View on personalized health(care), moving beyond just targeted medicine.

1. Personalized health(care), moving
beyond just targeted medicine
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
Oudejaarssymposium “Personalized Healthcare”
Groningen
6 January 2015

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)
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. Biomarkers in Personalized Healthcare
an evolving role
• From only diagnosis
• To Translational Medicine
• To Personalized/Precision/Targeted Medicine
• To Personalized Healthcare
• To Person-centered Health(care)
3

4. Diagnostic biomarkers 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
4

5. 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
5

6. 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}
6

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
• Promising examples in oncology
Promise of Personalized Medicine
Analysis of 108 failures in phase II
Reason for failure Therapeutic area
8

8. Case study: Development BRAF inhibitors for melanoma
{Miller and Mihm, 2006}
Rationale:
• BRAF mutation first
event in melanoma
• Mutation makes
BRAF kinase
constitutively active
• BRAF-MEK-ERK
pathway leads to
cell proliferation
• Inhibition BRAF
pathway leads to
tumor cell death
• High frequency
BRAF mutation in
melanoma
9

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: Flaherty et al, NEJM 2010}{Source: Chapman et al, NEJM 2011}
10

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
11

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}
12

12. ‘Complicating’ factors in oncology therapy
Source: 11 Sept 2013 @de Volkskrant
• Biological clock
• Smoking
• Pharma-Nutrition
• Drug-drug interaction
• Alternative medicine
• Genetic factors
• …
Interview with Prof Ron Matthijssen, ErasmusMC, Rotterdam
13

13. 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

14. Biomarkers in Personalized Healthcare
an evolving role
• From only diagnosis
• To Translational Medicine
• To Personalized/Precision/Targeted Medicine
• To Personalized Healthcare
• To Person-centered Health(care)
present
15

15. Personalized Healthcare, more than pathways only
Source: Barabási 2007 NEJM 357; 4}
• People are different
• Different networks and influences
• Different risk factors
• Different preferences
16

16. Personalized Healthcare in a systems view
17

17. 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)
18

18. A changing world: Personalized Medicine @Europe
European Science Foundation
30 Nov 2012
Innovative Medicine Initiative 2
8 July 2013
EC Horizon2020
10 Dec 2013
19

19. Most important in Personalized Healthcare:
Include the patient as partner
20

20. Patient
Radboud
Personalized Healthcare
A significant impact
on healthcare
Molecule
Population
Personalized Healthcare @Radboudumc
21

21. 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
22

22. 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
23

23. Personalized genomic diagnostics
{Nature, July 17 2014, 511: 344-}
24

24. 2012
Patient
Targeted
Metabolic
screen
Targeted
gene
analysis
Diagnosis
+ follow-up
2013 / 2014
Patient
Whole
exome
sequencing
Targeted
confirmatory
metabolite +
enzyme
testing
Diagnosis
+ follow-up
Targeted assays vs holistic approach
Next
generation
metabolic
screening
Times are changing… add functional genome diagnostics
25

25. 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
26
Full metabolite profile:
Highly suspected of
xanthinuria

26. 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
Biomarker development workflow @ Radboudumc
*CCKL accreditation/RvA/EFI
www.laboratorymedicine.nl
27

27. 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
28

28. Biomarkers in Personalized Healthcare
an evolving role
• From only diagnosis
• To Translational Medicine
• To Personalized/Precision/Targeted Medicine
• To Personalized Healthcare
• To Person-centered Health(care)
next
29

29. 30

30. Selfmonitoring
31

31. The future is nearly here …
Personalized advice
Action
Selfmonitor
Cloud
Lifestyle
Nutrition
Pharma
32

32. The future is nearly here …
Measure your brain waves (EEG)
Recognize conditions for maximal
concentration or relaxation.
Use device to train.
33

33. The future is nearly here …
• 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
34

36. But …
Knowledge and Innovation gap:
1. What to measure?
2. How much should it change?
3. What should be the follow-up for me?
37

37. 38

38. Translation is key in Personalized Healthcare !
Personal profile data
Knowledge
Understanding
Decision
Action
39

39. Translation is key in Personalized Healthcare !
“I’m afraid you’re
suffering from an
increased IL-1β and
an aberrant miR843
expression”
Adapted from:
40
?

40. 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
41

41. Biomarker innovation gap: some numbers
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
42

42. How to move forward? 1. System Biology
43
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

43. EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Relating tissue pharmacology – biomarker - therapy
44

44. 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”
45

45. Personalized interventions by Pharma-Nutrition
Ongoing: Shared Innovation Programs through public-private consortia
Higher efficacy / less side effects
46

46. How to move forward? 2. Interdisciplinary team work
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
47

47. www.radboudumc.nl/research/technologycenters
How to move forward? 2. Interdisciplinary team work
48

48. 49
• 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/

49. How to move forward? 2. Interdisciplinary team work
50

50. How to move forward? 2. Interdisciplinary team work
Patient
Caregiver
Insurer
Self-monitoring
Patient
Caregiver
Insurer
Participatory
research
Bas Bloem
Marten Munneke
et al
51
Central
data point

51. Personalized Health(care) model
HomeostasisAllostasisDisease
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)
52
Personalized
Participatory
Pre-emptive

52. Personalized Health(care)
Ways forward to the Future of Medicine:
• Patients included
• Molecular profiling
• Personal preferences
• System biology
• Personal profiles
• Health informatics
• Personalized therapies by
Lifestyle + Nutrition + Pharma
53

53. 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
54