View how to bridge the gap between scientific biomarker research in academia and point of care diagnostic apps in society.

1. Biomarkers in Personalized Health(care),
moving beyond Targeted Medicine
Professor of Personalized Healthcare
Head Radboud Center for Proteomics, Glycomics
and Metabolomics
Coordinator Radboud Technology Centers
Head Biomarkers in Personalized Healthcare
Prof Alain van Gool

3. 3
Personalized advice
Action
Selfmonitor
Cloud
Lifestyle
Nutrition
Pharma

4. ‘insideables’
‘wearables’

5. 5
“Selfmonitoring = Trend of 2014”The future of medicine

8. Biomarkers in Personalized Health(care)
an evolving role
• From only diagnosis
• To Translational Medicine
• To Personalized/Precision/Targeted Medicine
• To Personalized Healthcare
• To Person-centered Health(care)
present
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9. But …
Knowledge and Innovation gap:
1. What to measure?
2. How much should it change?
3. What should be the follow-up for me?

10. 10

11. Exponential technologies
“The only constant is change,
and the rate of change is
increasing”
We are at the knee
of the exponential curve

12. Buzzwords Progress and beyond
You are the CEO of your own healthcare team
Exponential technologies
Disruptive innovation
Digitalizing yourself
Sitting is the new smoking
Uber-ization of healthcare
The future is already here, it’s just not evenly distributed …
What’s normal for me is not normal for you
Do things different
Don’t think out of the box, just think there is no box !

13. Demo room

14. Exponential developments in biomarker technologies
• Next generation sequencing
• Large level of detail on genome level (DNA, RNA)
• Sequencing per patient is becoming practice
• Allows risk analysis and therapy selection
• Mass spectrometry
• Large level of detail on metabolic level (proteins, metabolites)
• Analysis of blood, urine, cells, tissues, hair, etc all possible
• Allows monitoring of disease and treatment effects
• Imaging
• Large level of detail on intact in vivo level
• Analysis of any tissue, real time
• Allows spatial view of intact organs and organisms

15. Genome sequencing will become much cheaper !

16. Next in Next Generation Sequencing
• Trends:
₋ Further reduction in sequencing costs
₋ Computational power
₋ Machine learning to analyse (big) data
₋ Link molecular diagnosis to cellular therapies
Also beyond the oncology field:
• Volker: Intestinal surgery → XIAP → Cord blood
• Beery twins: Cerebral palsy → SPR → Diet 5HTP
• Wartman: Leukemia → FLT3 → Sunitinib
• Gilbert: Healthy → BRCA → Mas/Ovarectomy
• Snyder: T2Diabetes → GCKR, KCNJ11 → Diet, exercise
• Lauerman: Scotoma, leg → JAK2 → Aspirin
• Bradfield: Healthy → CDH1 → Gastrectomy
Georg Church, Craig Venter

17. The microbiome

18. The epigenome

19. Consider individual differences in biomarker research
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21. 21

22. But …
Knowledge and Innovation gap:
1. What to measure?
2. How much should it change?
3. What should be the follow-up for me?

23. Most important for biomarkers in Personalized Healthcare:
Focus on the end user: the patient
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24. Lab values Clinical
outcomes
Patient important
outcomes
Pain
Pubmed Search query
Critical appraisal tool
Mobility Fatigue
INTEGRATE-HTA
Intervention
Focus on the end user
R van Hoorn, W Kievit, M Tummers, GJ van der Wilt
Clinical
outcomes

25. Translation is key in Personalized Healthcare !
“I’m afraid you’re
suffering from an
increased IL-1β and
an aberrant miR843
expression”
Adapted from:
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?

26. Translation is key in Personalized Healthcare !
Personal profile data
Knowledge
Understanding
Decision
Action
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27. Translation is key in Personalized Healthcare !
Select personalized therapy
Treatment options
Successrates
Example from Prostate cancer patient guide

28. Translation is key in Personalized Healthcare !
Treatment options
Pro’sCon’s
Select personalized therapy

29. Biomarker innovation gaps
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
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Gap 3

30. Biomarker innovation gaps: some numbers
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Data from Thomson Reuters Integrity database, February 2015
Alzheimer’s Disease
Chronic Obstructive
Pulmonary Disease
Type II Diabetes Mellitis

31. Biomarker innovation gaps: some numbers
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
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Gap 3
5 biomarkers/
working day
1 biomarker/
1-3 years
1 biomarker/
3-10 years
?
Eg Biomarkers in time: Prostate cancer
May 2011: n= 2,231 biomarkers
Nov 2012: n= 6,562 biomarkers
Oct 2013: n= 8,358 biomarkers
Nov 2014: n= 10,350 biomarkers

32. How to move forward?
1. Focus on the end user
2. Validate more biomarkers in one go
3. System biology
4. Define, share and act on “Good Biomarker Practices”
5. Build biomarker development pipelines
6. Develop translational DIY technologies
7. Interpret data with self-normalisation
8. Interdisciplinary teamwork
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33. How to move forward?
2. Validate more biomarkers in one go
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1. Determine the context of change in a biomarker.
2. Drive validation of multiple biomarkers at once
Multiple
measures
Patient 1 Patient 2
Technologies are already out there:
• Next generation sequencing
• Microarrays
• Multiplex immunoassays
Single
measure
• Targeted mass spectrometry
• Binding assays
• Mass spec imaging

