The document discusses the evolution of personalized healthcare, emphasizing the shift from traditional pharmaceutical approaches to a more integrated paradigm that includes nutrition and biomarker-driven decisions. It highlights the need for tailored treatments and the importance of innovations in life sciences, such as next-generation sequencing and mass spectrometry, to enhance understanding and management of diseases. Key challenges addressed include the gaps in biomarker validation, translation into clinical practice, and the necessity of public-private collaborations to improve health outcomes.

1. Pharma-Nutrition:
From separate silos towards synergy
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 med school (NL)
(personalized healthcare, Omics, biomarkers)
3 years applied research institute (NL, EU)
(biomarkers, personalized health, nutrition)
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
2

3. Outline
3
• Paradigm shifts in pharma
• Personalized Medicine to Personalized Health(care)
• Pharma-Nutrition

4. 4
The development of medicines (past)
• Little understanding of cause of disease
• Use of natural compounds from plant and animal
• Limited testing in laboratory + trial and error in clinic
• Frequently not effacious and/or side effects in patients
• Unacceptable approach (ethical, financial)

5. 5
Example: hormone replacement therapy
• Postmenopausal complaints in women ≥45 years old
• eg hot flushes, loss of concentration and memory
• 1924 First drug for treatment : dried powder of animal ovaria
• Risks of estrogen treatment emerged
• Induction breast and endometrium cancer, cardiovascular risk
• Optimal profile:
• Estrogen-like on CNS and bone
• Anti-estrogen like on breast, endometrium, cardiovascular
• 1929 Discovery of estrogens
• Decrease of estrogens in menopause causes complaints
• Main component of 1924 drug was estrogen
• Estrogen Receptor α (1958) and β (1996), and cofactors
• Needed: Selective Estrogen Receptor Modulators

6. 6
The development of medicines (present)
• A rational and step-wise approach
• ‘reverse pharmacology’
• Cleaner and more specific drugs
which
disease?
mechanism?
drug target?
activity activity
side
effect
activity
production,
marketing
active
compound
(cell)
active, safe
compound
(animal)
safe
compound
(healthy human)
active, safe
compound
(patiënt)
(reumatoid arthritis)
side
effect
side
effect

7. 7
Successes of drug development
Antibiotics Vaccins
Reproductive medicine Oncology

8. 8
The development of medicines (present)
which
disease?
mechanism?
drug target?
activity activity
side
effect
activity
production,
marketing
active
compound
(cell)
active, safe
compound
(animal)
safe
compound
(healthy human)
active, safe
compound
(patiënt)
(reumatoid arthritis)
side
effect
side
effect
• Per marketed drugs: average 14 years R&D at costs of 1.700.000.000 USD
• Return investment of 20% of net income in pharma R&D
60 projects
one
successful
medicine
Translation laboratory → patient
only 1 in 10 projects success

9. Translational Medicine in pharma
{Source: Van Gool et al, Drug Disc Today 2010}
9

10. Biomarker-based translational medicine
• Does the compound get to the site of action?
• Does the compound cause its intended pharmacological/
functional effects?
• Does the compound have beneficial effects on disease or
clinical pathophysiology?
• What is the therapeutic window (how safe is the drug)?
• How do sources of variability in drug response in target
population affect efficacy and safety?
 Exposure ?
 Mechanism ?
 Efficacy ?
 Safety ?
 Responders ?
Source: van Gool et al, Drug Disc Today 2010
Kumar, van Gool, RSC 2013
10
activity
side
effect

11. 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”
11
Source: Kumar, van Gool, RSC 2013

12. Rational selection of best targets and drugs works
The 5R’s assessment:
• Right Target
• Right Tissue
• Right Safety
• Right Patients
• Right Commercial Potential

13. Adopt lessons learned
CarTarDis = Cardiovascular Target Discovery
Public-private partnership, 13 partners, 8 countries, project budget 8.0M Eur
Started 1 Oct 2013 for 4 years
Adopting AstraZeneca’s 5R strategy in drug target selection
(Coordinator)
CarTarDis

14. 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
Need for Personalized Medicine
Analysis of 108 failures in phase II
Reason for failure Therapeutic area
14

15. 15

16. Consider individual differences in life science research
16

17. Source: Chakma Journal of Young Investigators. Vol 16, 2009.
Principle of Personalized/Precision/Targeted Medicine
17

18. Optimal targeted / precision medicine

19. 19
Precision medicine @USA
President Obama
State of Union 2015

20. Subapproaches of Personalized Medicine
20
Diagnosis & prognosis
Dosing Source: Kumar, van Gool, RSC 2013

21. Subapproaches of Personalized Medicine
21
Patient selection
Source: Kumar, van Gool, RSC 2013

22. 22
Paradigm shifts in pharma
• 1990’s Genomics revolution: decipher disease mechanisms
From trial and error to ‘reverse pharmacology’
Translational medicine
• 2000’s Biomarker-driven decision making
From blockbuster model to smaller PoC
Pharma R&D model: from internal to external
• 2010’s Improve diagnosis and knowledge of disease
Personalized (targeted, precision) medicine
• 2020 Elucidate individual health/disease status - Big Data
Combine pharma with other therapies
Personalized Health(care)

23. A changing world: Personalized Medicine @Europe
European Science Foundation
30 Nov 2012
Innovative Medicine Initiative 2
8 July 2013
EC Horizon2020
10 Dec 2013
23

