, etc.

Good indicator for range of impacts, estimate could be developed from literature-based research

H ND ND

9b Financial value of total stocks by variety (fish, livestock, bivalves, plants) treated using veterinary biotech products such as vaccines, therapeutics, diagnostics for diseases, etc.

Good indicator for financial impacts, estimate could be developed from literature-based research

H ND ND



Table 7.4 continued


Avail-ability

9c Cost-utility effectiveness: relative costs of a biotech-based treatment com-pared to an alternative treatment, or cost-utility when no alternative treat-ments are available

Excellent impact measure, but requires a product by product evaluation

H ? ?

9d Reduction in premature mortality or loss of usable stock rates (stock could be lost to market due to disease without mortality)

High validity as a measure of stock protection value, but requires a product by product evaluation

H ? ?

NR: Not relevant: the relevant literature can span several years. A review of the available literature is beyond the remit of this report. For Europe, an evalua-tion of GM impacts requires estimating impacts based on comparative trials in other countries. Impacts from gross margins from veterinary products can partly be based on European comparative trials and partly on trials in other countries, as with bST. ND: No data – requires survey.



7.2.1 Data quality and availability The data for assessing impact indicators 1 to 7a inclusive will come from combining data on hectares per crop type (discussed in section 7.1.2) and from a literature review of published GM/non GM comparative trials. The same technique can be used for indicator 7b, although based on data for animal populations. A review of data availability and quality of comparative trials is outside the remit of this report. Data for indicators 8 and 9 are also not directly available, but some initial results might be attainable through desk research combined with data on stock values. Otherwise, a survey of firms active in these areas would be required. 7.2.3 Recommendations for impact indicators It is not possible to make recommendations for additional data to be collected on impact indi-cators 1 to 7a because this requires in-depth analysis of comparative trials. However, due to high costs and the availability of impact data from outside Europe, we do not recommend collecting impact data from surveys of firms that develop GM or MAS crop varieties or from users such as farmers. Data for indicators 8 and 9 could be obtained through desk research.

8. Industrial manufacturing, energy, environment: application-specific output and impact indicators

Output indicators In this study the industrial uses of biotechnology will include five applications: the use of bio-technology to produce chemicals, biopolymers, enzymes and biofuels and the use of bioreme-diation to treat air, water, soil and solid waste and the use of biosensors in the bioremediation context. Although there are other uses of biomass for energy production ( such as decentralised biogas units) in this study we will focus on biofuels. The number of indicators varies from eight (chemicals and biofuels) to six (bioremediation). As far as possible, similar indicators are given for each main type of application. These include two indicators that can also serve as input indicators: the number of firms active in each type of biotechnology and the share of biotechnologists out of total R&D staff in each of the five indus-trial applications. These two indicators are included here because the structure of the relevant industry sectors suggests that they are also useful as output measures. For instance, chemi-cals, polymers and enzymes are mature sectors where the output of biotechnology depends very little on the establishment and activities of DBFs (as in pharmaceuticals) but to a great ex-tent on the adoption of biotechnology by established firms. Therefore, the indicator for the adoption of biotechnology by firms in the sector and the share of R&D staff that are biotech-nologists are important indicators for future biotechnology outputs. 8.1.1 Description of phenomena and indicators Chemicals The indicators for biotechnology-related outputs in the chemical sector include leading indica-tors and indicators for the production of bio-chemicals. In total, there are eight different indica-tors, described in table 8.1. The three leading indicators reflect the integration of biotechnology in the industrial structure of a country. They include the share of companies – diversified companies (1a) and new dedicated biotech companies (1b) – that use biotechnology and how this number changes over time. The third indicator (1c) measures the replacement of chemical engineers by biotechnological engi-neers.



The indicators for the production of biochemicals include three economic indicators that provide information about market penetration and market value on the sector level (2a – 2c), and a fourth indicator deals with the unique new types of products that are produced through biotech-nology (2d). Most indicators are of high value, but data availability and quality are low. See annex 3, table A.8.1.1 for more information about these indicators and data availability and quality.



Table 8.1: Output phenomena and indicators for biotechnology in the chemical sector


Last year Data availability

Data quality

1. Leading indicators (partial input indicators) 1a Chemical companies that apply

one or more bioconversion steps in their production processes / total of chemical companies, per year (from 2000 onwards)

Strength: Provides insight in adoption of biotech by the chemical industry Limitations: Indirect relation and dynamics of the sector (Mergers & Acquisitions) can influence numbers

M

2005 L L

1b Dedicated biotech companies specialised in bioconversion, per year (from 2000 onwards)

Strength: Provides insight in adoption by new start-up DBFs active in bioconversion R&D Limitations: Dynamics of the sector (Mergers & Acquisi-tions) can influence numbers

M

2005 L L

1c Share of biotechnologist of total R&D staff in chemical companies

Strength: Measure of integration of biotechnology in (petro)chemical industry at the human resources level Limitations: No direct relation between size of new staff and integration of new technology

H

2001 L L

2. Biochemical production and diffusion 2a Volume of chemicals that are

produced only by biotechnologi-cal production processes / total chemical production, per product group

Strength: Direct measure for integration of biotech in chemical industry Limitations: -

H

2001 L L

2b Volume of chemicals that are produced through production processes in which one or more chemical reaction steps have been replaced by bioconversion steps / total production pro-cesses, per product group

Strength: Direct measure for adoption. Provides infor-mation about speed in replacing traditional chemicals by chemicals produced through bioprocessing Limitations: -

H

2001 L L



Table 8.1 continued


Last year Data availability

Data quality

2c Turn over (partly) bio-produced chemicals / total gross value of chemicals, per product group

Strength: Direct measure for adoption. Provides direct insight in economic value of bio-produced chemicals Limitations: -

M

2001 L L

2d Number of new type of chemicals through bio-production with unique functionality (specialities)

Strength: provides information about new products made through biotech, that can not be produced through chemical procedures Limitations: Definition of new functionality

H

2004 L L



Biopolymers • In this study the term biopolymers includes (Crank et al. 2004)130:

1. to make use of natural polymers which may be modified but remain intact to a large ex-tent

2. to produce bio-based monomers by fermentation which are then polymerised

3. to produce bio-based polymers directly in microorganisms or in genetically modified crops.

• We do not make any distinctions between these three types of production in our indicators. The phenomena for the identification of biotechnology-related output in the production of poly-mers include leading indicators and indicators for the production and diffusion of these poly-mers. The six identified indicators are described in table 8.2. There are two leading indicators. The integration of biotechnology in the industrial sector with respect to the number of established companies producing biopolymers evaluates the industrial structure of the chemical sector (1a). The second indicator (1b) measures the replacement of (petro)chemical scientists and engineers by biotechnological engineers. Two of the four production indicators are economic indicators. The indicator on market penetra-tion (2a) illustrates the extent and ability of the industry to use new biotechnologies, a second indicator (2b) gives insight into the economic value of biopolymers. A technology-related indi-cator deals with new types of biopolymers on the market (2c). One indicator describes the diffu-sion of biopolymers in the production of products that use polymers (2d). It shows the openness of the industry for biotechnology products. Data availability and quality are medium. See annex 3, table A.8.1.2 for more information about these indicators and data availability and quality.

130 Crank, M. M. Patel, F. Marscheider-Weidemann, J. Schleich, B. Hüsing, G. Angerer (2004)Techno-economic Feasibility of Large-scale Production of Bio-based Polymers in Europe (PRO-BIP) Draft Final Report May 2004, Prepared for IPTS



Table 8.2: Output phenomena and indicators for biopolymers

No Description of indicators

Strengths & limitations Value Last year Data availability

Data quality

1. Leading indicators (partial input indicators) 1a Share of biopolymer pro-

ducing companies in total number of companies pro-ducing polymers

Strength: Provides insight in adoption by established polymer producing firms. Limitations: Dynamics in the sector (Mergers & Acquisi-tions) can influence numbers throughout the years

H 2004 L L

1b Share of biotechnologist of total R&D staff in biopoly-mers producing companies, per year

Strength: Direct measure of integration of biotechnology in (petro)chemical industry, at the human resources level. Limitations: No direct relation between size of new staff and integration of new technology

M 2000 L L

2. Biopolymer production and diffusion 2a Share of biopolymers in total

polymer production volume, per year

Strength: Direct measure for adoption. Gives information about how fast polymers are replaced by biopolymers Limitations:

H 2002 M M

2b Share of gross value added of biopolymers in total gross value added of polymers

Strength: Direct measure for adoption. Provides direct insight in economic value of biopolymers Limitations: Only gross value added data available by sector, requiring educated guesses.

M 2001 L L

2c Share of different types of biopolymers in total number of polymers on the market

Strengths: Provides information adoption of biopolymers in total polymer market Limitations: Indirect measure. Difficulties with defining polymers. Can probably better be addressed in a qualita-tive way

L 2004 M M

2d Share of products using biopolymers in total number of products using polymers

Indirect measure L - - -



Enzymes in downstream sectors The adoption of biotechnology is the main phenomenon to be studied in the section dealing with the use of enzymes in a number of downstream sectors. The main downstream sectors that will be included are: food and feed, textile and leather, pulp and paper. Enzymes are also used in other sectors such as mining. The phenomena for the identification of biotechnology-related output in the production of en-zymes include two leading indicators and five indicators for the production and diffusion of bio-enzymes. The seven identified indicators are described in table 8.3. The two leading indicators include the number of companies in each sector that use biocataly-sis, as part of the total size of the sector (1a) and a research staff-related indicator that measures the replacement of chemical engineers by biotechnology-related R&D staff in compa-nies in these sectors (1b). The production and diffusion indicators include economic indicators that provide information about the volumes of enzymes used (2a), the production volume of products resulting from bio-catalysed production in these sectors (2b), the number of new types of products from the use of enzymes in downstream sectors (2c); the gross value of products produced by biocatalysed processes in each sector (2d) and the cost reductions of using biocatalysed processes in each sector (2e). A technology-oriented indicator measures the number of different types of biocata-lysed processes used in each sector, per year of introduction (2f). Three indicators are of high value as they directly measure the adoption and the economic value it has for these down stream sectors. The other indicators are of medium value, mostly because they indirectly measure the adoption of biotechnology. The main problem is the lack of available data and of low quality following the criteria that are used in this study. See annex 3, table A.8.1.3 for more information about these indicators and data availability and quality.



Table 8.3: Output phenomena and indicators for enzymes in downstream sectors

No. Description of indicator Strengths & Limitations Value Last year available

Data avail-ability

Data quality

1. Leading indicators (partial input indicators) 1a Share of companies that apply biocatalysis

processes in total number of companies, for each sector, per year

Strength: Provides insight in adoption by estab-lished industry Limitations: see 2b

M 2001 L L

1b Share of biotechnologists out of total R&D staff in companies in each sector, per year

Strength: measures integration of biotechnology in downstream industries, at the human resources level Limitations: No direct relation between size of new staff and integration of new technology

M 2001 L l

2. Enzyme production and diffusion 2a Volume of enzymes used in each sector (food

and feed, textile and leather, pulp and paper, mining and others) in years

Strength: direct measure of integration of biotech-nology in downstream processes Limitations: -

H 2001 L L

2b Share of production volume of products re-sulting from biocatalysed production pro-cesses in each sector in total production volume of products resulting from catalyse processes in each sector

Strength: measure of biotech-related production Limitations: Indirect measure for adoption, as bio-catalysed part of production can be a minor part of the production process

H 2001 L L

2c Number of new types of products from the use of enzymes/microorganisms in downstream processes (e. g. new food products);

Strength: Direct measure of contribution of bio-technology to product innovations in downstream sectors

H 2005 L L

2d Share of gross value of products produced by biocatalysis in total gross value of products produced by catalysis, by sector

Strength: see 2b Limitations: see 2b

H 2001 L L



Table 8.3 continued

No. Description of indicator Strengths & Limitations Value Last year available

Data avail-ability

Data quality

2e Cost reductions of using biocatalysed pro-cesses instead of non-bio catalysed pro-cesses in each sector

Strength: Direct measure for adoption. Cost re-duction is very important driver for companies to adopt technology Limitations: complicated indicator as it addresses very much factors

H 2004 L L

2f Number of different types of biocatalysed processes used in each sector, per year of introduction

Strength: provides an overview of how biocatalysis penetrates the production processes of down-stream sectors Limitations: it is an indirect measure of adoption

L 2004 L L



Biofuels The phenomena for the identification of biotechnology-related output in the production of liquid fuels (biofuels) include three leading indicators and five indicators for the production and diffu-sion of biofuels. The eight identified indicators are described in table 8.4. The three leading indicators includes one indicators that reflect the integration of biotechnology in the (petro)chemical industry: the number of established companies producing biofuels and the change in this number over time (1a) and the number of new specialised fluid biofuel pro-ducing companies and the change in this number over time (1b). The third indicator measures the replacement of (petro)chemical scientists and engineers by biotechnological engineers (1c). The production and diffusion of biofuels is covered by indicators for the extent and ability of the (petro)chemical industry to use new biotechnologies for the production of liquid fuels. This is illustrated by the indicators that provide information about market penetration (2a) and eco-nomic value (2b). The diffusion of biofuels in transportation and industrial manufacturing is measured by two indicators describing the share of biofuels in the sales of fuels to industry and through retailers (for transportation) (2c and 2d). The technology indicator deals with new types of biofuels on the market (2e). Data availability and quality differ for each indicator. See annex 3, table A.8.1.4 for more infor-mation about these indicators and data availability and quality.



