The Future of Open Innovation: Development and Use of Therapies End of the Era of Medical Guilds and Alchemy Moving beyond the Medical Industrial Complex Stephen Friend MD PhD Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ Amsterdam UC Berkeley Hass School of Business Topics in Innovation March 5, 2012
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
The Future of Open Innovation: Development and Use of Therapies
End of the Era of Medical Guilds and Alchemy
Moving beyond the Medical Industrial Complex
Stephen Friend MD PhD Sage Bionetworks (Non-Profit Organization)
Seattle/ Beijing/ Amsterdam
UC Berkeley Hass School of Business Topics in Innovation
March 5, 2012
• New ways of Building Models of Disease
• What prevents us from building them?
• What is Sage Bionetworks?
• Review of Six Pilots
• So what are the next steps?
What is the problem?
Most approved therapies were assumed to be monotherapies for diseases represen4ng homogenous popula4ons
Host evolving computational models in a “Compute Space”
WHY NOT USE “DATA INTENSIVE” SCIENCE
TO BUILD BETTER DISEASE MAPS?
what will it take to understand disease?
DNA RNA PROTEIN (dark maOer)
MOVING BEYOND ALTERED COMPONENT LISTS
2002 Can one build a “causal” model?
Preliminary Probabalistic Models- Rosetta /Schadt
Gene symbol Gene name Variance of OFPM explained by gene expression*
Mouse model
Source
Zfp90 Zinc finger protein 90 68% tg Constructed using BAC transgenics Gas7 Growth arrest specific 7 68% tg Constructed using BAC transgenics Gpx3 Glutathione peroxidase 3 61% tg Provided by Prof. Oleg
Mirochnitchenko (University of Medicine and Dentistry at New Jersey, NJ) [12]
Lactb Lactamase beta 52% tg Constructed using BAC transgenics Me1 Malic enzyme 1 52% ko Naturally occurring KO Gyk Glycerol kinase 46% ko Provided by Dr. Katrina Dipple
(UCLA) [13] Lpl Lipoprotein lipase 46% ko Provided by Dr. Ira Goldberg
Equipment capable of generating massive amounts of data A-
“Data Intensive” Science- Fourth Scientific Paradigm Score Card for Medical Sciences
Open Information System D-
IT Interoperability D
Host evolving computational models in a “Compute Space F
.
We still consider much clinical research as if we were “hunter gathers”- not sharing
TENURE FEUDAL STATES
Clinical/genomic data are accessible but minimally usable
Little incentive to annotate and curate data for other scientists to use
Mathematical models of disease are not built to be
reproduced or versioned by others
Lack of standard forms for future rights and consents
Lack of data standards..
Sage Mission
Sage Bionetworks is a non-profit organization with a vision to create a “commons” where integrative bionetworks are evolved by
contributor scientists with a shared vision to accelerate the elimination of human disease
Sagebase.org
Data Repository
Discovery Platform
Building Disease Maps
Commons Pilots
Sage Bionetworks Collaborators
Pharma Partners Merck, Pfizer, Takeda, Astra Zeneca, Amgen, Johnson &Johnson
27
Foundations Kauffman CHDI, Gates Foundation
Government NIH, LSDF, NCI
Academic Levy (Framingham) Rosengren (Lund) Krauss (CHORI)
Federation Ideker, Califano, Nolan, Schadt
What is this?
Bayesian networks enriched in inflammaVon genes correlated with disease severity in pre-‐frontal cortex of 250 Alzheimer’s paVents.
What does it mean?
InflammaVon in AD is an interacVve mulV-‐pathway system. More broadly, network structure organizes complex disease effects into coherent sub-‐systems and can prioriVze key genes.
Are you joking?
Gene validaVon shows novel key drivers increase Abeta uptake and decrease neurite length through an ROS burst. (highly relevant to AD pathology)
ALZHEIMER’S
Liver Adipose
FaDy acids
Hypothalamus
Macrophage/ inflamma4on
Lep4n signaling
Phagocytosis-‐ induced lipolysis
Phagocytosis-‐ induced lipolysis
M1 macrophage
A mulV-‐Vssue immune-‐driven theory of weight loss
RULES GOVERN
PLAT
FORM
NEW
MAP
S
PLATFORM Sage Platform and Infrastructure Builders-
( Academic Biotech and Industry IT Partners...)
PILOTS= PROJECTS FOR COMMONS Data Sharing Commons Pilots-
(Federation, CCSB, Inspire2Live....)
