1. Assessing the value of biomarkers
in personalized healthcare
Professor in Personalized Healthcare
Head Radboud Center for Proteomics, Glycomics
and Metabolomics
Coordinator Radboud Technology Centers
Head Biomarkers in Personalized Healthcare
Prof Alain van Gool

2. My mixed perspectives in personalized health(care)
8 years academia (NL, UK)
(molecular mechanisms of disease)
13 years pharma (EU, USA, Asia)
(biomarkers, Omics)
3 years applied research institute (NL, EU)
(biomarkers, personalized health)
3 years med school (NL)
(personalized healthcare, Omics, biomarkers)
A person / citizen / family man
(adventures in EU, USA, Asia)
1991-1996 1996-1998 2009-2012
1999-2007 2007-2009 2009-2011
2011-now
2011-now
2
ACI Biomarker Utilisation & Commercialisation
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10-11 September 2014
Alain van Gool

3. Biomarkers in Personalized Healthcare
an evolving role
• From only diagnosis
• To Translational Medicine
• To Personalized/Stratified/Precision Medicine
• To Personalized Healthcare
• To Person-centered Health(care)
ACI Biomarker Utilisation & Commercialisation
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Alain van Gool
3

4. Biomarkers in Personalized Healthcare in the early days
{Kumar and van Gool, RSC, 2013}
1506:
The urine wheel
Use color, smell and taste of
urine to diagnose disease and
decide best treatment
Ullrich Pinder
Epiphanie Medicorum
ACI Biomarker Utilisation & Commercialisation
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Alain van Gool