Table 8.4: Output phenomena and indicators for biofuels

No Description of indicator Strengths & limitations Value Last year available

Data avail-ability

Data quality

1. Leading indicators (partial input indicators) 1a Number of established companies

producing biofuels, by year (from 2000)

Strength: Direct measure for adoption by chemical industry. Provides insight in adop-tion by ‘established’ fuel producing firms Limitations: Unclear definition of fuel pro-ducing companies as the petrochemical in-dustry is active in a long chain from well to wheel. Dynamics of the sector (Mergers & Acquisition) can influence numbers through-out the years

H 2001 M M

1b Number of new specialised fluid bio-fuel producing companies, by year (from 2000)

Strength: Direct measure for adoption. Pro-vides insight in the adoption through new start-ups that are specialised in producing biofuels Limitations: see 1a

H ? L L

1c Share of biotechnologist of total R&D staff in chemical companies

Strength: Direct measure for integration of biotechnology in (petro)chemical industry at the human resources level Limitation: no direct relation between size of new staff and integration of new technology

M 2000 L L

2. Biofuel production and diffusion 2a

Share of fluid biofuels in total produc-tion volume of fuels, per year

Strengths: Direct measure for adoption. Gives information about how fast fossil fuels are replaced by biofuels Limitations: -

H 2003 C H

2b Share of gross value added of fluid biofuels in total gross value added of all fuels

Strength: Direct measure for adoption, pro-vides direct insight in economic value of bio-fuels Limitations: Gross value added figures are available only on sectoral level. This means that educated guesses have to be made.

M 2001 L L



Table 8.4 continued

No Description of indicator Strengths & limitations Value Last year available

Data avail-ability

Data quality

2c Share of sales (volume) of fluid bio-fuels through retailers in total sales of fluid fuels through retailers

Strength: Direct measure for diffusion Limitation:-

M 2003 C H

2d Share of volume of fluid biofuels sold to and used by industry in total sales to and use of fluid fuels by industry

Strength: Direct measure for diffusion Limitation:-

M 2003 C H

2e Share of number of fluid biofuel pro-ducts in total number of fuel products

Strength: provides insight in penetration of biotech in fuels industry Limitations: Indirect measure.

L - L L



Bioremediation Bioremediation concerns end of pipe technologies used for the treatment of air and effluent gases, water, soil and solid waste and includes also biosensors for detection and monitoring of organic and other compounds. The indicators for the identification of biotechnology remediation include three leading indicators and three indicators for the production and diffusion of bioreme-diation. The six identified indicators are described in table 8.4. The first of the three leading indicators deals with the industrial structure; the number of compa-nies in each sector that produce bioremediation services and equipment, as part of the total size of the sector (1a) and the number of companies that produce biosensors for bioremediation applications (1b). The third indicator measures biotechnology-related R&D staff in companies in these sectors (1c). The economic indicators provide information about the size of the biotechnology-related eco-nomic performance in this sector: investments and expenditures by public sector organisations and companies when buying the services and products of the bioremediation industry (including biosensors), as part of total volumes of end-of-pie expenditures (2a). A second economic indi-cator relates to the cost-reductions by using biotechnological processes instead of chemical or physicals procedures (2b). A technical indicator measures the share of different types of biore-mediation processes used in each subsector (2c). Three indicators are of high value as they directly measure the adoption and the economic value of bioremediation compared to non-bio-based end-of-pipe technologies. The other indi-cators are of medium value, mostly because they indirectly measure the adoption of biotechnol-ogy. The main data problem is the lack of available data and the low quality following the criteria that are used in this study. See annex 3, table A.8.1.5 for more information about these indicators and data availability and quality.



Table 8.5: Output phenomena and indicators for bioremediation

No. Description of indicator Strengths & limitations Value Last year Data availability

Data quality

1. Leading indicators (partial input indicators) 1a Share of bioremediation

companies in total number of end-of-pipe technologies producing companies, per waste stream.

Strength: Direct measure of adoption Limitations: Dynamics of the sector (Mergers & Acquisi-tions) can influence numbers.

M - L L

1b Number of companies pro-ducing biosensors for biore-mediation applications

Strength: Direct measure of adoption Limitations: Dynamics of the sector (Mergers & Acquisi-tions) can influence numbers

M 2005 L L

1c Share of biotechnologists of total R&D staff in companies in environmental sector

Strength: Direct measure of integration of biotechnology in environmental sector, at the human resources level Limitations: -

M 2003 L L

2. Bioremediation application and diffusion 2a Share of expenditures and

investments on biotechno-logical end-of-pipe applica-tions in total sales volume of end-of-pipe applications, per waste stream, per year

Strength: Direct measure of adoption Limitations: definition of ‘biotech-related end-of-pipe applications’

H 2002 L L

2b Cost reductions by bioreme-diation techniques relative to non-biological treatment, per waste stream

Strength: Direct measure for adoption. Cost reduction is very important driver for companies to adopt technologyLimitations: complicated indicator as it addresses many factors

H 1994 L L

2c New biological treatment techniques/tools with unique features, per waste stream

Strength: direct adoption of biotechnology in terms of new products and services Limitations: -

H 2005 L L



8.1.2 Assessment of data quality and data availability Chemicals (excluding biopolymers) Table 8.1 shows an overall low data availability and data quality of data for the selected indica-tors. Availability and quality of data (turnover, value added, employment) for all products of chemical sector (denominator) is high, for EU25, USA and Japan. Data on the biotechnology part (nominator) is of low availability and quality. The EUROSTAT database does not specify for biotech products. So estimates of EUROSTAT figures have to be made and together with addi-tional information from a number of different sources including the companies themselves data on production volumes/sales/turnover/value added/ labour productivity of biotech-related part can be estimated. Both scores combined result in the evaluation of the data availability as low (L). Some of these data could be available in reports produced by consultancy firms, such as Frost and Sullivan (2003)131 Availability and quality of data concerning cost and benefits of using biotechnological pro-cesses, instead of chemical processes is rather patchy and only available on a case-to-case basis for a number of specific chemicals. Country availability seems less relevant for this indi-cator. Data about numbers of chemical companies (denominator) are highly available and of high quality. Only very few national governments, private and academic organisations have collected data about numbers of dedicated biotech firms in industrial biotechnology and of diversified companies active in this field (nominator). Data on biotech R&D staff in chemical companies is not available; only employment figures for total and for tertiary education level for total chemical sector. A complete overview of new types of chemicals with unique features that can only be produced by biotechnology is not available. Data is scattered about several publications, including the DECHEMA report ‘Weiße Biotechnologie: Chance für Deutschland’. More details about data quality, availability and sources are presented in annex 3, tables A.8.1.1 and A.8.3.1. Biopolymers Table 8.2 shows a low data availability and data quality for most indicators. The study on bio-polymers by Crank et al (2004)132 provides details on production volumes of biopolymers and polymers in EU15 and accession countries. However, these figures are based on combining production volumes of the major (bio)polymer producing companies; hence, this is not neces-sarily the total production volume of (bio)polymers. Statistics by EUROSTAT and national statis-tics bureaus do have data on the number of companies in the chemical industry, distinguished by industry categories, such as the NACE structure. However, these structures do not include a dedicated category for companies producing polymers or biopolymers. The same is true for data on gross added value and the number of (R&D) employees. The study by Crank et al (2004) provides more details on different product groups in biopolymers and the biopolymers produced by the companies included in the study. This study also describes the main categories in polymers, but more detailed information on all different polymers is not available. No data are available about the diffusion of biopolymers in products using polymers. Tables A.8.1.2 and A.8.3.2 in annex 3 provide more detailed information about the data quality, data availability and key sources. 131 Frost and Sullivan (2003) Advances in Biotechnology for Chemical Manufacture - Part 1 and 2 (Technical Insights), www. technical-insight.frost.com 132 Crank, M. M. Patel, F. Marscheider-Weidemann, J. Schleich, B. Hüsing, G. Angerer (2004)Techno-economic Feasibility of Large-scale Production of Bio-based Polymers in Europe (PRO-BIP) Draft Final Report May 2004, Prepared for IPTS



Enzymes in down stream sectors The data availability and quality of most data concerning the use of enzymes in down stream sectors is low (see table 8.3). Although denominator data sets for numbers of companies in each down stream sector are highly available and of high quality (EUROSTAT), this is not the case for the nominators. A number of sources provide estimations of volumes of enzymes used in down stream sectors. Data concerning biotechnology-related production volumes and value added are not available. A number of case studies provide patchy information about cost reduc-tions of using biocatalysis in down stream sectors. Numbers of companies that apply biocataly-sis processes or of biotech-related R&D staff are not available. For more details see tables A.8.1.3 and A.8.3.3 in annex 3. Biofuels Table 8.4 shows that data availability and quality differ among indicators. The International En-ergy Agency provides very detailed statistics about energy production and consumption in all its member countries, OECD countries, non-OECD countries, as well as EU 15 and EU 25. ‘Re-newables’ is a main category, further de-tailed for different types of renewables, including liquid biofuels. This results in a complete data availability and very high data quality for the indicators on market penetration and diffusion of fluid biofuels. As it was already shown for the for the other industrial biotechnology categories, the data availability and data quality for the indicators concerning the number of specialised biofuels producing companies, gross value added and the research community are low. EUROSTAT provides data on companies, gross value added and the research community related to the production of refined petrochemical products including fuels, but there are no data available for biofuels. In the US, the Economic Research Service of USDA tracks ethanol and biodiesel industries in the US and data concern plants operating, pro-duction capacity, value-added, feedstock used, the use in major fleets (e. g. US Postal Service) and the production costs, based on cost of production survey in 1998. Hence, this study pro-vides more details on biofuels in the US. We have not found any data for the indicator on the number of biofuel products on the market. Tables A.8.1.4 and A.8.3.4 in annex 3 give more in-formation on data availability, data quality and key sources. Bioremediation The data availability and quality for the bioremediation sector is also rather poor. After the publi-cation of the OECD report ‘Biotechnology for a clean environment. Prevention, Detection and Remediation’ in 1994133, no up-dates have been published that provide such an extensive over-view of this sector. However, it should be mentioned that this end-of-pipe sector is - from a value-added perspective - less interesting, as the value added is rather low. This is reflected in the number of studies in this field. Relevant information for the biotechnology-related activities in this sector – investment/expenditures, cost reductions, number of specialised companies or employees – are not available in terms of statistical data overviews, but can be found in publi-cations and reports. Data availability and quality concerning expenditures and investments in environmental protection in general is high. Detailed information about data and sources is given in tables A.8.1.5 and A.8.3.5 in annex 3. 8.1.3 Recommendations for sector studies Data availability for indicators of industrial biotechnology applications is poor. Therefore only a limited number of indicators is recommended. These indicators were selected in order to gather, in a methodologically sound way, additional data that provide a first quantitative indication of the economic importance of biotechnology for these subsectors. As we assumed that already these few indicators would already imply a huge amount of work, we restricted ourselves in the num-ber of recommended indicators. In order to get a comprehensive and representative picture ex- 133 OECD (1994): Biotechnology for a Clean Environment. Prevention, Detection, Remediation, Paris.



tensive (quantitative) data collection together with in-depth case studies have to be made. Thus currently there exists a trade off between a realistic feasibility of data collection and the avail-ability of sources. The next steps to be taken in a middle to long-term perspective have to over-come the shortcomings of the OECD report. Chemicals (excluding biopolymers) The main indicators proposed for the sector study deal with market penetration of biotechnology in the chemical sector as they can provide insight in the relevance of biotechnology for the chemical sector. It is proposed to make an overview on the basis of the available sources, com-bined with expert consultations through Delphi methods and interviews. A description of the new types of chemicals with unique functionality (specialities) and their present and future markets emphasizes that biotechnology is not only a substitute for another type of technology, but that through biotechnology completely new types of products and new markets can be developed. A fifth and final indicator proposed for the sector study relates to the barrier concerning attracting bioengineers in a ‘chemical organisation’. Table 8.6: Recommended output indicators for biotechnology in the chemical sector

No. Key indicator Method for data collection 2a Volumes of chemicals that are

traditionally produced only by bio-technological production processes in total production volume of chemicals, per product group

1. Use Frost and Sullivan (2003) 2. Use available, but patchy data about production volumes, market volumes, etc in public domain and make on the basis of this a first overview. Consult through Delphi method number of experts and make on the basis of their input final overview

2b Volumes of chemicals that are pro-duced through production pro-cesses in which one or more chemical reaction steps have been replaced by bioconversion steps in the total production volumes of chemical produced, per product group, per year

1. Use Frost and Sullivan (2003) 2. Use available, but patchy data about production volumes, market volumes, etc in public domain and make on the basis of this a first overview. Consult through Delphi method number of experts and make on the basis of their input final overview

2d Cost reductions by using bioconver-sion processes in total cost reduc-tions in chemical industry, per pro-duct group

This is a very relevant indicator, especially as the data provided can convince industry to invest in biotech. That is why industry organisations such as EuropaBio and BIO, but also OECD have invested many efforts in ga-thering these data. On the other hand, industry is also the most important source for these data and data avail-ability is, because of this, a problem. A first method to utilise the available information now already collected in the OECD and other case studies, is to extrapolate the case-study findings to the sector as a whole. On the basis of data gathered under 1b, expert interviews can be used to make these extrapolations.