Why not share clinical /genomic data and model building in the ways currently used by the software industry (power of tracking workflows and versioning
Leveraging Existing Technologies
Taverna
Addama
tranSMART
Watch What I Do, Not What I Say sage bionetworks synapse project
Most of the People You Need to Work with Don’t Work with You
sage bionetworks synapse project
My Other Computer is Cloudera Amazon Google
sage bionetworks synapse project
Sage Metagenomics Project
• > 10k genomic and expression standardized datasets indexed in SCR • Error detection, normalization in mG • Access raw or processed data via download or API in downstream analysis • Building towards open, continuous community curation
Processed Data (S3)
Sage Metagenomics using Amazon Simple Workflow
Full case study at http://aws.amazon.com/swf/testimonials/swfsagebio/
Synapse Roadmap
Q1-2012 Q2-2012 Q3-2012 Q4-2012 Q1-2013 Q2-2013
Synapse Platform Functionality
Data / Analysis Capabilities
Q3-2013 Q4-2013
Internal Alpha Public Beta Testing Synapse 1.0 Synapse 1.5 Future
• Data Repository • Projects and security • R integration • Analysis provenance
• Search • Controlled Vocabularies • Governance of restricted data
• Workflow templates • Publishing figures • Wiki & collaboration tools • Integrated management of cloud resources
• Social networking • User-customized dashboards • R Studio integration • Curation tool integration
• Predictive modeling workflows • Automated processing of common genomics platforms
• TBD: Integrations with other visualization and analysis packages
CTCAP Arch2POCM The FederaVon Portable Legal Consent Sage Congress Project BRIDGE
Six Pilots involving Sage Bionetworks
RULES GOVERN
PLAT
FORM
NEW
MAP
S
Clinical Trial Comparator Arm Partnership (CTCAP)
Description: Collate, Annotate, Curate and Host Clinical Trial Data with Genomic Information from the Comparator Arms of Industry and Foundation Sponsored Clinical Trials: Building a Site for Sharing Data and Models to evolve better Disease Maps.
Public-Private Partnership of leading pharmaceutical companies, clinical trial groups and researchers.
Neutral Conveners: Sage Bionetworks and Genetic Alliance [nonprofits].
Initiative to share existing trial data (molecular and clinical) from non-proprietary comparator and placebo arms to create powerful new tool for drug development.
Started Sept 2010
Shared clinical/genomic data sharing and analysis will maximize clinical impact and enable discovery
• Graphic of curated to qced to models
Arch2POCM
Restructuring the PrecompeVVve Space for Drug Discovery
How to potenVally De-‐Risk High-‐Risk TherapeuVc Areas
Arch2POCM: scale and scope
• Proposed Goal: Initiate 2 programs. One for Oncology/Epigenetics/Immunology. One for Neuroscience/Schizophrenia/Autism. Both programs will have 8 drug discovery projects (targets) - ramped up over a period of 2 years
– It is envisioned that Arch2POCM’s funding partners will select targets that are judged as slightly too risky to be pursued at the top of pharma’s portfolio, but that have significant scientific potential that could benefit from Arch2POCM’s crowdsourcing effort
• These will be executed over a period of 5 years making a total of 16 drug discovery projects
– Projected pipeline attrition by Year 5 (assuming 12 targets loaded in early discovery)
• 30% will enter Phase 1 • 20% will deliver Ph 2 POCM data 45
Arch2POCM: Highlights A PPP To De-Risk Novel Targets That The Pharmaceutical Industry Can
Then Use To Accelerate The Development of New and Effective Medicines • The Arch2POCM will be a charitable Public Private Partnership (PPP) that will file no patents and
whose scientific plan (including target selection) will be endorsed by its pharmaceutical, private and public funders
• Arch2POCM will de-risk novel targets by developing and using pairs of test compounds (two different chemotypes) that interact with the selected targets: the compounds will be developed through Phase IIb clinical trials to determine if the selected target plays a role in the biology of human disease
• Arch2POCM will work with and leverage patient groups and clinical CROs to enable patient recruitment, and with regulators to design novel studies and to validate novel biomarkers
• Arch2POCM will make its GMP test compounds available to academic groups and foundations so they can use them to perform clinical studies and publish on a multitude of additional indications
• Arch2POCM will release all reagents and data to the public at pre-defined stages in its drug development process. To ensure scientific quality, data and reagents will be released once they have been vetted by an independent scientific committee
• Arch2POCM will publish all negative POCM data immediately in order to reduce the number of ongoing redundant proprietary studies (in pharma, biotech and academia) on an invalidated target and thereby – minimize unnecessary patient exposure – provide significant economic savings for the pharmaceutical industry