5. Biomarkers in Translational Medicine in pharma
• Translational medicine
 Exposure
 Mechanism
 Efficacy
 Safety
• Personalized medicine
 Diagnosis
 Prognosis
 Response prediction
• Tools for data-driven decision making
 Biologically relevant
 Clinically accepted
 Quantitative
 Different analytes/types
 Fit-for-purpose application
{Source: Van Gool et al, Drug Disc Today 2010}
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6. Biomarker data-driven decisions
Target engagement? Effect on disease?
yes yes !
no no
• No need to test current
drug in large clinical trial
• Need to identify a more
potent drug
• Concept may still be
correct
• Concept was not correct
• Abandon approach
• Proof-of-Concept
• Proceed to full
clinical
development
“Stop early, stop cheap”
“More shots on goal”
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7. Source: John Arrowsmith: Nature Reviews Drug Discovery 2011
• Success rates of clinical proof-of-concept have dropped from 28% to 18%
• Insufficient efficacy as the most frequent reason
• Targeted therapy through Personalized Medicine may be the solution
Promise of Personalized Medicine
Analysis of 108 failures in phase II
Reason for failure Therapeutic area
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Alain van Gool
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8. Biomarkers in Personalized Medicine
• Melanoma – targeted medicine
• Metabolic health – system medicine
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9. Clinical efficacy of Vemurafenib (PLX-4032, Zelboraf)
Key biomarkers:
Stratification: BRAFV600E mutation
Mechanism: P-ERK
Cyclin-D1
Efficacy: Ki-67
18FDG-PET, CT
Clinical endpoint: progression-free survival (%)
{Source: {Source: Chapman et al, NEJM 2011} Flaherty et al, NEJM 2010}
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10. Clinical efficacy of Vemurafenib
{Wagle et al, 2011, J Clin Oncol 29:3085}
Before Rx Vemurafenib, 15 weeks Vemurafenib, 23 weeks
• Strong initial effects vemurafenib
• Emerging drug resistancy
• Reccurence of aggressive tumors
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11. Tumor tissue/biomarker heterogeneity
• BRAFV600D/E is driving mutation
• However, also no BRAFV600D/E
mutation found in regions of
primary melanomas
• Molecular heterogeneity in
diseased tissue
• Biomarker levels in tissue vary
• Biomarker levels in body fluids
will vary
• Major challenge for
(companion) diagnostics
{Source: Yancovitz, PLoS One 2012}
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12. Metabolic health and disease
Type 2
Diabetes
Diabetes complications
time
ACI Biomarker Utilisation & Commercialisation London 10-11 September 2014 Alain van Gool
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13. Systems view on metabolic health and disease
β-cell
Pathology
gluc
Risk factor
{Source: Ben van Ommen, TNO}
Visceral adiposity
LDL elevated
Glucose toxicity
Fatty liver
Gut inflammation
endothelial
inflammation
systemic Insulin resistance
Systemic
inflammation
Hepatic IR
Adipose IR
Muscle metabolic
inflexibility
adipose
inflammation
Microvascular damage
Myocardial
infactions
Heart failure
Cardiac
dysfunction
Brain disorders
Nephropathy
Atherosclerosis
β-cell failure
High cholesterol
High glucose
Hypertension
dyslipidemia
ectopic lipid overload
Hepatic
inflammation
Stroke
IBD
fibrosis
Retinopathy
Chronic Stress
Disruption
circadian rhythm
Parasympathetic tone
Sympathetic
arousal
Gut
activity
Inflammatory
response
Adrenalin
Heart rate
Heart rate variability
High cortisol
α-amylase
ACI Biomarker Utilisation & Commercialisation
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10-11 September 2014
Alain van Gool
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14. Systems view on metabolic health and disease
β-cell Pathology
gluc Risk factor
{Source: Ben van Ommen, TNO}
Visceral
adiposity
LDL elevated
Glucose toxicity
Fatty liver
Gut
inflammation
endothelial
inflammation
systemic
Insulin resistance
Systemic
inflammation
Hepatic IR
Adipose IR
Muscle metabolic
inflexibility
adipose
inflammation
Microvascular
damage
Myocardial
infactions
Heart
failure
Cardiac
dysfunction
Brain
disorders
Nephropathy
Atherosclerosis
β-cell failure
High cholesterol
High glucose
Hypertension
dyslipidemia
ectopic
lipid overload
Hepatic
inflammation
Stroke
IBD
fibrosis
Retinopathy
Chronic Stress
Disruption
circadian rhythm
Parasympathetic
tone
Sympathetic
arousal
Gut
activity
Inflammatory
response
Adrenalin
Heart rate
Heart rate
variability
High cortisol
α-amylase
{Nakatsuji, Metabolism 2009}
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Alain van Gool
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15. EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Systems view on metabolic health and disease
β-cell
Pathology
gluc
Risk factor
{Source: Ben van Ommen, TNO}
therapy
Visceral
adiposity
LDL elevated
Glucose toxicity
Fatty liver
Gut
inflammation
endothelial
inflammation
systemic
Insulin resistance
Systemic inflammation
Hepatic IR
Adipose IR
Muscle metabolic
