Review of the molecular innovations in personalized healthcare, reporting back from the Exponential Medicine 2014 conference in San Diego.

1. Personalized Healthcare,
a molecular view in the (near) future
Impressions from the Exponential Medicine 2014 conference
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
Skipr-99 Masterclass
‘Disruptive innovatie & e-health’
Arnhem, 11 Dec 2014

2. My mixed perspectives in Personalized Healthcare
8 years academia (NL, UK)
(molecular mechanisms of disease)
13 years pharma (EU, USA, Asia)
(biomarkers, Omics)
3 years university medical center (NL)
(personalized healthcare, Omics, biomarkers)
3 years applied research institute (NL)
(biomarkers, personalized health)
A family man (NL, Europe, Asia, USA)
1991-1996 1996-1998 2009-2012
1999-2007 2007-2009 2009-2011
2011-now
2011-now
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3. My professional passions
Personalized Health(care)
Biomarkers
Molecular Profiling (Omics)
Future of medicine
3

4. Patient
Radboud
Personalized Healthcare
A significant impact
on healthcare
Molecule
Population
4

5. Working in complex human biological systems
requires a systems biology approach
System biology in:
Diagnosis
Prognosis
Treatment
Monitoring

6. 6

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

10. 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

11. 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

12. Next in Next Generation Sequencing
• Trends:
₋ Further reduction in sequencing costs
Georg Church, Craig Venter

13. Exponential development of Next Generation Sequencing
E Mardis. Nature 2011
Costs per base
(towards a $1,000 dollar genome - 2015?)
2001:
1.5 genome/year
300.000.000 USD/genome
Nov 2014:
100.000 genomes/year
1.400 USD/genome

14. Genome sequencing will become much cheaper !

15. 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
Georg Church, Craig Venter
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

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
• Sequence 1 billion people in 2020
• Sharing genomes through Personalgenomes.org
• Longevity (sequencing very old people to identify rare protective alleles)
• Synthetic life, teleporting for vaccinations at remote places
• Micobiome sequencing
Georg Church, Craig Venter

17. The gut microbiome

18. The future?
• 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

19. Selfmonitoring
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20. Digital medicine

21. Demo room

22. The future is nearly here …
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Personalized advice
Action
Selfmonitor
Cloud
Lifestyle
Nutrition
Pharma

23. The future is nearly here …
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Measure your brain waves (EEG)
Recognize conditions for maximal
concentration or relaxation.
Use device to train.

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

28. Translation is key in Personalized Healthcare !
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Personal profile data
Knowledge
Understanding
Decision
Action

29. Translation is key in Personalized Healthcare !
“I’m afraid you’re
suffering from an
increased IL-1β and
an aberrant miR843
expression”
Adapted from:
29
?

30. 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
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31. Biomarker innovation gap: some numbers
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Data from Thomson Reuters Integrity, 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
Nov 2014: 10,350 biomarkers with
33,863 biomarker uses

32. How to move forward? 1. 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
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

33. EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Relating tissue pharmacology – biomarker - therapy

34. Translating knowledge to field labs
1. Implementation-plan ‘Personalized diagnosis of (pre)diabetic and their lifestyle treatment in Dutch Health care’.
2.Use of Oral Glucose Tolerance Test as a stratification biomarker for (pre)diabetic patients
3. Advice a tailored treatment (lifestyle and/or medical)
4. Monitor added value of stratification
5. Communicate results and lessons learned
Being implemented in
1st line care
(region Hillegom,
Netherlands)
Alliance “Expedition Sustainable Care, starting with diabetes”

35. Personalized interventions by Pharma-Nutrition
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Ongoing: Shared Innovation Programs through public-private consortia
Higher efficacy / less side effects

36. How to move forward? 2. Interdisciplinary team work
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37. www.radboudumc.nl/research/technologycenters
How to move forward? 2. Interdisciplinary team work

38. 38
• 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/

39. 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
Biomarker Development Center
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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