2e Number of new type of chemicals through bio-production with unique functionality (specialities) and their markets

Description of new type of products through biotech-nology on the basis of a combination of desk study using scientific literature and other documents and interviews with scientists and experts in the field

1c Replacement of chemical by bio-technological engineers

On the basis of expert estimates and interviews with companies the quantitative penetration of biotechnology at the human resources level and the main stimuli and barriers that influence this can be investigated



Biopolymers The main indicators proposed deal with market penetration of biotechnology in the production of polymers in the chemical industry. These indicators provide insight in the extent and ability of the industry to use new (bio)technologies and thus give an indication of the future competitive-ness of the industry. It is proposed to use the available data from the Crank et al (2004)134 study and to combine it with expert consultations through Delphi methods and interviews. Experts in (bio)polymers have a good overview of the market and can help to provide estimates for all countries. Surveys to biotechnology companies are carried out in many countries and can help to identify the number of companies using biotechnology in their production activities. However, mainly broad categories are used (e. g. active in human health, diagnostics, therapies, agro-food, feed, white biotechnology etc) and no further distinction is made to specific type of pro-ducts or services developed (such as ‘biopolymers’). Another option for collecting data on poly-mers and biopolymers could be contacting national and international industry organisations re-presenting the polymer industry. Table 8.7: Recommended output indicators for biopolymers

No. Key indicator Method for data collection

2a Share of production volume of bio-polymers in total polymer production volume

Use available data about production volumes in EU 15 + accession countries

Consult through Delphi method number of experts and make on the basis of their input a final overview

1a Share of biopolymer producing com-panies in total number of companies producing polymers

Use available data about major biopolymer pro-ducing companies in EU 15 + accession countries

Consult through Delphi method number of experts and make on the basis of their input a final overview

Contact industry organisations representing companies in polymers

Enzymes in down stream sectors For the sector study dealing with the use of enzymes in down stream sectors we recommend one indicator: the volumes of enzymes used in this sectors. Table 8.8: Recommended output indicators for enzymes in downstream sectors


2a Volume of enzymes used in each sector (food and feed, textile and leather, pulp and paper, mining and others) in years

A combination of different sources, including sales figures from the main enzyme producers (such as the website of Novozyme which pro-vides a nice overview of key figures and main competitors for each business area) can provide estimations of volumes of enzymes used in down stream sectors. Commercial consultancy reports may also be available that can provide this in-formation.

134 Crank, M. M. Patel, F. Marscheider-Weidemann, J. Schleich, B. Hüsing, G. Angerer (2004)Techno-economic Feasibility of Large-scale Production of Bio-based Polymers in Europe (PRO-BIP) Draft Final Report May 2004, Prepared for IPTS



Biofuels Three indicators are recommended for the biofuels sector study: the share of fluid biofuels in total production volume of fluid fuels, the share of number of fluid biofuels producing companies in total number of companies producing fluid fuels and change over years and the number of specialised fluid biofuel producing companies, over the years. The International Energy Agency provides very detailed statistics of the production and use of liquid biofuels in the world. All countries in this study are covered. The public statistics include data on the year 2003, but more statistics are available through prescription or on pay-per-view basis. Data for the indicators on the number of biofuels producing companies (new, specialised companies and established pe-trochemical companies) are more difficult to collect. EUROSTAT and national statistics can be used to collect data on the number of companies producing liquid fuels, but these sources do not provide data on companies producing biofuels. Only the USDA study gives details about bioethanol and biodiesel production plants in the US. In order to be able to come to a complete overview of the number of new and established companies producing biofuels Delphi studies with experts on biofuels could help to make a final overview. In addition, industry organisations representing petrochemical companies and companies active in renewables could help to collect this information. Table 8.9: Recommended output indicators for biofuels


2a Share of fluid biofuels in total pro-duction volume of fluid fuels, per year

Use the statistics of the International Energy Agency: they provide complete coverage of all countries and have dedicated figures for liquid biofuels.

1a Number of established companies producing biofuels, per year (from 2000)

Use EUROSTAT and national statistics for collecting data on fuel producing companies Use data from the USDA study on biofuels Consult through Delphi method number of ex-perts and make on the basis of their input a final overview Contact industry organisations representing companies in the petrochemical industry and companies active in renewables

1b Number of specialised fluid biofuel producing companies in time, over the years

Use data from the USDA study on biofuels Consult through Delphi method number of ex-perts and make on the basis of their input a final overview Contact industry organisations representing companies in the petrochemical industry and companies active in renewables

Bioremediation It is recommended to use three indicators for the sector study on bioremediation. For the indi-cator on the share of expenditures and investments in end-of-pipe technologies a rich data sources is available in EUROSTAT. It is proposed to use expert-based methods for the estima-tion of the biotech-related part in these. This estimation process can be fed by what is available already in the public domain, brought together through desk research.



Table 8.10: Recommended output indicators for bioremediation


2a Share of expenditures and invest-ments on biotechnological end-of-pipe applications in total sales volume of end-of-pipe applications, per waste stream, per year

Desk research to provide an overview about biotech-related expenditures and investments on the basis of publications (articles, reports, grey literature)

Experts in the field, make based on total figures (EUROSTAT) and overview estimates of biotech-related part

2b Cost reductions by bioremediation techniques relative to non-biological treatment, per waste stream

Data are available on a case-by-case basis. A method is proposed by which through desk study these data are collected and then extrapolated for each of the sectors. Through a Delphi approach experts will be asked to make the ex-trapolations from the desk study data to the whole sector.

2c New biological treatment tech-niques/tools with unique features, per waste stream

Produce description of new techniques with unique features on the basis of a combination of desk study using scientific literature and other documents and interviews with scientists and experts in the field

8.2 Impact indicators Chemicals (excluding biopolymers) The most all-inclusive – some say ‘holistic’ – method to address the contribution of biotechnol-ogy to the chemical industry is to do a so-called footprint analysis in which the impact of a pro-duction process on a number of highly relevant indicators is measured (1a). A Life Cycle Analy-sis (LCA) is an analysis of the same type. As this is the most important indicator that measures the overall contribution of biotechnology to a more sustainable environment, we have also in-cluded the most relevant parts of this footprint for industrial productions and included savings of energy, greenhouse gas emissions, toxic waste and water as four additional indicators (1b-1e). The bio-based economy implies that fossil fuels as raw materials will be replaced by biomass. For that reason land-use is a relevant impact indicator for all applications of biotechnology in the industrial sector; also some NGOs have addressed this issue (2a). Finally, also the impact of increased biomass production on employment in the agricultural sector is included (3a). The footprint (but also the LCA) analyses are not made on a regular basis. Some companies and some academic environmental study centres perform them but do not include all elements that constitute a footprint analysis. In the OECD (2001)135 and BIO (2004)136 case studies, information is available for a number of specific products. For the sector study we recommend to make a draft overview of the potential contribution of biotechnology to the reduction of the use of energy (1b) and to the production green house gas emissions (1c), on the basis of these data and additional data which is available in the public

135 OECD (2001): The Application of Biotechnology to Industrial Sustainability, Paris. 136 BIO (2004): New Biotech Tools for a Cleaner Environment. Industrial Biotechnology for pollution prevention, Resource Conservation and Cost Reduction, Biotechnology Industry Organization, USA.



domain. This overview will be presented and discussed with experts in the field in a workshop and on the basis of the results of the workshop a final overview can be made. Nevertheless, we also recommend that a footprint/LCA analyse of the use of biotechnology in the chemical sector for a number of relevant products is made. This is an all-elements covering study; the choice for the two indicator energy and CO2 reduction are a pragmatic proposal. In analogy to the land-use study for biopolymers of Crank et al (2004), it is proposed to do this for the biotech-related part of the whole chemical sector. Information about data availability is summarised in table A.8.2.1 in annex 3.



Table 8.11: Impact phenomena and indicators for biotechnology in the chemical sector

No Phenomenon Description of indicator

Value Strengths Limitations

Data avail-ability

Data quality

1 Contribution of (partly) bio-produced chemicals to a more sustainable environment

1a Ecological footprint137 of chemicals (partly) produced by bioprocesses compared to non-bio produced chemicals, per product group

Value: H Strength: Indicator measuring the overall sustain-able impact of bio-based production processes, compared to chemical processes Limitations: it is a very extensive exercise, for which a lot of data from companies have to be used. They are not always willing to provide these data

L L

1b Energy savings by (partly) bio-produced chemicals: share of energy used for – partly - bio-produced chemicals in energy used for 100 % non-bio chemi-cal pendant, per product group

Value: H Strength: Although this indicator is already include in the Footprint-indicator, the decrease of energy use (esp. fossil-based) is a highly relevant political issue Limitations: see 1a

L L

1c Greenhouse gas emissions savings by bio-produced chemicals compared to their petrochemical counter-parts

Value: H Strength: This is an important political argument for the introduction of bio-based processes in the chemical industry Limitations: see 1a

L L

137 Environmental footprint is a measure of the burden or impact that a product, operation or corporation places on the environment. Using Life Cycle Assessment (LCA) methodology, one can compute a holistic environmental foot print of a product or a production process. Some key metrics that need to be measured in this process are: Green House gas emissions (COX, VOCs), Energy Consumption, Total waste production (mitigated by reduction in use, and recycling and composting), Toxic waste generation, Regulated Air pollutants release – air emissions (SOCs/NOX, particulates) and Water discharges (http://www.abe.iastate.edu/bio-based/LCAFootprint.pdf, accessed 9 December 2005) In OECD (2001) a Green Index for industrial biotechnology is presented. It is a checklist for the sustainability of biotechnological processes. It covers the categories: energy, raw materials, waste, products and by-products, process and safety



Table 8.11 continued



Data avail-ability

Data quality

1d Savings of toxic/aggressive chemicals by (partly) bio-produced chemicals, per product group

Value: M Strength: This is one of the arguments for the in-troduction of bio-based processes in the chemical industry, supported by new EU polices (REACH) Limitations: see 1a

L L

1e Savings of water by (partly) bio-produced chemicals, per product group

Value: L Strength: This is one of the arguments for the in-troduction of bio-based processes in the chemical industry Limitations: see 1a

L L

2 Land use by biomass feedstock for chemical industry

2a Share of land use for chemical industry in total land use

Value: H Strength: This argument is brought forward by some NGO’s, mainly related to biomass for bio-fuels, but also relevant for chemicals Limitations: -

L L

3 Additional employment in agricultural sector

3a Change in employment related to production of bio-mass used for chemical industry relative to the change in employment in the total agricultural sector

Value: M Strength: Employment gain/loss through biotech is a highly relevant political issues Limitations: -

l L



Biopolymers As for the chemical industry section, the most all-including method to address the ‘sustainable’ impact of the production of biopolymers is by a footprint analysis in which the impact of a pro-duction process on a number of highly relevant indicators is measured (1a). Two main parts of these ecological footprint are the greenhouse gas emissions and the use of energy and these are included in table 8.12 as well (1b – 1e). Biopolymers can be produced by using biomass as a feedstock. Land use is a relevant impact indicator as some NGO’s raise the issue of replacement of food crops by fuel crops in order to keep the economies running: what is the effect for food supplies, especially for developing countries? (2a). Increased biomass production could also impact the employment in the agri-cultural sector (3a). Footprint analyses are not made on a regular basis and only some companies and academic environmental study centres have prepared some kind of footprint studies. Most cases do not include all elements that constitute a footprint analysis. The Crank et al (2004) study provides data on greenhouse gas emissions, energy use savings by biopolymers and land use in relation to their petrochemical counterparts in EU 15. In the BIO (2004) study, for some (bio)polymer products these data are provided for the US. For more detailed information on data availability see table A.8.2.2 in annex 3. For the sector study we recommend to organise a workshop with experts in the field to discuss the potential contribution of biotechnology in the production of polymers to the reduction of the use of energy, greenhouse gas emissions and land use. Based on the data already available in the Crank et al (2004) study and the BIO (2004) study a complete overview can be made, that can be discussed and updated in the workshop. In addition, we propose to make a number of ecological footprint analyses of the use of biotechnology in the production of polymers for a number of relevant biopolymers.



