This document discusses biomarkers in personalized healthcare and medicine. It is presented by Professor Alain van Gool, who has experience in both academia and the pharmaceutical industry researching biomarkers and personalized health. The presentation discusses the evolving role of biomarkers from diagnosis to personalized healthcare and medicine. It emphasizes the need for a systems approach using multi-omics data to develop personalized diagnostics and combination therapies for complex diseases like diabetes. Personalized healthcare is about tailoring treatment to each individual based on their molecular profiling and preferences.

1. Biomarkers in Personalized Health(care),
from Discovery to Clinical Diagnostics
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
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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)
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4. 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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5. 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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6. Source: 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
• Better therapies following Personalized Medicine strategies are needed
Need for Personalized Medicines
Analysis of 108 failures in phase II
Reason for failure Therapeutic area
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7. EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
System biology in: Diagnosis Prognosis Treatment Monitoring
People are complex biological systems which requires a systems biology approach

8. Metabolic health and disease
Type 2 Diabetes
Diabetes complications
An increasing health problem globally !
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9. 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
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10. 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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11. 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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12. 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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13. 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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14. 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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15. 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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16. 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 and/or Nutrition?
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17. EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Relating Tissue pharmacology – Biomarker - Therapy
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18. Potential synergy of Pharma-Nutrition
Ongoing: Shared Innovation Programs through public-private consortia
Higher efficacy / less side effects
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19. 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
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20. 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)
present
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21. Personalized Healthcare, more than pathways only
Source: Barabási 2007 NEJM 357; 4}
• People are different
• Different networks and influences
• Different risk factors
• Different preferences
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22. Personalized Healthcare in a systems view
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23. Most important in Personalized Healthcare:
Include the patient as partner
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24. A 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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25. 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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26. Radboud university medical center
• Nijmegen, The Netherlands
• Mission: “To have a significant impact on healthcare”
• Strategic focus on Personalized Healthcare through “the
patient as partner”
• Core activities:
• Patient care
• Research
• Education
• 11.000 colleagues
• 52 departments
• 3.300 students
• 1.000 beds
• First academic centre outside US to fully implement EPIC

27. Personalized Healthcare @ Radboudumc
People are different Stratification by multilevel diagnosis
+
Patient’s preference of treatment
Exchange experiences in
care communities Select personalized therapy
Population
Patient
Molecule
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28. 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
Data
stewardship
Molecule
Flow
cytometry
(Aug 2014)
About 250 dedicated people working in 17 Technology Centers, ~1500 users (internal, external), ~130 consortia
www.radboudumc.nl/research/technologycenters/
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29. Personalized genomic diagnostics
{Nature, July 17 2014, 511: 344-}
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30. Human
samples
Plasma, CSF (urine)
Controls vs. patient
QTOF Mass Spectrometry
- Reverse phase liquid chromatography
- Positive and negative mode
- Features
XCMS
Alignment
Peak comparison
> 10,000 Features
Personalized metabolic diagnostics
Xanthine Uric acid
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31. Example of omics-enabled 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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Personalized self-monitoring

33. Need to change development process for Personalized
Healthcare therapies
• Randomized Clinical Trials won’t be good enough (= groups)
• n=1 clinical trial designs needed whereby:
• Multiple monitoring in same person
• Use different types of biodata (molecular, non-molecular)
• Normalize data per individual
• Combine separate data through meta-analysis
• Output:
• Responders vs non-responders
• Tight data per subgroup
• Clear conclusions on therapy
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34. healthy disease disease +
treatment
Different trial outcomes in Personalized Healthcare
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100%
Normalisation Subgroups

35. H2020 PHC1 application - L’Homme Machine: Exploiting Industrial Control Techniques for Personalized Health
Partners
Biobanks
Databank
Coordinator: prof Lutgarde Buydens,

36. 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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37. Translation is key in Personalized Healthcare !
Personal profile data
Knowledge
Understanding
Decision
Action
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38. Translation 1: Data to usable tests
• 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
Biomarker Innovation Gap
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39. Some numbers
Data obtained from Thomson Reuters Integrity Biomarker Module
Eg Biomarkers in time: Prostate cancer
May 2011: 2,231 biomarkers
Nov 2012: 6,562 biomarkers
Oct 2013: 8,358 biomarkers
25 Sep 2014: 9,975 biomarkers with
31,403 biomarker uses
EU: CE marking
USA: LDT, 510(k), PMA

40. Translation 2: Science to patient
“I’m afraid you’re
suffering from an
increased IL-1”
Adapted from:
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41. Personalized Health(care) model
Homeostasis Allostasis Disease
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)
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42. Person-centered Health(care)
Ways forward:
• Patients included
• Participation + collaboration
• Personal profiles
• System biology
• Health informatics
• Personal preferences
• Personalized therapies in
Lifestyle + Nutrition + Pharma
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43. 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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45. Patient
Radboud
Personalized Healthcare
A significant impact
on healthcare
Molecule
Population
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