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)
24

25. Exponential developments in life science 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
25

26. Next Generation Sequencing
Good examples personalized medicine in Oncology:
• Cyp450, Her2/neu, BRCA, BRAF, EGFR, EML4/ALK, etc
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

27. Mass spectrometry
• Example: Glycoproteomics in plasma
• Optimized procedure: detection of ~12.000 unique deconvoluted
monoisotopic masses per single analysis (> 50% are glycopeptides)
500
1000
1500
2000
m/z
5 10 15 20 25 30 35 40 Time [min]
Proof of principle study:
Monique van Scherpenzeel, Dirk Lefeber, Hans Wessels, Alain van Gool
Translational Metabolic Laboratory, Radboudumc, unpublished data

28. Imaging
Slide courtesy of Profs Maroeska Rovers, Peter Friedl, Otto Boerman, Radboudumc
Example: Image-guided surgery:
• Use (auto)fluorescence to highlight tumor cells
• Specific removal of tumor tissue
• Extend to other imaging modalities in operation room (eg MRI)

29. Example: Personalized Healthcare in rare diseases
• 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
29

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

31. 31

32. Demo room

33. The epigenome

34. The microbiome

35. 35
Personalized advice
Action
Selfmonitor
Cloud
Lifestyle
Nutrition
Pharma
DIY monitoring of vital signs

36. • 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
DIY sequencing

37. 37
• Measure your brain waves (EEG)
• Recognize conditions for maximal
concentration or relaxation.
• Use device to train.
DIY brainwave monitoring

38. DIY blood biomarker analysis
• Measure key biomarkers in one drop of blood at few $ per test panel
• Download data to your smartphone to monitor your own trend

39. ‘insideables’
‘wearables’

42. 42

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

45. Most important in Personalized Healthcare:
Focus on the end user: the patient
45

46. Translation is key in Personalized Healthcare !
“I’m afraid you’re
suffering from an
increased IL-1β and
an aberrant miR843
expression”
Adapted from:
46
?

47. Translation is key in Personalized Healthcare !
Personal profile data
Knowledge
Understanding
Decision
Action
47

48. Biomarker innovation gaps
48
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Gap 3
1. Imbalance between biomarker discovery, validation and application
2. Many more biomarkers discovered than available as diagnostic test
3. Limited translation to point-of-care devices

49. Biomarker innovation gaps: some numbers
49
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
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Gap 3

50. Interdisciplinary biomarker validation
Standardisation, harmonisation,
knowledge sharing in:
1. Assay development
2. Clinical validation
Biomarker Development Center
Open
Innovation
Network !
Roadmap Molecular Diagnostics
PPP Grant 4.3M Euro
50

51. www.radboudumc.nl/research/technologycenters
Genomics
Bioinformatics
Animal
studies
Stem
cells
Translational
neuroscience
Image-guided
treatment
Imaging
Microscopy
Biobank
Health
economics
Mass
Spectrometry
Radboudumc
Technology
Centers
Investigational
products
Clinical
trials
EHR-based
research
Statistics
Human
physiology
Data
stewardship
Molecule
Flow
cytometry
March 2015

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

53. Translation is key in Personalized Healthcare !
Select personalized therapy
Treatment options
Successrates
Example from Prostate cancer patient guide

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

55. Explore personalized interventions by Pharma-Nutrition
Shared Innovation Programs through public-private consortia
Higher efficacy / less side effects
55

56. Explore personalized interventions by Pharma-Nutrition
Double inhibition
56
No inhibition

57. Next: increase system biology knowledge
57
β-cell Pathology
gluc Risk factor
{Source: Ben van Ommen, TNO}
therapy

58. Next: cross-field collaborations in Pharma-Nutrition
58
Data
mining
Models
Modelling
Analytics
(Mx, Px, Tx)
Organ-on-
a-chip
Imaging
Academic/ Clinical
Industry
20+ partners
Diagnostics
Pharma Nutrition
20+ partners
Better diagnosis and interventions
Personalized !
20+ partners
10+ partners

59. Next: cross-field collaborations in Pharma-Nutrition
59
Mixed diner 12th April:
• Pharma – Nutrition
• Public – Private
• Netherlands - Spain

60. Next: bridge innovations across fields
60
Year 1
TNO’s system biology projects
Year 2
Year 3
Innovation

61. Cost-benefit
analysis
Stakeholder
map
Regulatory
landscape map
Biological
feasibility
Clinical
need/issue
PharmaNutrition
business case
Carlien ter Mors
Laura Han
Jochem Jansen
Next: design sensible Pharma-Nutrition business cases

62. Finally, be passionate !
My professional passions:
Personalized Health(care)
Biomarkers
Molecular Profiling (Omics)
Future of medicine
62

63. Acknowledgements
Ron Wevers
Jolein Gloerich
Hans Wessels
Monique Scherpenzeel
Dirk Lefeber
Leo Kluijtmans
Lucien Engelen
Paul Smits
Maroeska Rovers
Nathalie Bovy
Bas Bloem
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
Slides on slideshare.net/alainvangool
Many collaborators
Jan van der Greef
Ben van Ommen
Bas Kremer
Lars Verschuren
Ivana Bobeldijk
Marjan van Erk
Carina de Jongh
Peter van Dijken
Robert Kleemann
Suzan Wopereis
and others
63
And funders
CarTarDis