34. How to move forward?
3. System Biology
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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
activitySweet &
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
Atorvastati
n
Omega3-fatty acids
Pharma
Nutrition
Lifestyle

35. How to move forward?
4. Define, share and act on“Good Biomarker Practices”
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Some items in need of standardisation:
• Reproducibility, quality requirements
• Study design & statistical power
• Variability & heterogeneity
• Specimen acquisition & pre-analytics
• Sample preparation
• Patient & associated clinical data
• Analytical standards & quality control
Not reinvent the wheel.
Standardisation, harmonisation, knowledge sharing needed in:
1. Assay development
2. Clinical validation
3. Biomarker qualification
• Assay/platform development
• Quality system manufacturing
• Data analysis & management
• Regulatory requirements
• Ethical, IP & legal aspects
• Early HTA
• Quality in documentation & publication

36. How to move forward?
5. Build biomarker development pipelines
Standardisation, harmonisation,
knowledge sharing needed in:
1. Assay development
2. Clinical validation
Example: Biomarker Development Center
Open
Innovation
Network !
Roadmap Molecular Diagnostics
PPP Grant 4.3M Euro
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2015/2016

37. Good example of multi-center biomarker validation

38. Research Biomarkers Diagnostics
Department of Laboratory Medicine, Radboudumc
Integrated Translational Research and Diagnostic Laboratory, 220 fte, yearly budget ~ 28M euro.
Close interaction with Dept of Genetics, Pathology and Medical Microbiology
Specialities:
• Proteomics, glycomics, metabolomics
• Enzymatic assays
• Neurochemistry
• Cellulair immunotherapy
• Immunomonitoring
Areas of disease:
• Metabolic diseases
• Mitochondrial diseases
• Lysosomal /glycosylation disorders
• Neuroscience
• Nefrology
• Iron metabolism
• Autoimmunity
• Immunodeficiency
• Transplantation
In development:
• ~500 Biomarkers
• Early and late stage
• Analytical development
• Clinical validation
Assay formats:
• Immunoassay
• Turbidicity assays
• Flow cytometry
• DNA sequencing
• Mass spectrometry
• Experimental human (-ized)
invitro and invivo models for
inflammation and
immunosuppression
Validated assays*:
• ~ 1000 assays
• 3.000.000 tests/year
Areas of application:
• Personalized healthcare
• Diagnosis
• Prognosis
• Mechanism of disease
• Mechanism of drug action
Use available resources:
Biomarker development pipeline @ Radboudumc
*CCKL accreditation/RvA/EFI
www.laboratorymedicine.nl
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39. • DIY sequence your genome and/or your microbiome
genome
• at a provider, at a pharmacy, at home
• Take your genome to the doctor
• Have a personalized healthcare advice
How to move forward?
6. Develop translational DIY technologies

40. 40
• Measure your brain waves (EEG)
• Recognize conditions for maximal
concentration or relaxation.
• Use device to train.
How to move forward?
6. Develop translational DIY technologies

41. How to move forward?
6. Develop translational DIY technologies

42. healthy disease disease +
treatment
How to move forward?
7. Interpret data with self-normalisation
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Subgroups
100%
Normalisation of responders

43. How to move forward?
8. Interdisciplinary team work
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44. www.radboudumc.nl/research/technologycenters
How to move forward?
8. Interdisciplinary team work

45. 45
• Proteins
• Metabolites
• Drugs
• PK-PD
• Preclinical
• Clinical
• Behavioural
• Preclinical
• Animal facility
• Systematic review
• Cell analysis
• Sorting
• Pediatric
• Adult
• Phase 1, 2, 3, 4
• Vaccines
• Pharmaceutics
• Radio-isotopes
• Malaria parasites
• Management
• Analysis
• Sharing
• Cloud computing
• DNA
• RNA
• Internal
• External
• HTA
• Evidence-based
surgery
• Field lab
• Statistics
• Biological
• Structural
• Preclinical
• Clinical
• Economic
viability
• Decision
analysis
• Experimental design
• Biostatistical advice
• Electronic Health Records
• Big Data
• Best practice
• In vivo
• Functional
diagnostics
About 240 dedicated people working in 17 Technology Centers, ~1500 users (internal, external), ~130 consortia
www.radboudumc.nl/research/technologycenters/

46. How to move forward?
Collaboration in Health Informatics
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47. How to move forward?
Start small, think big
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48. Finally, be passionate !
My professional passions:
Personalized Health(care)
Biomarkers
Molecular Profiling (Omics)
Future of medicine
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49. Acknowledgements
Lucien Engelen
Jan Kremer
Paul Smits
Maroeska Rovers
Nathalie Bovy
Ron Wevers
Jolein Gloerich
Hans Wessels
Dirk Lefeber
Leo Kluijtmans
Bas Bloem
and others
Lutgarde Buydens
Jasper Engel
Jeroen Jansen
Geert Postma
and others
www.radboudumc.nl/personalizedhealthcare
www.radboudumc.nl/research/technologycenters
www.Radboudresearchfacilities.nl
alain.vangool@tno.nl
alain.vangool@radboudumc.nl
www.linkedIn.com
Many external collaborators
Jan van der Greef
Ben van Ommen
Bas Kremer
Lars Verschuren
Ivana Bobeldijk
Marjan van Erk
Peter van Dijken
Marijana Radonjic
Thomas Kelder
Robert Kleemann
Suzan Wopereis
and others
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And funders