Table 8.12: Impact phenomena and indicators for biopolymers


Strength & limitations Value Data avail-ability

Data quality

1 Contribution of bio-based polymers to a more sustain-able environ-ment

1a Ecological foot-print of biopoly-mers compared to the ecological footprint of polymers

Strength: Indicator measuring the overall sustain-able impact of biopolymers, compared to non-bio-polymers Limitations: it is a very elaborated study, for which a lot of data from companies have to be used and they are not always willing to provide these data

H L L

1b Greenhouse gas emissions savings by bio-polymers rela-tive to their pe-trochemical counterparts

Strength: Although this indicator is already included in the footprint indicator, the decrease of green-house gas emissions is a highly relevant political issue Limitations: -

H M M

1c Energy savings by biopolymers relative to their petrochemical counterparts

Strengths: Although this indicator is already in-cluded in the footprint indicator, energy savings is a highly relevant political issue Limitations: -

H M M

2 Land use by biopolymers

2a Share of land use by biopoly-mers in total land use

Strength: Land use is an issue in the political de-bate on biopolymers: some parties expect that relatively large area have to be used for biomass Limitations:-

H M M





Strength & limitations Value Data avail-ability

Data quality


3a Change in em-ployment related to production of biomass used for biopolymers relative to the change in em-ployment in the total agricultural sector

Strength: Employment gain/loss through biotech-nology is a highly relevant political issue Limitations: limited visibility of where which bio-mass stream comes from and goes to

M L L



Enzymes in downstream sectors (food and feed, textile and leather, pulp and paper, mining and others) Similar to the sections above it is also proposed for enzymes in downstream sectors to use the footprint indicator and a number of specific impact indicators that deal with savings of energy use, of greenhouse gas emissions, toxic/ chemicals and water use (1a – 1d). A second type of phenomena deals with the public acceptance of products that have been produced with the help of GM produced enzymes (2a). The availability of data is again very poor, except for the data concerning public acceptance of GM foods (see table 8.13 and table A.8.2.3 in annex 3). Data for the sustainable impact analy-sis is found scattered in case studies. Similar as for the chemical sector we recommend the footprint study and as a pragmatic alternative the two main environmental issues: savings of energy and of greenhouse gas emissions. Also here we propose to first make a draft overview of the potential contribution of the biotechnology in the downstream sectors to reduction of the use of energy and to the production of green house gas emissions, on the basis of the data in case studies and additional data which is available in the public domain. This overview can be presented and discussed with experts in the field in a workshop and on the basis of that a final overview can be made.



Table 8.13: Impact phenomena and indicators for enzymes in the downstream sectors



Data availability

Data quality

1 Contribution of (partly) bio-produced products of these sectors to a more sustainable environment 1a Ecological footprint of products (partly) produced by

enzyme compared to total chemically produced products in these sectors

Value: H Strength: Indicator measuring the overall sus-tainable impact of the use of enzymes in these sectors. If applicable: compared to non-biocata-lysed production processes in these sectors Limitations: it is very elaborated study, for which a lot of data from companies have to be used. They are not always willing to provide these data

L L

1b Energy savings by using enzymes in production: share of energy used for (partly) enzyme-based products in these sectors in total energy used by 100 % chemical pendants

Value: H Strength: Although this indicator is already in-clude in the Footprint-indicator, the decrease of energy use (esp. fossil based) is a highly relevant political issue Limitations: see 1a

L L

1c Greenhouse gas emissions savings by using en-zymes in production

Value: H Strength: Although this indicator is already in-clude in the Footprint-indicator, the decrease of emission of greenhouse gas is a highly relevant political issue Limitations: see 1a

L L

1d Savings of toxic/aggressive chemicals by (partly) enzyme-based products in these sectors

Value: M Strength: Value: H Strength: This is a relevant political argument for the introduction of enzymes in down stream in-dustries, supported by new EU policies (REACH) Limitations: see 1a

L L






Data availability

Data quality

1e Savings of water by (partly) enzyme-based products in these sectors

Value: L Strength: This is an argument for the introduction of enzymes in down stream industries Limitations: see 1a

L L

2 Public acceptance of GM enzymes in products of downstream sectors 2a Change in public acceptance of GM enzymes in

downstream sector-products, in years Value: H Strength: This is relevant issue that has and could in the future influence highly the uptake of biotechnology in this sector Limitations: -

H H



Biofuels Similar to the previous sections, an ecological footprint would include a complete overview of the contribution of biofuels to a more sustainable environment (1a). One important part of the footprint is the greenhouse gas emissions reduction (1b) as biofuels are considered as one of the most important solutions to decrease greenhouse gas emissions in the future. Fluid biofuels can be made from different sources, such as sugar, grain, cellulosic and oil-seed crops. A third impact indicator deals with the use of organic waste for the production of biofuels (1c). Biofuels heavily rely on various crops as biomass feedstock and this will certainly impact the land use. The issue is – given the volumes produced in this sector - the largest for biomass for bio-fuels.(2a). Increased biomass production could also impact the employment in the agricultural sector (3a). Table 8.14 shows the impact phenomena and indicators for biofuels. Information on greenhouse gas emissions is provided in great detail by the International Energy Agency. The OECD (1998)138 and BIO (2004) provide some data on this issue. Availability of data on the use of organic waste for the production of biofuels is very limited; again the Interna-tional Energy Agency gives some information about the use of waste oils, greases and fats for the production of biodiesel. The International Energy Agency also provides rather detailed in-formation on land use for biofuels, although it is limited to the production of biofuels for trans-portation and the geographic areas include the US and the EU. Data on the impact of biomass on employment in the agricultural sector is not available. More information on data availability is given in table A.8.2.4 in annex 3. For the sector study we recommend the ecological footprint indicator in order to make an analy-sis of the production of certain types of biofuels (e. g. ethanol or biodiesel) compared to the footprint of the production of their petrochemical (fossil fuels) counterparts. This will provide a complete overview of the contribution of biofuels to a more sustainable environment. A prag-matic solution - if the footprint efforts are beyond scope -, is to select two of the most relevant indicators for the sector study: energy savings and land use. For both some data sources are available. Methods to provide more complete overviews are presented in the sectors discussed above.

138 OECD (1998): Biotechnology for Clean Industrial Products and Processes, Paris.



Table 8.14: Impact phenomena and indicators for biofuels

No Phenomenon Description of indicator Strength & limitations Value Data avail-ability

Data quality

1a Contribution of fluid fuels to a more sustainable environment

Environmental footprint of fluid biofuels

Strength: Indicator measuring the overall sustainable im-pact of biofuels, compared to fossil fuels Limitations: it is a very elaborated study, for which a lot of data from companies have to be used and they are not always willing to provide these data

H L L

1b Greenhouse gas emissions reduction by fluid biofuels relative to their fossil coun-terparts

Strength: Although this indicator is already included in the ecological footprint indicator, the decrease of greenhouse gas emissions is one of the key targets of the EU Limitations:-

H H H

1c Share of the number of m3 or tons of organic waste used in fluid biofuel production in total number of m3 or tons of organic waste recycled

Strength: This is a relevant issue in political discussion on sustainability of biofuels Limitations:-

L L L

2 Land use by biofuels 2a Share of land used by bio-

mass for biofuel production in total land use

Strength: Land use is an issue in the political debate on biofuels: some parties expect that relatively large areas have to be used for biomass Limitations:-

H M H


3a Change in employment re-lated to production of bio-mass used for biofuels

Strength: Employment gain/loss through biotech is a highly relevant political issue Limitations:-

L L L



Bioremediation Similar to the sections above it is also proposed in this sector to use the footprint indicator, although the sector by itself is very much contributing to decreasing the environmental burden of industrial activities. Nevertheless, making a footprint of bioremediation technologies and compare it with similar chemical and physical technologies is very valuable. In addition we propose to include three other impact indicators that address three main environmental as-pects: energy, toxic chemicals and water use (1b-1d). Footprint and energy indicators are of high value, the others are qualified of medium and low value. The availability of data is again very poor (see tables 8.15 and A.8.2.5 in annex 3). Also here, the sustainable impact data is found scattered in case studies and in other public sources. Similar as for other sectors the footprint study is recommended and the savings of energy, also because of the availability of energy consumption figures which make it possible to make rough estimates for the biotech-related part. Also here we propose to first make a draft overview of the potential contribution of the bioremediation to reduction of the use of energy, on the basis of the data in case studies and additional data which is available in the public domain. This overview can be presented and discussed with experts in the field in a workshop and on the basis of that a final overview can be made.



Table 8.15: Impact phenomena and indicators for bioremediation



Data avail-ability

Data quality

1 Contribution of bioremediation to more sustainable environment 1a Ecological footprint of bio-

remediation compared to its chemical or physical counter part, per waste stream

Value: H Strength: Indicator measuring the overall sustainable impact of the use of bioremediation technologies Limitations: it is an extensive exer-cise, for which a lot of data from companies have to be used. They are not always willing to provide these data

L L

1b Share of energy savings by bioremediation in total energy savings in environ-mental cleaning, per waste stream

Value: H Strength: Use of energy (esp. fossil based) is a highly relevant political issue Limitations: several ‘measure’ diffi-culties

L L

1c Share of bioremediation in total savings of toxic/aggressive chemicals in end-of-pipe applications and environmental cleaning for each waste stream

Value: M Strength: This is an argument for the introduction of biotechnology in cleaning up of waste streams, sup-ported by new EU policies (REACH) Limitations: several ‘measure’ diffi-culties,

L L

1d Share of bioremediation in total savings of water, for each waste stream

Value: L Strength: this is an argument for in-troduction of biotechnology Limitations: several ‘measure’ diffi-culties, also related to identification of bioremediation-related water use in companies

L

L

9. Generic impact indicators Generic impact indicators share a common denominator across all application fields. There are four main relevant indicators:

1. The number of employees active in biotechnology-related occupations, which is a measure of the impact of biotechnology on jobs.

2. Sales or turnover from biotechnology products, which measures the effect of biotech-nology on firm revenues.

3. Value-added from biotechnology products, which is the best measure of the impact of biotechnology on GDP.

4. The financial costs or benefits from the use of biotechnology processes versus non biotechnology alternatives, which measures the effect of biotechnology on competi-tiveness.

Developing the first two indicators is relatively simple and is based on surveys of firms active in biotechnology. The third indicator is a far better estimate than sales of the financial impacts of biotechnology, but it requires data on both sales and the value of material inputs to produce biotechnology products. The fourth indicator is a variation on the third and provides a measure of the competitive ad-vantages or disadvantages of biotechnology processes and poses a much greater challenge.



As an example, crop feedstocks for biofuels could be genetically modified in order to improve bioavailability of carbohydrates for bioconvenion. The change in costs from producing ethanol from such a GM crop versus a non GM crop could be estimated using data on GM and non GM production costs. This is closely related to value-added if the only differences are in mate-rial and non-labour production costs. In this case, the financial costs or benefits equal the dif-ference in the value added of GM ethanol and non GM ethanol. However, if labour costs are higher for the GM ethanol, for instance due to large learning costs for a new technology, the value added of GM ethanol could be greater than that for non GM ethanol, even though total production costs in the former are much higher, leaving GM ethanol non-competitive. Of course, this can only happen if subsidies or other mechanisms to distort market prices are in place. The costs and benefits of biotech products for final consumers partly depend on market prices for biotech goods and services versus non-biotech goods and services (when alternatives are available). Although it would be theoretically possible to calculate the price differential for all biotech goods and services, in practice this is only relevant on a product-by-product case, such as the market price for GM maize versus non GM maize. Consequently, prices are application-specific impact indicators. Theoretically, it would also be possible to sum value-added or the financial costs and benefits across biotechnologies, producing a single estimate of the financial impacts in agro-food or industrial applications. In practice, it would be very difficult to obtain enough detailed data to perform this exercise for financial costs and benefits. The best that might be possible at this time is to try to obtain complete data on value-added and case study estimates of financial costs and benefits for key applications. The agro-food sector includes good examples of case studies on the costs and benefits of GM crops versus non GM crops. 9.1 Statistics Table 9.1 summarizes available survey data for turnover from the sale of biotechnology pro-ducts. Relevant data are available for six countries. Employment data are given in table 5.1, as employment is an input measure when limited to R&D employees. Total employment of ‘biotechnology active’ employees is both an impact and also an output measure. Total em-ployment of firms active in biotechnology, which will include many employees that are not in-volved in biotechnology, is a very poor impact measure139.

Table 9.1: Statistics for biotechnology turnover

Country Year Total turnover1

Biotech sales

Net total profit/ loss

Health Agro-food

Industrial

Canada 2003 United States

2002 ( )4

Belgium 2002 UK 2003 ( )2 ( )3 ( )3 ( )3 Finland 2003 ( )2 ( )3 ( )3 ( )3 Germany140 2004

Notes: 1: Total turnover of firms with biotechnology activities. 2: Limited to DBFs. 3: Not limited to biotech turnover, but includes total turnover of DBFs. 4: Estimate for DBFs available from Ernst and Young. 139 The literature includes arguments that all employees in a firm with biotechnology activities should be assigned to biotechnology on the assumption that the competitiveness and survival of the firm depends on its biotechnology activities. This is not a believable assumption, since it would mean that all firms with failing product divisions would be doomed to die, or that a minor success in one product line could make up for rampant failure in the firm’s main lines of business. 140 Survey of the Federal Statistical Office 2005.



We are not aware of statistics for value-added for biotechnology. 9.2 Indicators Table 9.2 lists impact indicators that could be constructed from available turnover and em-ployment statistics. The examples include indicators for all application fields combined and indicators for specific applications when the latter can also be aggregated across sectors or applications. Indicator 1b requires additional data on total employment among biotechnology active firms while indicator 1c requires additional data on total turnover for these firms. For Canada, the United States and Belgium, these indicators can also be calculated for each of the three application fields. This is not possible for the UK and Finland, where the data by application field are limited to total employment and total turnover within each application and for Ger-many where data are not available by sector.



Table 9.2: Generic impact indicators

No. Description Strengths and limitations Value1 Latest year

Data Avail- ability

Data quality

1 Turnover 1a Total biotechnology turnover per million units of

GDP First approximation of the economic importance of biotechnology, but value-added would be a far supe-rior indicator.