inflexibility
adipose inflammation
Microvascular
damage
Myocardial
infactions
Heart
failure
Cardiac dysfunction
Brain
disorders
Nephropathy
Atherosclerosis
β-cell failure
High cholesterol
High glucose
Hypertension
dyslipidemia
ectopic
lipid overload
Hepatic inflammation
Stroke
IBD
fibrosis
Retinopathy
Physical inactivity
Caloric excess
Chronic Stress
Disruption circadian rhythm
Parasympathetic
tone
Sympathetic arousal
Worrying
Hurrying
Endorphins
Gut activity
Sweet & fat foods
Sleep disturbance
Inflammatory
response
Adrenalin
Fear
Challenge
stress
Heart rate
Heart rate variability
High cortisol
α-amylase
Lipids, alcohol, fructose
Carnitine, choline
Stannols, fibre
Low glycemic index
Epicathechins
Anthocyanins
Soy
Quercetin, Se, Zn, …
Metformin
Vioxx
Salicylate
LXR agonist
Fenofibrate
Rosiglitazone
Pioglitazone
Sitagliptin
Glibenclamide
Atorvastatin
Omega3-fatty acids
Pharma
Nutrition
Lifestyle
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Alain van Gool
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16. EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Challenging metabolic equilibrium by Pharma-Nutrition
Age-matched “healthy” control group
t=16 w
(sampling)
t=9 w
t=0
Induction of Diabetes
intervention period
High-fat (HF) diet
High-fat diet “diseased” control group
Nutrition/Life style switch
HF + Drug 1
HF + Drug 2
HF + Drug 3
….
HF + Drug 10
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17. EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
clinical chemistry
System networks
Metabolome
Transcriptome
fluxes
Analysis: high throughput, multi organ, multi level
High-end data mining and warehousing
Extensive histological and molecular phenotyping
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18. EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Effects on total adipose tissue weight
Full reversal of obese phenotype by Nutrition switch, not by all drug treatments
T0901317 (LXR agonist) also reverses obese phenotype
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19. EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Effects on atherosclerosis
Still increased atherosclerosis in Nutrition switch group
T0901317 (LXR agonist) strongly induces atherosclerosis
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20. EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
{Nolan, Lancet 2011}
A sure need for systems medicine
•Multiple interactions and flexibilities in human system (tissues, cells, proteins)
•Blocking one pathway will shift equilibrium and create new problems
•System medicine approach needed for maximal effect
•High value of biomarkers but how to translate to combination therapy?
•Pharma-Nutrition?
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21. EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Relating tissue pharmacology – biomarker - therapy
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22. Personalized interventions by Pharma-Nutrition
Ongoing: Shared Innovation Programs through public-private consortia
Higher efficacy / less side effects
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23. Data
mining
Models
Modelling
Analytics (Mx, Px, Tx)
Organ-on-
a-chip
Imaging
Academic/ Clinical
Industry
Shared Innovation Programs
20+ partners
Diagnostics
Pharma
Nutrition
20+ partners
Better diagnosis and interventions
Personalized !
20+ partners
10+ partners
ACI Biomarker Utilisation & Commercialisation
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10-11 September 2014
Alain van Gool
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24. Biomarkers in Personalized Healthcare
an evolving role
• From only diagnosis
• To Translational Medicine
• To Personalized/Stratified/Precision Medicine
• To Personalized Healthcare
• To Person-centered Health(care)
ACI Biomarker Utilisation & Commercialisation
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10-11 September 2014
Alain van Gool
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25. Changing world: Personalized Medicine@ 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, October 2013)
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10-11 September 2014
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26. A changing world: Personalized Medicine
@Europe
European Science Foundation
30 Nov 2012
Innovative Medicine Initiative 2
8 July 2013
EC Horizon2020
10 Dec 2013
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27. Patient
Radboud
Personalized Healthcare
A significant impact
on healthcare
Molecule
Population
Personalized Healthcare @ Radboud university medical
center
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28. Personalized Healthcare @ Radboudumc
People are different Stratification by multilevel diagnosis
+
Patient’s preference of treatment
Exchange experiences in
care communities
Select personalized therapy
Population
Man
Molecule
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29. Translational medicine @ Radboudumc
SelectBio Biomarkers 2014
Cambridge
8 July 2014
Alain van Gool