• In the rare instance in which a molecule achieves positive POCM, Arch2POCM will ensure that the compound has the ability to reach the market by arranging for exclusive access to the proprietary IND database for the molecule 46
Arch2POCM: proposed funding strategy – $160-200M over five years is projected as necessary to advance
up to 8 drug discovery projects within each of the two therapeutic programs
– Arch2POCM funding will come from a combination of public funding from governments and private sector funding from pharmaceutical and biotechnology companies and from private philanthropists
– By investing $1.6 M annually into one or both of Arch2POCM’s selected disease areas, partnered pharmaceutical companies:
1. obtain a vote on Arch2POCM target selection 2. have the opportunity to donate existing compounds from their
abandoned clinical programs for re-purposing on Arch2POCM’s targets
3. gain real time data access to Arch2POCM’s 16 drug discovery projects
4. have the strategic opportunity to expand their overall portfolio 47
Five Year Objective: Initiate ≈ 8 drug discovery projects with 6 entering in Early Discovery, one entering in pre-clinical and one entering in PH I
Early discovery (45% PTRS) Pre-clinical (70% PTRS) Ph I (65% PTRS)
Ph II (10% PTRS)
1.3
1
Ph 1 (1)
1
Year #2 Arch2POCM Target Load
Arch2POCM Snapshot at Year 5
Year #1 Arch2POCM Target Load
Early discovery (2)
1
Targets Loaded 8
Projected INDs filed 3-‐4
Ph 1 or 2 Trials In Progress 2
Projected Complete Ph 2 (POCM) Data Sets
1
*PTRS = Probability of technical and regulatory success
Pre-clinical (1)
Early discovery (4)
Pre-clinical
Pre-clinical
Ph 1
Ph 1
Ph 1
Ph 2
Ph 2
Ph 2
48
The case for epigenetics/chromatin biology
1. There are epigenetic oncology drugs on the market (HDACs)
2. A growing number of links to oncology, notably many genetic links (i.e. fusion proteins, somatic mutations)
3. A pioneer area: More than 400 targets amenable to small molecule intervention - most of which only recently shown to be “druggable”, and only a few of which are under active investigation
4. Open access, early-stage science is developing quickly – significant collaborative efforts (e.g. SGC, NIH) to generate proteins, structures, assays and chemical starting points
49
Arch2POCM epigenetics program: Assumptions for launch and completion of Year 1
• Funding necessary to prosecute 8 epigenetic target-based projects o ≈$85M for five years with $15M available for Year 1
• $1.6M from each of 3 pharma partners ($4.8M) • $5M from public funders and $5M from philanthropists
o Year 1: load 3 targets with 2 in Early Discovery and 1 in pre-clinical stage of development o Year 2: load 5 targets with at least one late stage clinical asset from a pharma partner
• Partners – In kind partners
o GE Healthcare (imaging): open sharing of its experimental oncology biomarkers o CRUK: through some of its drug discovery and development resources participating in Arch2POCM
– Potential academic partner sites • Institutions that have indicated willingness to let their scientists participate without patent filing: UCSF,
Massachusetts General Hospital, University of North Carolina, University of Toronto, Oxford University, Karolinska Institute
• Costs to fund Arch2POCM academic partners will be de-frayed by crowd-sourcing: each funded investigator will use their own network to amplify what they can do and publish on Arch2POCM targets
– Patient groups will enable patient recruitment and reduce costs for clinical studies – FDA and EMEA team of regulators available
o Oncology experts available o Can provide in vitro screening assays for toxicities and biomarker development to improve patient
selection o FDA to help build and host a compliant Arch2POCM data-sharing site
o Infrastructure that needs to be in place to execute on time o Align vendors and CROs prior to initiation of Arch2POCM projects o IT and patient database management: harmonization of data-entry across participating clinical collaborators
in place well before start of first Arch2POCM trial 50
General benefits of Arch2POCM for drug development
1. Arch2POCM’s use of test compounds to de-risk previously unexplored biology enables drug developers to initiate proprietary drug development starting from an array of unbiased, clinically validated targets
2. Arch2POCM’s crowdsourced research and trials provides the pharmaceutical industry with “parallel shots on goal: by aligning test compounds to most promising unmet medical need”
3. The positive and negative clinical trial data that Arch2POCM and the crowd produce and publish will increase clinical success rates (as one can pick targets and indications more smartly) and will save the pharmaceutical industry money by reducing redundant proprietary efforts on failed targets
51
Why is Arch2POCM a “smart bet” for Pharma investment?