M 2003 M M

1b Total biotechnology turnover per biotechnology em-ployee.

Indicator for the output per employee, but value-added per employee is preferable.

M 2003 M M

1c Share of biotechnology turnover out of total turnover of biotechnology active firms

Measures the focus of biotechnology active firms on biotechnology, which is a vital indicator for assessing the value of indicators that assign all revenues of bio-technology active firms to biotechnology.

H 2003 L M

1d Share of biotech turnover in each application out of total turnover in each application.

Application-based measure of economic importance of biotech by sector that can be aggregated across sec-tors. Denominator data on total turnover can be ob-tained from the OECD’s STAN database, but would be a crude estimate for health applications.

H 2003 L M

1e Shares of turnover in each application out of total biotechnology turnover: for instance, turnover from GM crops or bio-pharmaceuticals2.

Measures focus of firms on specific types of biotech-nology. Application totals can also be summed to ob-tain totals when the turnover from biotechnology prod-ucts is dispersed, as in agriculture.

H 2003 M M

2 Employment 3a Total biotechnology ‘active’ employees out of total

national employment. Measure of job impact of biotechnologies. H 2003 L M

3b Number of employees per application out of total employees in each application.

Application-based measure of job impacts that can be aggregated across sectors. Denominator data on total employees can be obtained from the OECD’s STAN database, but would be a crude estimate for health applications.

H 2003 L M



Table 9.2 continued

No. Description Strengths and limitations Value1 Latest year

Data Avail- ability

Data quality

3c Total biotechnology active employees out of total employment in biotechnology firms.

As with 1c, measures reliability of biotechnology indi-cators that assign all activities among biotechnology firms to biotechnology.

H 2003 L M

3d Shares of employment in each application out of total biotechnology employment.

Measures focus of firms on specific types of biotech-nology; can be averaged across all sectors.

H 2003 M M

1: Value for assessing the consequences of biotechnology. 2: The calculation of turnover for each application will vary. For instance, there are rarely direct data for the sales value of GM crops. Consequently, the result

must be estimated from the GM share of hectares for each crop, adjusted for data on the yield difference between GM and non GM crops. Therefore, GMshare = Hectarestotal

/(nonGMh*(1+Ya)), where h = hectares and Ya = yield adjustment for non-GM crops compared to GM crops. Once GMshare is esti-mated, the GM share of total production (either in tonnes or sales value) is = GMshare*Prodtotal For second generation crops based on product quality, the estimate must adjust for both differences in yield and in the market cost of the GM versus non-GM crop.

Availability: H = 10 or more countries, M = 5 – 9 countries, L = < 5 countries.

Value: H = high, priority for future data collection, M = moderate, only worth collecting if existing availability is High or Moderate in order to complete data sets, L = low, VL = very low, I = data collection difficult so indicator value is impractical, NS = no survey is required.

Data quality: The highest rating given here is Moderate, because of concerns over the ability of firms to give accurate data on the number of employees ac-tive in biotechnology or the revenues from the sale of biotechnology products.



9.3 Recommendations There are three main recommendations for generic impact indicators. All concern information that would need to be collected in surveys of firms. First, national coverage within Europe for biotechnology employment and turnover is poor. Ernst and Young provides estimates for both indicators for all of Europe, but these are limited to DBFs and do not include the biotechnology relevant employment of large firms active in biotechnology. Surveys of biotechnology active firms are needed to collect data on total reve-nues and employment and on biotechnology revenues and employment. Second, there are no data for Europe on firms that use biotechnology but which do not per-form R&D in biotechnology. The definitions in use of a ‘biotechnology firm’, DBF, or ‘biotech-nology active firm’ all require the firm to perform R&D in biotechnology. In 1997 Statistics Canada sampled all firms in sectors where biotechnology use was most likely and asked the firms if they made any use of a extensive range of biotechnologies, but the survey has never been repeated in any country, partly because biotechnology use rates were very low. These resulted in high survey costs to identify a small number of users. A less expensive alternative is to use a telephone survey of a sample of firms in sectors where biotechnology if they use a limited number of biotechnologies that are known to have applications in the respondent firm’s sector. Third, turnover data are a poor proxy for value-added. It would be worth experimenting in an interview study to see if it is feasible to obtain the necessary data to calculate value-added for biotechnology products.



III. Conclusions In response to a request from the European Parliament, the European Commission initiated the study "consequences, opportunities and challenges of modern biotechnology for Europe". The objective of this study is to provide a comprehensive assessment of the economic, social and environmental consequences, opportunities, and challenges from the application of modern biotechnology in Europe. This assessment should keep in mind major European policy goals: to become the most competitive and dynamic knowledge-based economy in the world, capable of sustainable economic growth with more and better jobs and greater social cohesion and respect for the environment. The present Task 1 prepares the ground for a number of empirical analyses within this project by 1) elaborating a comprehensive picture of relevant existing modern biotechnologies, 2) identifying and describing biotechnology applica-tions, 3) identifying appropriate indicators to enable an analysis of biotechnology applications and their consequences, and 4) identifying and evaluating available sources and required data for the assessment. A precondition for assessing the consequences, opportunities and challenges of modern bio-technology for Europe is a suitable definition and classification of modern biotechnology. Based on an analysis of available studies, surveys and statistics, we recommend using the latest OECD definitions from 2005. The OECD first defines biotechnology as "the application of science and technology to living organisms as well as parts, products and models thereof, to alter living or non-living materials for the production of knowledge, goods and services". This is normally followed by the OECD’s list-based definition to ensure that traditional bio-technologies are not included with modern biotechnologies, as noted in chapter 2. Using the OECD definitions in this project will improve international comparability, since these defini-tions are the most widely used in government biotechnology surveys and have produced several useful statistics. The analysis of applications of modern biotechnology (chapter 3) in various industry and service sectors shows that the main application areas of modern biotechnology can be classi-fied into three groups: • Applications of biotechnology in the medical and pharmaceutical sector, including new

drugs and therapies, new diagnostics and vaccines. • Biotechnology applications in primary production and the agro-food sector, covering ani-

mal husbandry, fisheries and aquaculture, crop production and forestry, and the production of pharmaceuticals in plants and animals, as well as the use of molecular diagnostics throughout the production chain.

• Biotechnology in industrial manufacturing energy and environment, ranging from bio-based feed stock for fuels, materials and chemicals, the use of biocatalysts in down-stream sec-tors, such as the food and feed industries, textile and leather, pulp and paper, and biore-mediation approaches for water, air and effluent gas, soil and solid waste.

For an assessment of the consequences of biotechnology applications, suitable indicators and data are required. For elaborating such indicators a conceptual approach was de-veloped which differentiates between three main categories (chapter 4): Firstly, we use input indicators which describe capabilities and capacities in biotechnology which form the pre-requisite in terms of knowledge and technologies for the adoption of biotechnologies by various economic sectors. Secondly, we use output indicators that evaluate the extent of the adoption of biotechnology within the different application sectors. Thirdly, impact indicators are proposed which assess the economic, social and environmental impacts of modern bio-technology applications. These three types of indicators were elaborated, taking into account generic features of bio-technology and biotechnology applications which are common to all application fields as well



as technology and application-specific features which are typical for specific applications. The process of developing indicators can be differentiated into three steps. The first step is to de-scribe the phenomena which need to be measured by the indicators. Second, suit-able indicators for measuring and defining the identified phenomena are developed. The third step is to assess the data requirements for constructing indicators and their availability and quality. Most input indicators for measuring capacities and capabilities in biotechnology are of a ge-neric nature and not disaggregated by applications due to limited data availability (chapter 5). The phenomena to be characterised and quantified by input indicators include firm counts, R&D prevalence and R&D expenditures, employees including R&D employees, collaborations between different actors of the innovation systems, capital raised, patents and publications. Key indicators describing these phenomena are private and public R&D expenditures, the number of employees, patent data and bibliometric data. The main sources for input indica-tors comprise business sector statistics from official surveys or reports, public sector statistics from official surveys or reports, database statistics such as publications and patent data-bases, and consulting firm statistics. The analysis of data availability and quality indicates that the availability of data for input indi-cators is almost inversely proportional to the value of the indicator. Availability is greatest for basic firm counts which is a highly misleading indicator and lowest for R&D investment and employment by field of applications which would be among the most useful indicators. Based on these observations, we recommend including in a future survey of European biotechnology firms questions about R&D expenditures and the number of employees active in biotechnol-ogy. A second very valuable type of indicator refers to public sector R&D expenditures and employment in biotechnology. Such data are difficult to obtain and are not collected using current statistical systems. We suggest taking advantage of the expected results of the Euro-pean BioPolis project, which aims at compiling data on public R&D expenditures in biotech-nology in all Member States. Finally, patent and bibliometric data are useful indicators of na-tional capabilities in biotechnology and can be tailored specifically to different application fields by designing suitable search strategies. Since comparability between countries and country coverage are very good for these indicators, we recommend to include patent and bibliometric analyses in the empirical sector analyses. Output indicators are both sector-specific to application areas in terms of sector-specific products or processes to be measured (chapters 6-8) and generic, as many of the phe-nomena to be measured in the different application areas are identical, such as building up biotechnology know-how in the sector, product approval, producing biotechnology-based products, gaining market shares for biotechnology products, or replacing established pro-cesses by bioprocesses. Sector-specific output indicators for pharmaceutical and medical applications include indica-tors for the early developmental stages, such as the share of clinical studies with novel bio-based approaches, the number of patents and publications, the specific legal framework con-ditions, such as the reimbursement situation, information to the public about bio-procedures, and finally the adoption of biotechnology processes for small molecules. For GM crops we observe a nearly complete coverage of all countries and all indicators, other agro-food appli-cations are characterised by a poor data availability. In industrial biotechnology data avail-ability and data quality are rather poor. Data availability and data quality are very heterogeneous in the different application areas. In the case of pharmaceutical and medical applications, we find reasonable data sources for only some of the key indicators. In particular, indicators related to the public domain and the political framework for adoption are characterised by poor data availability and quality. For agro-food applications of biotechnology we observe a good coverage of many countries and indicators. In industrial biotechnology, on the other hand, data availability and data quality are rather poor.



Impact indicators can also be divided into generic indicators and application-specific indica-tors. We find that the generic indicators often provide useful information for measuring both outputs and impacts. Important generic impact indicators include the number of employees, sales or turnover from biotechnology products, value-added from biotechnology products, and the financial costs or benefits from the use of biotechnology processes (chapter 9). Application-specific indicators for the medical and pharmaceutical field are morbidity, surro-gate endpoints (healthy life years, QUALYs, DALYs), mortality and composite indicators such as cost effectiveness, cost-utility or cost-benefit ratio. Important indicators for agro-food appli-cations include environmental effects such as carbon gas savings, effects on soil erosion and pesticide use, marginal land, animal waste reduction and societal effects (time savings, food safety etc). Relevant indicators for industrial applications of biotechnology include environ-mental effects such as the ecological foot-print, energy savings, greenhouse gas emissions, savings of toxic chemicals, savings of water, and effects on land use. In general, for most impact indicators, data availability and data quality are low. In many cases we find only case-specific data. Therefore, it is recommended to include company sur-veys, expert panels, Delphi approaches and case studies in the data-gathering exercise. In summary, we identified and developed a sufficient number of indicators for all three appli-cation fields and also for the input and impact side of the use of biotechnology. However, the analysis of data availability and data quality indicates that, for a considerable number of indi-cators, the available data (mainly based on different types of statistics) is not sufficient. Therefore, we recommend not restricting data gathering during the following empirical studies to the analysis of available statistical and survey material. Rather, additional methodological approaches are required. In particular we suggest: • Including specific questions on R&D expenditure and employees in European company

surveys, • Conducting case studies, such as life cycle analyses in the case of industrial biotechnol-

ogy, cost-benefit-analyses in the case of biotechnology-based medical treatments, and evaluations of the role of non-GM biotechnologies in developing new plant and animal va-rieties.

• Including patent and bibliometric analyses in all planned sector studies in order to provide highly comparable indicators about biotechnology capabilities and capacities.

In conclusion, this study shows the feasibility of carrying out a quantitative assessment of the use and impact of modern biotechnology. Implementing our recommendations should contribute to an improved evaluation of the consequences, opportunities and challenges of modern biotechnology for Europe.



