Protein and metabolite biomarkers in personalized healthcare Prof Alain van Gool Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers Head Biomarkers in Personalized Healthcare 1 st Dutch Life Science Technology event Leiden, 26 th Nov 2013
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Protein and metabolite biomarkers in personalized healthcare
Prof Alain van Gool
Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers
Head Biomarkers in Personalized Healthcare
1st Dutch Life Science Technology event
Leiden, 26th Nov 2013
Personalized Healthcare
Right patient with right drug at right dose at right time
In other words: Apply a well characterized therapy in a biological system you know well to treat a disease you understand well, in a way that you know works. Often: Co-develop (molecular) biomarkers as diagnostic companions of a drug
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Companion Diagnostics – some numbers
At present in pharmaceutical development:
40.000 clinical trials ongoing
16.000 trials in oncology
8.000 trials in oncology have a companion diagnostic (many genetic)
At present on market:
113 Biomarker in drug label (2012; up from 69 in 2010 = +64%)
16 CDx testing needed (2012; up from 4 in 2010 = +400%)
Costs of development:
>1.000 MUSD per drug
~10 MUSD per diagnostic Source: www.fda.gov
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Companion Diagnostics
Metabolism
Efficacy or safety
Source: www.fda.gov {Kumar and van Gool, 2013}
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Changing fields: Personalized Healthcare @ USA
“The term "personalized medicine" is often described as providing "the
right patient with the right drug at the right dose at the right time."
More broadly, "personalized
medicine" may be thought of as the tailoring of medical treatment to the individual characteristics, needs, and
preferences of a patient during all stages of care, including prevention,
diagnosis, treatment, and follow-up.”
(FDA, 1 nov 2013)
Changing fields: Personalized Healthcare @ EU
(ESF, 30 Nov 2012) (IMI2, 8 July 2013) (EC, draft Nov 2013)
Personalized Healthcare in a systems view
Source: Barabási 2007 NEJM 357; 4}
• People are different • Different networks and influences • Different risk factors
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Personalized Healthcare in a systems view
Source: Thomas Kelder
Marijana Radonjic
System biology model for Personalized Health(care) (a.k.a. Next Generation Life Sciences)
Ho
meo
sta
sis
A
llo
sta
sis
D
isease
Time
Disease
Health
Personalized Intervention
of patients-like-me
Big Data
Risk profiles of persons-like-me
Molecular Non-molecular Environment …
Personal profile
Selfmonitoring
Adapted from Jan van der Greef (2013)
Personalized Healthcare @ TNO
• Focus on translation to applications in pharma, nutrition
and healthcare
• System biology based
• Maximum use of knowledge from other areas in TNO
• Test added value in real life through field labs
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“Industry as partner”
TNO’s applied biomarker tool box
Widely used preclinical translational models
Pharma, nutrition and chemical industry, academia
Focus on etiology of disease and mechanism of action
Human studies
Experimental medicine through CRO’s
Microdosing
Validated analytical platforms
Metabolomics profiling and targeted analysis, with focus on
lipids, ceramids, cannabinoides
Genomics, transcriptomics, proteomics and imaging through
a wide network of selected partners
Clinical chemistry
Data analysis
Network biology for mechanistic understanding
Multiparameter statistics and chemometrics
PK/PD translational modelling
Comprehensive system dynamics modelling
Biomarker expertise
Best practise strategies and approaches
A wide network with biomarker academia and industry
Metabolic Syndrome
• Atherosclerosis
• Diabetes
• Obesity
• Vascular inflammation
• NASH, fibrosis
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Year 1
Applying lessons learned across fields
e.g. System Biology @TNO
Year 2
Year 3
Personalized Healthcare @ Radboudumc
“It’s far more important
to know what person
the disease has than
what disease the
person has.”
Hippocrates, 400 B.C
“Patient as partner”
Personalized Healthcare @ Radboudumc
People are different Stratification by multilevel diagnosis
+ Patient’s preference of treatment
Exchange experiences in care communities
Select personalized therapy
Centre for Proteomics, Glycomics & Metabolomics
Radboud Proteomics Center
Radboud Metabolomics Group
Radboud Glycomics Facility
Research Biomarkers Diagnostics
Mass spectrometry – NMR based, 20 dedicated fte, part of diagnostic laboratory (Department Laboratory Medicine), close interaction with Radboudumc scientists and external partners
Source: Allison Doerr, Nature Methods 9,36 (2012)
Case1: Glycoproteomics
Personalized Healthcare in rare diseases
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• 12 families with liver disease and dilated cardiomyopathy (5-20 years)
• Initial clinical assessment didn’t yield clear cause of symptoms
• Specific sugar loss of serum transferrin identified via glycoproteomics
• Genetic defect in glycosylation enzyme identified via exome sequencing
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile transferrin applied as diagnostic test (MS-based)
{Dirk Lefeber et al,
accepted NEJM 2013}
Dietary intervention
Incomplete glycosylation Complete glycosylation
ChipCube-LC- Q-tof MS
Case 2: Untargeted metabolomics
A typical plasma sample by Q-tof MS metabolomics analysis shows
Current: • Analytics possible in biotech and in biomedical research • High detail analysis of intact proteins (single or complex) • 40 subunits in 1 complex well doable (up to 100 proteins possible) • 50 fmol of protein complex enough (about 1 g)
Near future: • Decrease amount needed • Protein complex analysis in biological samples • Genetic/environment effects on complex composition and dynamics • Diagnostics ?
Genetics
Bioinformatics Preclinical
pharmacology
Clinical trials
Flow cytometry
Cleanrooms
Neuroscience unit
Robotic operations
Preclinical Imaging
Microscopy
Malaria lab Biobank
Big Data
Radboudumc Technology Centers
Proteomics Metabolomics
Glycomics
Radboudumc Technology
Centers
Maximize synergy within Radboudumc and with external partners / organisations
Eg. Next Generation Life Sciences
Translational medicine @ Radboudumc
Issue 1: A problem in biomarker land
Imbalance between biomarker discovery and application.
• Gap 1: Strong focus on discovery of new biomarkers, few biomarkers progress beyond initial publication to multi-center clinical validation.
• Gap 2: Insufficient demonstrated added value of new clinical biomarker and limited development of a commercially viable diagnostic biomarker test.
Discovery Clinical validation/confirmation
Diagnostic test
Number of biomarkers
Gap 1
Gap 2
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The innovation gap in biomarker research & development
Some numbers
Data obtained from Thomson Reuters Integrity Biomarker Module (April 2013)
Alzheimer’s Disease
Chronic Obstructive Pulmonary Disease
Type II Diabetes Mellitis
Eg Biomarkers in time: Prostate cancer May 2011: 2,231 biomarkers Nov 2012: 6,562 biomarkers Oct 2013: 8,358 biomarkers
EU: CE marking
USA: LDT, 510(k), PMA
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Shared biomarker research through open innovation
We need to set up a open innovation network to share biomarker knowledge and jointly develop and validate biomarkers (at level of NL and EU):