30. Biomarkers in Personalized Diagnostics
{Nature, July 17 2014, 511: 344-}
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31. Translation is key in Personalized Healthcare !
Personal profile data
Knowledge
Understanding
Decision
Action
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32. Translation in Personalized Healthcare
“I’m afraid you’re
suffering from an
increased IL-1”
Adapted from:
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33. Need for interdisciplinary team work
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34. (Aug 2014)
Genomics
Bioinformatics
Animal
studies Translational
neuroscience
Image-guided
treatment
Imaging
Microscopy
Biobank
Health
economics
Mass
Spectrometry
Radboudumc
Technology
Centers
Investigational
products
Clinical
EHR-based trials
research
Statistics
Human
physiology
About 250 dedicated people working in 17 Technology Centers, ~1500 users (internal, external), ~130 consortia
www.radboudumc.nl/research/technologycenters/
Data
stewardship
Molecule
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Flow
cytometry

35. Cross-technology interactions
SelectBio Biomarkers 2014
Cambridge
8 July 2014
Alain van Gool

36. Biomarkers in Personalized Healthcare
• 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
ChipCube-LC- Q-tof MS
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test
• Genetic defect in glycosylation enzyme (PGM1) identified via exome sequencing
{Tegtmeyer et al, NEJM 370;6: 533 (2014)}
Genomics Glycomics Metabolomics
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37. healthy disease disease +
treatment
Identify subpopulations in Personalized Healthcare
healthy disease disease +
treatment
• Biomarkers in populations often have a wide range
• Within this range, subpopulations can behave quite differently
• Chemometric methods dealing with multiple biomarker data points are needed to
define subgroups
• Leading to n=1 clinical trials
(Source: Jasper Engel, Lionel Blanchet, Udo Engelke, Ron Wevers and Lutgarde Buydens)
37
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38. H2020 PHC1 application - L’Homme Machine: Exploiting Industrial Control Techniques for Personalized Health
Partners
Biobanks
Databank
Coordinator: prof Lutgarde Buydens,

39. Biomarkers in Personalized Healthcare
an evolving role
• From only diagnosis
• To Translational Medicine
• To Personalized/Stratified/Precision Medicine
• To Personalized Healthcare
• To Person-centered Health(care)
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40. Selfmonitoring
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41. The future is nearly there …
41
Personalized advice
Action
Selfmonitor
Cloud
Lifestyle
Nutrition
Pharma

42. Biomarkers in Person-centered Health(care)
Patient
Caregiver
Insurer
Self-monitoring
Patient
Caregiver
Insurer
Participatory
research
Bas Bloem
Marten Munneke
et al
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43. Biomarkers in Personalized Healthcare
an evolving role
• From only diagnosis
• To Translational Medicine
• To Personalized/Stratified/Precision Medicine
• To Personalized Healthcare
• To Person-centered Health(care)
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44. However …
Knowledge and Innovation gap:
1. What to measure?
2. How much should it change?
3. What should be the follow-up for me?
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45. Biomarker innovation gap
• Imbalance between biomarker discovery, validation and application
• Many more biomarkers discovered than available as diagnostic test
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
ACI Biomarker Utilisation & Commercialisation
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46. 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
10 Sep 2014: 9,975 biomarkers with
31,403 biomarker uses
EU: CE marking
USA: LDT, 510(k), PMA
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47. Reasons for biomarker innovation gap
• Not one integrated pipeline of biomarker R&D
• Publication pressure towards high impact papers
• Lack of interest and funding for confirmatory biomarker studies
• Hard to organize multi-lab studies
• Biology is complex on organism level
• Data cannot be reproduced
• Bias towards extreme results
• Biomarker variability
• …
{Source: John Ioannidis, JAMA 2011}
{Source: Khusru Asadullah, Nat Rev Drug Disc 2011}
47
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48. Way forward: shared innovation network projects
Standardisation, harmonisation,
knowledge sharing needed in:
1. Assay development
2. Clinical validation
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49. Open Innovation Networks
(Next Generation Life Science)
(Source: Model TNO’s Holst Center)
Old New
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50. Biomarker Development Center (Netherlands)
STW perspectief grant
Biomarker Development Center
Public-private partnership 4 years
Project grant €4.3M of which € 2.2M government,
and € 2.1M industry (€ 0.9M cash/ € 1.2M kind)
Close interactions with:
- Clinicians (biomarker application)
- Industry partners and stakeholders
- Patient stakeholder associations
Open slot!
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51. Personalized Healthcare
Ways forward:
• Patients included
• Participation + collaboration
• Selfmonitoring
• Personal profiles
• System biology
• (Big) Data sharing
• Personal preferences
• Personalized therapies
• Lifestyle + Nutrition + Pharma
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52. Acknowledgements
Lucien Engelen
Jan Kremer
Paul Smits
Maroeska Rovers
Nathalie Bovy
Ron Wevers
Jolein Gloerich
Hans Wessels
Dirk Lefeber
Leo Kluijtmans
Bas Bloem
Marten Munneke
and others
Lutgarde Buydens
Jasper Engel
Jeroen Jansen
Geert Postma
and others
Members of the
Radboud umc Personalized Healthcare Taskforce (2013)
Radboud umc Technology Centers (2014)
alain.vangool@tno.nl
alain.vangool@radboudumc.nl
www.linkedIn.com
Many external collaborators
Jan van der Greef
Ben van Ommen
Peter van Dijken
Bas Kremer
Lars Verschuren
Marijana Radonjic
Thomas Kelder
Robert Kleemann
Suzan Wopereis
Ton Rullmann
William van Dongen
and others
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