IV. Annexes Annex 1: Tables on sources and data availability for medical and pharmaceutical applications (chapter 6)



Table A.6.1: Data availability

No Phenomenon Indicator US CA JP EU15 DE UK FR IT ES BE NL AT SE DK FI IE GR PT LU EE LT LV PL CZ HU SI SK

1-1 pham.sector 1-1a data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006

source CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1 CDB1

1-1b data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006


1-1c data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006


1-1d data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006


1-1e data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006


1-1f data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006


1-1g data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006


1-1h data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006





1-1i data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006


1-1k data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006


1-1l data availability X X X X X X X X X X X X X X X X X X X X X X X

latest year 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004

source CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3 CDB3

1-1m data availability X X X X X X X X X X

latest year 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002

source PSC3 PSC3 PSC3 PSC3 PSC3 PSC3 PSC3 PSC3 PSC3 PSC3

1-2 vet.sector1-2a data availability x x x x x x

latest year 2006 2006 2004 2005 2006 2006

source GS3 GS3 GS3 GS3 GS3 GS3

1-2b data availability X x x x x x x

latest year 2006 2006 2006 2005 2005 2004 2005

source GS4 GS4 GS4 GS4 GS4 GS4 GS4

1-2c data availability x x x x x

latest year 2006 2006 2004 2005 2005

source GS3 GS5 GS3 GS3 GS5

1-2d data availability X x x x

latest year 2006 2006 2004 2005

source GS3 GS3 GS3 GS3

1-2e data availability x x x x

latest year 2003 2006 2004 2005

source R1 GS3 GS3 GS3




1-2f data availability X x x x

latest year 2006 2006 2004 2005

source GS3 GS3 GS3 GS3

1-2g data availability x x x x

latest year 2006 2006 2004 2005

source R1/GSGS3 GS3 GS3

1-2h data availability X x x x x

latest year 2006 2006 2006 2006 2005

source GS3 GS6 GS4 GS4 GS6

1-2i data availability x x x x x x x x x x x x x x x x x x x x x x x

latest year 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

source CDB4 CDB4 CDB4 CDB4 CDB4 CDB4 CDB4/CDB5CDB4 CDB4 CDB4 CDB4 CDB4 CDB4/CDB5CDB4 CDB4 CDB4 CDB4/CDB5 CDB4 CDB4 CDB4/CDB5CDB4/

2 app. research2a data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006


2b data availability X X

latest year ? ?

source R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2

2c data availability

latest year


2d data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006


2f data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006


2g data availability X X X X X X X X X X X X X X X X X X X

latest year 1996 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004

source GS1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1




2h data availability X X X X X X X X X X X X X X X X X X X X X X X X X X X

latest year 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

source PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4 PSC4

3 health sector 3a data availability X X X X X X X X X X X X X X X X X X X

latest year 1996 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004

source GS1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1 PSA1

3b data availability X X

latest year 1996 2004

source GS1 GS2

3c data availability X X X X X X X X X X

latest year 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002


3d data availability X X X X X X X X X X

latest year 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002


4 policy 4a data availability X

latest year 2002

source PSA2

4b data availability

latest year


6 public 5a data availability

latest year



latest year





6 processes 6a data availability

latest year



latest year


6c data availability

latest year




Table A.6.2: Sources for output indicators

Source code Description CDB1 pjb pharmaprojects database CDB2 Science Citation Index CDB3 OECD Stan Database CDB4 OECD Biotechnology Database CDB5 OECD Agriculture Database CDB6 EPPATENT and WOPATENT databases GS1 http://www.hhs.gov/news/press/2001pres/01fsgenetictests.html GS2 http://www.parliament.uk/documents/upload/POSTpn227.pdf GS3 recommended sources:

USA: http://www.fda.gov/cvm/Green_Book/elecgbook.html CA: http://www.hc-sc.gc.ca/dhp-mps/prodpharma/databasdon/index_e.html JP: http://niah.naro.affrc.go.jp/publication/seikajoho2/index-e.html EU15: http://www.emea.eu.int/exlinks/exlinks.htm http://ultra.eudra.org/searchPharma/query/search.jsp?usearch.p_mode=Ba-sic D: http://www.bvl.bund.de/cln_007/nn_524538/DE/05__Tierarzneimittel/00__doks__downloads/MRL__Tabelle,templateId=raw,property=publicationFile.pdf/MRL_Tabelle.pdf http://dil.vetmed.vt.edu/default.htm Database of Approved Animal Drug Products (FDA Center for Veterinary Medicine, VMRCVM Drug Information Lab), Community register of veterinary medicinal products http://dg3.eudra.org/F2/register/vreg.htm

GS4 recommended sources: USA: http://www.fda.gov/cvm/antimicrobial.html CA: http://www.hc-sc.gc.ca/dhp-mps/vet/index_e.html JP: http://niah.naro.affrc.go.jp/publication/seikajoho2/index-e.html EU15: http://pharmacos.eudra.org/F2/register/vreg.htm (D. http://www.verbraucherministerium.de/index-000946D55EF21F8292876521C0A8D816.html) (UK: http://www.vmd.gov.uk/Publications/Antibiotic/AntiPubs.htm) (SE: http://www.sva.se/dokument/stdmall.html?id=647)

GS5 recommended sources: CA: http://www.inspection.gc.ca/english/anima/vetbio/prod/prode.shtml UK: http://www.defra.gov.uk/animalh/animindx.htm

GS6 CA: http://www.inspection.gc.ca/english/anima/feebet/feebete.shtml http://www.hc-sc.gc.ca/dhp-mps/prodpharma/databasdon/index_e.html EU15: http://europa.eu.int/comm/food/food/animalnutrition/feedadditives/index_en.htm http://europa.eu.int/comm/food/food/animalnutrition/feedadditives/comm_register_19122005.pdf

PSA1 IPTS study Ibaretta et al.: Towards quality assurance and harmonization of genetic testing services in the European Union



Source code Description PSA2 study conducted by national experts for the European HTA programme

http://gripsdb.dimdi.de/de/hta/hta_berichte/hta112_supplement_teil1_de.pdf; http://www.euro.who.int/HEN/Syntheses/prostate/20040518_3

PSC3 PICTF reports (includes among others data of PSC1)

PSC4 C.F. Grass Consulting Animal Health Database R1 recommended source:

international: http://www.oie.int/eng/normes/mmanual/A_summry.htm international: http://www.oie.int/eng/maladies/en_alpha.htm recommended source: http://www.who.int/rabies/vaccines/veterinary_vaccines/en/

R2 recommended source: company listings, e. g. http://www.biotech-europe.de/rubric/produkte/products_04/LjPr-04-12(A4)neu.pdf; http://www.gene-chips.com/GeneChips.html#Review %20Articles %20on % 20Microarray %20Technology

R3 recommended source: expert interviews with institutions responsible for approval, users and companies

R4 recommended source: expert interview with national health service, policy makers and insurance companies

R5 recommended source: expert interviews with national technology assess-ment boards such as http://www.hc-sc.gc.ca/ahc-asc/pubs/public-con-sult/2000decision/5-tech-panel_e.html, http://files.efbpublic.org, patient or-ganisations, medical doctors etc.

R6 recommended source: company survey among pharmaceutical companies



Annex 2: Tables on sources and data availability for the agro-food sector



Table A.7.1: Data availability

No Phenomenon Indicator US CA JP EU15 DE UK FR IT ES BE NL AT SE DK FI IE GR PT LU EE LT LV PL CZ HU SI SK CY MT CH SG KR

1-5 Field trials all data availability x x x x x x x x x x x x x x x x x x x x x x x x x x x x x ?

latest year 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 ?

source GS1 GS2 GS3 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 GS4 ? N1 N1

4 GM Hectares all data availability x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

latest year 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000

source FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO

6 all data availability x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

latest year 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 ?

source GS5 GS6 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 GS7 ? N1 N1

X4 data availability

latest year

source



Table A.7.2: Sources for agro-food indicators

GS1 Government statistics; see table 7.2 for website GS2 Government statistics, see table 7.2 for website GS3 Government statistics, see table 7.2 for website GS4 Government statistics, see table 7.2 for website N1 Singapore and South Korea restrict imports of GM crops and neither appear

to have a domestic system in place for field trials or for registration of GM products.

GS5 Government statistics, APHIS (Animal and Plant Health Inspection Service (not entirely clear how much responsibility remains with other US Govern-ment agencies, such as EPA or FSIS for product approvals)

GS6 Canadian Food Inspection Agency (but rules not yet established for ani-mals, as no approvals have been issued)

GS7 European registry of GM food and feed



Annex 3: Tables on sources and data availability for industrial biotechnology, energy and environment (chapter 8)



Tables A.8.1: Industrial biotechnology, energy and environment: output indicators Table A.8.1.1: Data characteristics chemicals

Phenomenon Indicator US CA JP EU15 DE UK FR IT ES BE NL AT SE DK FI IE GR PT LU EE LT LV PL CZ HU SI SK CY MT CH SG KR

OUTPUTLeading indicators (partial 1a (nom.) data availability y y y

latest year 2003 2004 2005

source GS2 OT2 PSA1

1a (denom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

source cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb11b data availability y

latest year 2005

source GS11c (nom.) data availability

latest year

source

1c (denom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

source cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1



Phenomenon Indicator US CA JP EU15 DE UK FR IT ES BE NL AT SE DK FI IE GR PT LU EE LT LV PL CZ HU SI SK CY MT CH SG KRBiochemical production and diffusion 2a (nom.) data availability y y y y

latest year 2004 2004 2004 2004

source OT2* OT2* OT2* OT2*

2a/b (denom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

source cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb12b (nom.) data availability y y y y

latest year 2004 2004 2004 2004

sourceOT2*, OT8*

OT2*, OT8*

OT2*, OT8*

OT2*, OT8*

2c (nom.) data availability

latest year

source


latest year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

source cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb12d data availability

latest year

source2e data availability

latest year

source




IMPACT

Contribution of (partly) bio-produced chemicals to a more sustainable environment 1a data availability

latest year

source1b (nom.) data availability

latest year

source

1b (denom.) data availability

latest year

source1c data availability

latest year

source1d data availability

latest year


latest year

source



Phenomenon Indicator US CA JP EU15 DE UK FR IT ES BE NL AT SE DK FI IE GR PT LU EE LT LV PL CZ HU SI SK CY MT CH SG KRLand use by biomass feedstock for chemical industry 2a (nom.) data availability

latest year

source2a (denom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

source CDB1 CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1Additional employment in agricultural sector 3a (nom.) data availability

latest year

source3a (denom.) data availability

latest year

source



Table A.8.1.2: Data availability of sources for chemicals (excl polymers)

Phenomenon Indicator Data availability OUTPUT Indicators Leading indi-cators

1a Share of chemical companies that apply one or more bioconver-sion steps in their pro-duction processes in total of chemical companies, per year

Nominator: Number of dedicated biotech firms in industrial biotechnology only for a few countries: Germany, 2004, in OT2 Netherlands, 2001 - 2005, in GS1 USA, in GS2 Number of diversified companies in industrial biotechnology on y for Netherlands, 2004, in PSA1

1b Increase in number of companies specialised in applying bioconversion in time (from 2000 on-wards), per year

Netherlands, 2001 - 2005, in GS1

1c Share of biotechnolo-gist of total R&D staff in chemical companies, per year

Data for nominator have to be provided by ex-perts Data for denominator are: Employment, by level in chemical Industry: lower secondary. Upper secondary, tertiary, for EU25, 2001, CDB1 Total employment figures and average number of employees of chemical enterprises, EU25, 2001, CDB1

Biochemical production and diffusion

2a Volume of chemicals that are produced only by biotechnological produc-tion processes in total of chemicals, per product group

OT2 holds: - overview of world production of all chemicals produced by bioconversion processes (fer-mentation, biocatalysis and biotransformation). size (mio. €), producing companies - world production of 4 biotechnological pro-duced vitamins, 6 bulk products and 14 amino acids (t/a and world market prices) CDB1 provides turnover of chemical industry (in mio. €), for EU25

2b Volume of chemicals that are produced through production pro-cesses in which one or more chemical reaction steps have been re-placed by bioconversion steps in the total of chemical produced, per product group, per year

OT2 provides data on part (in %) of chemical product (in bn. US$) that is produced by bio-tech production processes, World production of 4 biotechnological pro-duced vitamins, 6 bulk products and 14 amino acids (t/a and world market prices) Overview of all chemicals produced by bio-conversion separate for (fermentation and bio-catalysis) processes. Per product/method: t/a, price (€/kg), market size (mio. €), producing companies, in OT2 OT8 (published in 1998) provides data on biotech-related market share in chemical in-dustry for EU, USA and Japan (bn. US$)

2c Gross value of (partly) bio-produced chemicals in total gross value of chemicals, per product group

Nominator data has to be provided by experts CDB1 (2001) provides denominator data on value added at factor costs for chemical in-dustry, EU25 (mio. €) and labour productivity for chemical industry EU25 (value-added per person empl. - in thousand €)



Phenomenon Indicator Data availability 2d Cost reductions by

using – partly - biocon-version processes com-pared to using only chemical production processes

Case studies for specific products (groups), in OT1, 2001 Three case studies that show different ways in which companies can capture value from in-dustrial biotech, in OT3, 2004 Cost-benefits analysis of use of industrial bio-technology, in OT4, 2003 and OT5, 2003 Cost reduction of using biotechnology in in-dustrial production, in OT6, 2004 Examples of economic benefits, in OT7,2004 And other case studies in publications and conference proceedings

2e New type of chemi-cals through bio-produc-tion with unique function-ality (specialities)

Overview in OT2, 2004

IMPACT Indicators Contribution of (partly) bio-produced chemicals to a more sustain-able environ-ment

1a Ecological foot-print141 of chemicals (partly) produced by bio-processes compared to non-bio produced chemi-cals, per product group

-

1b Energy savings by (partly) bio-produced chemicals: share of en-ergy used for – partly - bio-produced chemicals in energy used for 100 % non-bio chemical pen-dant, per product group

Energy savings for some products, in OT4, 2003

1c Greenhouse gas emissions savings by bio-produced chemicals rela-tive to their petrochemical counterparts