Arch2POCM: an external epigeneVc think tank from which Pharma can load the most likely to succeed targets as proprietary programs or leverage Arch2POCM results for its other internal efforts • A front row seat on the progression of 8 epigeneVc targets means that:
• Pharma can select the epigeneVc targets that best compliment their internal poriolio and for which there is the greatest interest
• Pharma can structure Arch2POCM’s projects so that key objecVves line up with internal go/no-‐go decisions
• Pharma can use Arch2POCM data to trigger its internal level of investment on a parVcular target
• Pharma can use Arch2POCM resources to enrich their internal epigeneVcs effort: acVve chemotypes, assays, pre-‐clinical models, biomarkers, geneVc and phenotypic data for paVent straVficaVon, relaVonships to epigeneVc experts
• Pharma can use Arch2POCM’s lead compound chemotypes to: • inform their proprietary medicinal chemistry efforts on the target
• idenVfy chemical scaffolds that impact epigeneVc pathways: a proprietary combinaVon therapy opportunity
• Toxicity screening of Arch2POCM compounds with FDA tools can be used to guide internal proprietary chemistry efforts in oncology, inflammaVon and beyond
• Arch2POCM’s crowd of scienVsts and clinicians provides its Pharma partners with parallel shots on goal at the best context for Arch2POCM’s compounds/targets 52
• Arch2POCM’s Ph II validation of high risk high opportunity targets focuses Pharma’s NME efforts
• Positive POCM data: De-risked validated targets for Pharma development • Negative POCM data: public release of this data minimizes the amount of time
and money that Pharma and the industry place on failed targets
• Arch2POCM’s clinical candidate compounds provide Pharma with multiple paths to new medicines
• Arch2POCM compounds that achieve POCM can be advanced into Ph 3 by Arch2POCM Members
• The purchaser of Arch2POCM’s IND database obtains a significant time advantage over competitors to generate Phase III data and proceed to market
• NMEs that derive from Arch2POCM will launch with database exclusivity protections: 5-8 years to garner a return on investment
• The crowd’s testing of Arch2POCM compounds may identify alternative/better contexts for agonizing/antagonizing the disease biology target
How will Arch2POCM provide “line of sight” to new medicines?
53
The FederaVon
2008 2009 2010 2011
How can we accelerate the pace of scientific discovery?
Ways to move beyond “traditional” collaborations?
Intra-lab vs Inter-lab Communication
Colrain/ Industrial PPPs Academic Unions
(Nolan and Haussler)
sage federation: model of biological age
Faster Aging
Slower Aging
Clinical Association - Gender - BMI - Disease Genotype Association Gene Pathway Expression Pr
edicted Age (liver expression)
Chronological Age (years)
Age Differential
Reproducible science==shareable science
Sweave: combines programmatic analysis with narrative
Sweave.Friedrich Leisch. Sweave: Dynamic generation of statistical reports using literate data analysis. In Wolfgang Härdle and Bernd Rönz,editors, Compstat 2002 –
Proceedings in Computational Statistics,pages 575-580. Physica Verlag, Heidelberg, 2002. ISBN 3-7908-1517-9
Dynamic generation of statistical reports using literate data analysis
Normalization: Remove the influence of adjustment variables on data...!
=! +!
2) Automated, standardized workflows for cura4on and QC of large-‐scale datasets (Brig Mecham).
A. TCGA: Automated cloud-‐based processing. B. GEO / Array Expression: NormalizaVon workflows, curaVon of phenotype using standard ontologies. C. AddiVonal studies with geneVc and phenotypic data in Sage repository (e.g. CCLE and Sanger cell line datasets)
custom model 1 custom model 2 custom model N
4) Sta4s4cal performance assessment across models.
custom model 1 custom model 2 custom model N
5) Output of candidate biomarkers and feature evalua4on (e.g. GSEA, pathway analysis)
6) Experimental follow-‐up on top predic4ons (TBD) E.g. for cell lines: medium throughput suppressor / enhancer screens of drug sensiVvity for knockdown / overexpression of predicted biomarkers.
3) Pluggable API to implement predic4ve modeling algorithms.
A) Support for all commonly used machine learning methods (for automated benchmarking against new methods)
B) Pluggable custom methods as R classes implemenVng customTrain() and customPredict() methods.
A) Can be arbitrarily complex (e.g. pathway and other priors)
B) Support for parallelizaVon in for each loops.
Portable Legal Consent
(AcVvaVng PaVents)
John Wilbanks
weconsent.us
Sage Congress Project April 20 2012
RealNames Parkinson’s Project RevisiVng Breast Cancer Prognosis