CO2 emission reduction for some products, in OT4, 2003

1d Savings of toxic/aggressive chemi-cals by (partly) bio-pro-duced chemicals, per product group

Some case studies address savings of toxic/aggressive chemicals, in OT1, 2001

1e Savings of water by (partly) bio-produced chemicals, per product group

Some case studies address water savings, in OT1, 2001

141 Environmental footprint is a measure of the burden or impact that a product, operation or corporation places on the environment. Using Life Cycle Assessment (LCA) methodology, one can compute a holistic environmental foot print of a product or a production process. Some key metrics that need to be measured in this process are: Green House gas emissions (COX, VOCs), Energy Consumption, Total waste production (mitigated by reduction in use, and recycling and composting), Toxic waste generation, Regulated Air pollutants release – air emissions (SOCs/NOX, particulates) and Water discharges (http://www.abe.iastate.edu/biobased/LCAFootprint.pdf, accessed 9 December 2005) In OECD (2001) a Green Index for industrial biotechnology is presented. It is a checklist for the sustainability of biotechnological processes. It covers the categories: energy, raw materials, waste, roducts and by-products, process and safety



Phenomenon Indicator Data availability Land use by biomass feed-stock for chemical in-dustry

2a Share of land use for chemical industry in total land use

Landuse by agricultural crop, for EU25. It is very likely that these figures are also collected in the USA, in CDB1, 2001

Additional employment in agricultural sector

3a Change in employ-ment related to produc-tion of biomass used for chemical industry relative to the change in em-ployment in the total agri-cultural sector

-

Table A.8.1.3: Source Key Chemicals

Source Description and/or reference CDB1 Eurostat (SBS), for data on :

Turn over of chemical industry, Value added at factor costs for chemical indus-try, Labour productivity for chemical industry, number of companies and em-ployment for chemical industry Land use by agricultural crops

GS1 Sector reports of BioPartner Network for 2001, 2002, 2003, 2004, 2005: dedi-cated biotechnology companies in the Netherlands, by sector

GS2 US Department of Commerce (2003) A survey of the use of Biotechnology in Industry

PSA1 Enzing et al (2004) Economic Impact of Industrial biotechnology in the Nether-lands: dedicated and diversified companies active in industrial biotechnology

OT1 OECD (2001) The application of Biotechnology to Industrial Sustainability OT2 Dechema (2004) Weisse Biotechnologie: Chance für Deutschland OT3 Riese and Bachmann (2004) McKinsey,

http://www.mckinsey.com/clientservice/chemicals/potentialprofit.asp ass 18 Dec 2005

OT4 McKinsey and company (2003) for EuropaBio publication: ‘White Biotechnology: Gateway to a More Sustainable Future’

OT5 DSM (2004) Industrial white biotechnology, An effective route to increase EU innovation and sustainable growth, Position document on Industrial Biotechnol-ogy in Europe and The Netherlands

OT6 BIO (2004) New Biotech Tools for a Cleaner Environment – Industrial Biotech-nology for Pollution prevention, Resource conservation and Cost reduction

OT7 UK Industrial Biotechnology Task Force (2004) Industrial Biotechnology: Deliv-ering Sustainability and Competitiveness

OT8 OECD (1998) Biotechnology for clean industrial products and processes



Table A.8.2.1: Data characteristics biopolymers


OUTPUTLeading indicators (partial input indicators) 1a (nom.) data availability y y y

latest year 2004 2004 2004

source OT1 OT1 OT11a (denom.) data availability

latest year


latest year

source1b (denom.) data availability

latest year



Phenomenon Indicator US CA JP EU15 DE UK FR IT ES BE NL AT SE DK FI IE GR PT LU EE LT LV PL CZ HU SI SK CY MT CH SG KRBiopolymer production and 2a (nom.) data availability y y y

latest year 2002 2002 2002

source OT1 OT1 OT1

2a (denom.) data availability y

latest year 2002

source OT12b (nom.) data availability

latest year


latest year

source2c (nom.) data availability y y


source OT1 OT12c (denom.) data availability

latest year

source2d (nom.) data availability

latest year

source2d (denom.) data availability

latest year

source




IMPACTContribution of biopolymers to a more sustainable environment 1a (nom.) data availability

latest year


latest year

source1b (nom.) data availability y y


source OT2 OT11b (denom.) data availability y y


source OT2 OT11b-1 (nom.) data availability y

latest year 2002

source OT1(denom.) data availability

latest year

source1c (nom.) data availability y

latest year 2004

source OT11c (denom.) data availability

latest year

source1c-1 (nom.) data availability y y


source OT2 OT1(denom.) data availability

latest year

source



Phenomenon Indicator US CA JP EU15 DE UK FR IT ES BE NL AT SE DK FI IE GR PT LU EE LT LV PL CZ HU SI SK CY MT CH SG KRLand use by biopolymers 2a (nom.) data availability y y y y y

latest year 2004 2004 2004 2004 2004?

source OT1 OT1 OT1 OT1 OT12a (denom.) data availability

latest year

sourceAdditional employment in agricultural sector 3a (nom.) data availability

latest year


latest year

source



Table A.8.2.2: Data availability of sources for biopolymers

Phenomenon Indicator Data availability OUTPUT Indica-tors

Leading indica-tors (partial in-put indicators)

1a Share of biopolymer producing companies in total number of companies producing polymers

CDB1 Data only available for chemical ‚end’ products producing companies, not specified for polymers or biopolymers OT1 gives overview of major (bio)polymer producing companies in EU 15 and accession countries

1b Share of biotechnologist of total R&D staff in bio-polymers producing com-panies

Number of employees in companies in the chemical industry, not specifically available for (bio)polymer producing companies, CDB3, 2000 No data on biotech R&D staff in chemical industry

Biopolymer production and diffusion

2a Share of biopolymers in total polymer production volume

Data for some countries in OT1 2002

2b Share of gross value added of biopolymer in total gross value added of polymers

Yes for gross value added for total chemi-cal industry, refinery products, not espe-cially for polymers, CDB2, 2001 No for gross value added for biopolymer

2c Share of number of dif-ferent biopolymers in total number of different poly-mers on the market

Description of 7 bio-based polymer product groups in biopolymer study for IPTS, OT1, 2004 Total number of different polymers on the market is not known, only different types of polymers (broad categories)

2d Share of products using biopolymers in total num-ber of products using polymers

-

IMPACT Indicators phenomenon Indicator Data availability Contribution of biopolymers to a more sustain-able environ-ment

1a Greenhouse gas emissions savings by bio-polymers relative to their petrochemical counterparts

OT 1: in kg CO2 eq/kg, per biopolymer group and their petrochemical counterparts For EU 15 + accession countries OT2: Air pollution releases of plastics in-dustry in the US

1b Share of greenhouse gas emissions savings by biopolymers in total chemi-cal industry greenhouse gas emissions savings

EU15 + accession countries - in kg CO2 eq/kg, per biopolymer group - in Mt CO2 2002 compared to 2000 - Co2 emission reduction for bio-based polymers compared to Co2 increase for petrochemical polymers in 2002 compared to 2000, in OT1

1c Energy savings by bio-polymers relative to their petrochemical counterparts

EU 15 + accession countries - In MJ/kg, per biopolymer group, in OT1

2a Share of biopolymers in total chemical industry energy savings

OT1: EU 15 + Accession countries - in MJ/kg, per biopolymer group - in PJ for 2002 compared to 2000 - Energy reduction for bio-based polymers compared to energy increase for petro-chemical polymers in 2002 compared to 2000



Phenomenon Indicator Data availability OT2: US, if PLA used industry wide

Land use by biopolymers

3a Share of land use by biopolymers in total land use

EU 15 + accession countries Land use in (ha*a)/t polymer per polymer type, per crop type, per country (CH, D, F, USA, EU-15), in OT1

Table A.8.2.3: Source Key Biopolymers

Source Description and/or reference CDB1 EUROSTAT: number of enterprises for all NACE sectors

Number of companies provided by national bureaus for statistics CDB2 EUROSTAT: gross value added figures for all NACE sectors

Gross value added figures for economic sectors provided by national bureaus for statistics

CDB3 EUROSTAT: number of employees and number of R&D employees for all NACE sectors Number of employees and R&D employees provided by national bureaus for statistics

OT1 Manuela Crank, Martin Patel, Frank Marscheider-Weidemann, Joachim Schleich, Bärbel Hüsing, Gerhard Angerer (2004)Techno-economic Feasibility of Large-scale Production of Bio-based Polymers in Europe (PRO-BIP) Draft Final Report May 2004, Prepared for IPTS

OT2 Biotechnology Industry Organisation (BIO) (2004) New Biotech Tools for a Cleaner Environment: Industrial Biotechnology for Pollution Prevention, Re-source Conservation, and Cost Reduction



Table A.8.3.1: Data characteristics enzymes in downstream industries Phenomenon Indicator US CA JP EU15 DE UK FR IT ES BE NL AT SE DK FI IE GR PT LU EE LT LV PL CZ HU SI SK CY MT CH SG KR

OUTPUTLeading indicators (partial input) 1a (nom.) data availability

latest year

source

1a (denom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

source cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb11b (nom.) data availability

latest year

source

1b (denom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

source cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1Enzyme production and diffusion 2a data availability y y y y

latest year 2004 204 2004 204

source OT2,3 OT2,3 OT2,3 OT2,32b (nom.) data availability

latest year

source

2b (denom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

source cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb12c (nom.) data availability

latest year

source


latest year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

source cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb1 cdb12d data availability

latest year


latest year

source




IMPACT

Contribution of (partly) bio-produced products of these sectors to a more sustainable environment 1a data availability

latest year


latest year


latest year

source1c (nom.) data availability

latest year

source1c (denom.) data availability

latest year

source1d data availability

latest year


latest year

source

Public accept-ance of GM enzymes in food 2a data availability y

latest year 2003

source GS1

data availability



Table A.8.3.2: Data availability of sources for enzymes in downstream industries: (food and feed, textile and leather, pulp and paper, mining and others)

Phenomenon Indicator Data availability OUTPUT Indicators Leading indicators (par-tial input indicators)

1a Share of companies that apply biocatalysis processes in total number of compa-nies, for each sector

Nominator: estimates to be collected through expert interviews Denominator: CDB1 (2001) holds numbers of companies in each of the down stream sectors, EU25

1b Share of biotechnologist of total R&D staff in compa-nies in each sector

Nominator: estimates to be collected through expert interviews Denominator: CDB1 (2001) holds figures on number of employees for each downstream sector, in total

Enzyme production and diffusion

2a Volume of enzymes used in each sector in years

Use of (technical) enzymes in down-stream sectors (Per sector: % of to-tal), worldwide in OT2, 2004 (indi-cated: world = USA+Japan+Canada+EU25) Estimates of biotech-related market share in the downstream sectors, Europe, USA and Japan (bn. US$), in OT3, 2004

2b Share of production volume of products resulting from biocatalysed production processes in each sector in total production volume of products resulting from catalyse processes in each sector

Nominator: estimates to be collected through expert interviews Denominator: Production by sector (mio. €/a), in CDB1, 2001

2c Share of gross value of products produced by bio-catalysed processes in each sector in total gross value of products produced by cata-lysed processes in each sector

Nominator: estimates to be collected through expert interviews Denominator: Value added per sector (mio. €), EU25 in CDB1, 2001

2d Cost reductions by using biocatalysed processes in-stead of non-bio catalysed processes in each sector

Examples of economic benefits, in OT2, 2004 Case studies for specific products (groups), in OT1, 2001 and OT5, 2004 Case studies of archetypical pro-cesses for downstream sectors, OT4, 2004

2e Number of different types biocatalysed processes used in each sector, per year of introduction

Several publications, reports, in-cluding OT1-OT5 (2004, is last year)

IMPACT Indicators Phenomenon Indicator Data availability Contribution of (partly) bio-produced products of these sectors to a more sustainable environment

1a Ecological footprint of products (partly) produced by enzyme compared to total chemically produced pro-ducts in these sectors

-



Phenomenon Indicator Data availability 1b Energy savings by using

enzymes in production: share of energy used for (partly) enzyme-based pro-ducts in these sectors in to-tal energy used by 100 % chemical pendants

Energy savings figures for some processes OT2, 1998

1c Greenhouse gas emissions savings by using enzymes in production: share of energy used for (partly) enzyme-based pro-ducts in these sectors in to-tal energy used by 100 % chemical

CO2 reduction of archetypes of bio-catalysed production in downs stream sectors, in OT4, 2002 CO2 emission reduction for 6 pro-ducts, in OT7, 2003

1d Savings of toxic/aggressive chemicals by (partly) enzyme-based products in these sectors

AOX discharges of archetypes of bio-catalysed production in downs stream sectors, in OT4, 2002 and OT7, 2003

1e Savings of water by (partly) enzyme-based pro-ducts in these sectors

Some incidental figures on water savings, in OT3, 1998

Public acceptance of GM enzymes in food

2a Change in public accep-tance of GM enzymes in food in years

Public attitude of citizens towards gm food, EU15, in GS1, 2003

Table A.8.3.3: Source key for enzymes in the downstream sector

Source Description and/or reference CDB1 Eurostat (SBS) for data on:

Turnover, productions figures, employment for each of the down stream sectors GS1 Eurobarometer 58.0. (2003) Europeans and Biotechnology 2002, Report by

George Gaskell, Nick Allum and Sally Stares from Methodology Institute, Lon-don School of Economics for the EC Directorate General Research

OT1 OECD (2001) The application of Biotechnology to Industrial Sustainability OT2 Dechema (2004) Weisse Biotechnologie: Chance für Deutschland OT3 OECD (1998) Biotechnology for clean industrial products and processes OT4 IPTS (2002) The assessment of future environmental and economic Impacts of

process-Integrated Biocatalysis OT5 BIO (2004) New Biotech Tools for a Cleaner Environment – Industrial Biotech-

nology for Pollution prevention, Resource conservation and Cost reduction OT6 UK Industrial Biotechnology Task Force (2004) Industrial Biotechnology: De-

livering Sustainability and Competitiveness OT7 McKinsey and company (2003) for EuropaBio publications: ‚White Biotechnol-

ogy: Gateway to a More Sustainable Future’



Table A.8.4.1: Data characteristics biofuels


OUTPUT

Leading indicators (partial input indicators) 1a (nom.) data availability y

latest year 1998

source CDB41a (denom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

source CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB2CDB21b data availability y

latest year 1998

source CDB41c (nom.) data availability

latest year


latest year

sourceBiofuel production and diffusion 2a (nom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003

source CDB1 & CDB4CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1

2a (denom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003

source CDB1 & CDB4CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB12b (nom.) data availability y

latest year 1998

source CDB42b (denom.) data availability

latest year

source



Phenomenon Indicator US CA JP EU15 DE UK FR IT ES BE NL AT SE DK FI IE GR PT LU EE LT LV PL CZ HU SI SK CY MT CH SG KRDiffusion of bio-fuels in the consumption of fuels 2c (nom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003

source CDB1 CDB1CDB1CDB1CDB1CDB1CBD1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB12c (denom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003

source CDB1 CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB12d (nom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003

source CDB1 CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB12d (deom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003

source CDB1 CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB12e (nom.) data availability

latest year

source2e (denom.) data availability

latest year

source




IMPACT

Contribution of fluid biofuels to a more sustainable environment 1a (nom.) data availability

latest year


latest year

source1b (nom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004

source OT2 CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CBD1CDB11b (denom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004

source CDB1 CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CDB1CBD1CDB11c (nom.) data availability

latest year

source OT11c (denom.) data availability

latest year

sourceLand use by biofuels 2a (nom.) data availability y y


source CDB1 CDB12a (denom.) data availability

latest year

sourceAdditional employment in agricultural sector 3a (nom.) data availability

latest year

source

latest year


latest year

source



Table A.8.4.2: Data availability of sources for biofuels

Phenome-non

indicator Data availability

OUTPUT Indicators Leading indicators (partial in-put indica-tors)

1a Share of number of fluid biofuels producing companies in total number of fluid fossil fuels producing com-panies

Yes for total number of liquid fossil fuels producing companies, CDB2, 2001 EU25 in total, all individual members, accession countries, CH, NO, Not for number of liquid biofuels producing compa-nies in EU

1b Increase in number of specialised fluid biofuel producing companies in time (from 2000 onwards

CDB4, 2004

1c Share of biotech-nologist of total R&D staff in fluid fuels pro-ducing companies

Number of employees in companies in the manufac-turing of refined petrochemical products, not specifi-cally available for fluid fuels producing companies, CDB5, 2000 No data on (biotech) R&D staff in petrochemical in-dustry

Biofuel production and dif-fusion

2a Share of fluid bio-fuels in total produc-tion volume of fluid fossil fuels

Members of International Energy Agency: AU, BE, CZ, FI, DE, HU, IT, KOREA, NL, NO, ES, CH, UK, AT, CA, DK, FR, GR, IE, JP, LU, NZ, PT, SE, TU, US, OECD Countries, NON-OECD countries, EU 25, EU 15, in CDB1, CDB4, 2003 Economic Research Service of USDA tracks ethanol and biodiesel industries in the U.S. (contact: Hosein Shapouri, [email protected]) • Plants operating • Production capacity • Value-added • Feedstock used • Use in major fleets (e. g., US Postal Service) Production costs, based on cost of production survey in 1998

2b Share of gross value added of liquid biofuels in total gross value added of all li-quid fossil fuels

Yes for gross value added for total energy produc-tion, not for biofuels, CDB3, 2001

2c Share of sales (volume) of fluid bio-fuels through retailers in total sales of fluid fuels through retailers

See 2a

2d Share of volume of fluid biofuels sold to and used by industry in total sales to and use of fluid fuels by industry

See 2a

2e Share of number of fluid biofuel products in total number of fuel products

-



Phenome-non

indicator Data availability

IMPACT Indicators Contribution of fluid bio-fuels to a more sus-tainable environ-ment

1a Environmental foot-print of fluid biofuels compared to footprint of liquid fossil fuels

Case by case availability e. g.: Comparative life sycle assessment of coconut biodiesel and con-ventional diesel for Philippine automotive transpor-tation and industrial boiler application Liezzel M. Pascual1 Raymond R. Tan http://www.lcacenter.org/InLCA2004/papers/ Pascual_L_paper.pdf Stoglehner, Gernot (2002). Ecological Footprint – a tool for assessing sustainable energy supplies. Journal of Cleaner Production 11 (2003) 267-277.

1b Greenhouse gas emissions reduction by fluid biofuels relative to their fossil counterparts

International Energy Agency members: estimated greenhouse gas emission reductions, CDB1, 2000 OECD: CO2 emissions from synthetic and biotechnological ethanol production, OT1, 1998 US: savings when using biomass fuels to power-steam generation plants to produce E10 fuels, for individual process savings, OT2, 2004

1c Share of the number of m3 or tons or organic waste used in fluid bio-fuel production in total number of m3 or tons or organic waste recycled

Some data on the use of waste oils, greases and fats for the production of biodiesel, in CDB1

Land use by biofuels

2a Share of land used by feedstock (biomass) for biofuel production in total land use

US and EU data on land use for ethanol and bio-diesel production, in CDB1, 2000

Table A.8.4.3: Source Key for Biofuels

Source Description and/or reference CDB1 International Energy Agency: An intergovernmental body committed to advancing security of

energy supply, economic growth and environmental sustainability through energy policy co-operation. Provides statistics for several regions and all Member States as well as non OECD countries. Special study: Biofuels for Transport: An International Perspective (2004) http://www.iea.org/index.asp

CDB2 EUROSTAT: number of enterprises for all NACE sectors Number of energy producing and mining companies provided by national bureaus for statistics

CDB3 EUROSTAT: gross value added figures for all NACE sectors Gross value added figures for economic sectors provided by national bureaus for statistics

CDB4 Economic Research Service of USDA tracks ethanol and biodiesel industries in the U.S. (contact: Hosein Shapouri, [email protected]) • Plants operating • Production capacity • Value-added • Feedstock used • Use in major fleets (e. g., US Postal Service) • Production costs, based on cost of production survey in 1998

CDB5 EUROSTAT: number of employees for all NACE sectors Number of employees for petrochemical industry provided by national bureaus for statistics

OT1 OECD (1998) Biotechnology for clean industrial products and processes - draft final report OT2 BIO (2004) New Biotech Tools for a Cleaner Environment



Table A.8.5.1: Data characteristics bioremediation


OUTPUTLeading indicators 1a (nom.) data availability

latest year

source

1a (denom.) data availability

latest year

source1b (nom.) data availability y

latest year 2004

source OT11b (denom.) data availability

latest year

sourceBioremediation application and diffusion 2a (nom.) data availability

latest year

source2a (denom.) data availability y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

latest year 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002

sourceCDB2, -3

CDB1, -2, -

3CDB2, -3

CDB1, -2, -

3CDB2, -3

CDB2, -3

CDB1, -2, -

3CDB2, -3

CDB1, -2,

-3CDB2, -3

CDB1, -2, -

3CDB2, -3

CDB2, -3

CDB2, -3

CDB2, -3

CDB2, -3

CDB2, -3

CDB2, -3

CDB2, -3

CDB1, -2,

-3CDB2, -3

CDB2, -3

CDB2, -3

CDB2, -3

CDB2, -3

CDB2, -3

CDB2, -3

CDB2, -3

CDB2, -3


latest year

source2c data availability

latest year

source




IMPACT

Contribution of bioremediation to more sustainable environment 1a data availability

latest year


latest year


latest year

source1c (nom.) data availability

latest year


latest year

source1d (nom.) data availability

latest year

source1d (denom.) data availability

latest year

source



Table A.8.5.2: Data availability of sources for bioremediation

Phenomenon Indicator Data availability OUTPUT Indicators Leading indica-tors

1a Share of bioremediation companies in total number of environmental treatment companies, per waste stream (from 2000 onwards)

-

1b Share of biotechnologist of total R&D staff in companies in environmental sector

Biotech-related employment in environmental sector, in UK in OT1, 2003 Biotech employment in DBFs (see Chapter 5)

Bioremediation application and diffusion

2a Share of expenditures and investments on biotechnological end-of-pipe applications in total sales volume of end-of-pipe applications, per waste stream, per year

Nominator: Nominator: estimates to be collected through expert interviews Denominator: Expenditures and investments on environmental protection for public sectors and specialised producers of environmental services in percent of GDP and € per capita (CDB1) and by industry (specified per sector), share of GVA and in mio. €, in CDB2, 2002. For 8 counties (BE, DK 2000. AT 2001. DE, FR, EE, PL, RO 2002) data on waste water and waste domains, for public sector and specialised producers in CDB1, 2002 Investments and current expenditure on environmental protection by public sector, for part (in %) of air, waste water, waste and other in total for 2002 (CDB1) and by industry (CDB2) Enterprises with an environmental management system, EU25 (CDB3)

2b Cost reductions by bioremediation techniques relative to non-biological treatment, per waste stream

Case studies OT1 (1994) and several publications

2c New biological treatment techniques/tools with unique features, per waste stream

Overview of bioremediation tools in several publications, such as ‘Biotechnology for the Environment: Soil Remediation’ (2002) of Agapos and Reineke, or ‘Biotechnol-ogy for the Treatment of Hazardous Waste’ of Stoner

IMPACT Indicators Contribution of bioremediation to more sustainable environment

1a Ecological footprint of bioremediation com-pared to its chemical or physical counter part, per waste stream

-

1b Share of energy savings by bioremediation in total energy savings in environmental cleaning, per waste stream

Energy consumption figures, CDB3, 2003 Some energy consumption of some bioremediation processes (OT2, 1994) and other public sources



Phenomenon Indicator Data availability 1c Share of bioremediation in total savings of

toxic/aggressive chemicals in end-of-pipe appli-cations and environmental cleaning for each waste stream

Some data on raw materials and waste materials (including use of chemicals) for a number of bioremediation processes in OT2 (1994) and other public sources

1d Share of bioremediation in total savings of water each waste stream

Some data on raw materials and waste materials (including use of chemicals) for a number of bioremediation processes in OT2, 1994 and other public sources

Table A.8.5.3: Source Key for Bioremediation

Source Description and/or reference CDB1 Eurostat (2005 ) Environmental Protection Expenditure in Europe by public

sector and specialised producers 1995-2002, Statistics in Focus, 10/2005 CDB2 Eurostat (2005) Environmental protection expenditure by industry in the Euro-

pean Union, 1995-2002, Statistics in Focus: 09/2005 CDB3 Eurostat (2005) A Statistical Review of Environmental issues OT1 UK Industrial Biotechnology Task Force (2004) Industrial Biotechnology: De-

livering Sustainability and Competitiveness OT2 OECD (1994) Biotechnology for a clean environment. Prevention, Detection and

Remediation



Annex 4: Tables on sources and data availability for generic impact indicators (chapter 9)



Table A.9.1: Data availability for generic impact indicators No Phenomenon Indicator US CA JP EU15 DE UK FR IT ES BE NL AT SE DK FI IE GR PT LU EE LT LV PL CZ HU SI SK CY MT CH SG KR

1 Turnover 1a data availability x x x x x x N2

latest year 2002 2003 2004 2003 2002 2003

source N1 N1 N1 N1 N1 N1

1b data availability x x x x x x

latest year 2002 2003 2004 2003 2002 2003

source N1 N1 N1 N1 N1 N1

1c data availability x x x

latest year 2002 2003 2002

source N1 N1 N1

1d data availability x x x

latest year 2002 2003 2002

source N1 N1 N1

1e data availability x x x x x

latest year 2002 2003 2003 2002 2003

source N1 N1 N1 N1 N1

2 Employment 2a data availability x x x x

latest year 2002 2003 2003 2002

source N1 N1 N1 N1

2b data availability x x


source N1 N1

2c data availability x x x x

latest year 2002 2003 2003 2002

source N1 N1 N1 N1

2d data availability x x


source N1 N1



Table A.9.2: Sources for generic impact indicators

N1 See relevant national survey cited in table 5.1. The data for the UK, Ger-many and Finland are based on DBFs.

N2 Some data are recently available from a 2004 survey of 640 South Korean biotech firms, conducted by the Korea Institute for Industrial Economics and Trade, but it is not yet clear how biotechnology is defined or if the relevant indicators can be produced from the results.