View how to enable personalzied healthcare through integrated technolgies, with great challenges to bridge the innovation gaps in biomarkers.

1. Personalized health(care) through
integrated technologies
Opening EATRIS Finland
Helsinki
11 March 2015
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 background
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 university medical center (NL)
(personalized healthcare, Omics, biomarkers)
1991-1996 1996-1998 2009-2012
1999-2007 2007-2009 2009-2011
2011-now
2011-now
2

3. Radboud university medical center
• Nijmegen, The Netherlands
• Mission: “To have a significant impact on healthcare”
• Strategic focus on Participatory and 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
3

4. Takehome message
• Strategic focus on implementing Personalized Healthcare
• Strong technological and methodological infrastructure
• Continuous exploration of functional networks
4

5. Patient
Radboud
Personalized Healthcare
A significant impact
on healthcare
Molecule
Population
5

6. Personalized Healthcare @ Radboudumc
People are different Stratification by multilevel diagnosis
+Patient’s preference of treatment
Exchange experiences in
care communities
Select personalized therapy
6

7. Personalized diagnostics @ Radboudumc
7

8. Personalized genomic diagnostics
{Nature, July 17 2014, 511: 344-}
8

9. 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
9

10. 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
10
Full metabolite profile:
Highly suspected of
xanthinuria

11. Research Biomarkers Diagnostics
Department of Laboratory Medicine, Radboudumc
Integrated Translational Research and Diagnostic Laboratory, 220 fte, yearly budget ~ 28M euro.
Close interaction with Departments 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
*CCKL accreditation/RvA/EFI
www.laboratorymedicine.nl
11
Diagnostic power in departments:
Example: Department of Laboratory Medicine

12. Orientation across the spectrum
from molecule to man to population
Orientationacross
thespectrumofdiseases
PI
Research theme
TechnologyCenters
Research support by Technology Centers
12

13. Radboudumc Technology Infrastructure
Get organised:
1. What technological expertise do we have and should we have ?
2. How should we organise this ?
3. How will we communicate this ?
Activities:
• Make inventories on current state and desired future state.
• Work with technology coordinators + departments (research, clinical, strategy,
communication , valorisation).
• Include input from research themes.
• Organize monthly full team meetings + many 1:1 meetings.
• Discussed output with research institutes, executive board.
• Implementation structure 1.0 by 1H2014. Improve in version 2.0 1H2015.
13

14. External role
Internal role
• Knowledge hub for technological expertise
• Maximise use of available technical capabilities and knowledge (‘duurzaamheid’)
• Advise scientists with technological expertise
• Advise management on strategic investments and opportunities
• Drive innovations by working with each other, theme’s and Valorisation
• Easy access to Radboudumc’s technological expertise
• Represent Radboudumc as one in external technology networks
• Increase funding (grants, contract research) with Valorisation
Internal / external role
Radboudumc Technology Centers

15. Technology Platforms UMC St Radboud
(Potential)
Technology
Platforms
Genomics
RPC
CMBI
PRIME
MIC
CDL
CRCN
Radboud
BiobankMalaria lab
Flow
cytometry
TR&CT
TNU
MITeC
PDRC
December 2013
15
Inventory phase

16. Radboudumc Technology Centers
Genomics
Bioinformatics Animal
studies
Flow
cytometry
Translational
neuroscience
Image-guided
treatment
Imaging
Microscopy
Biobank
Data
stewardship
Proteomics
Glycomics
Metabolomics
Radboudumc
Technology
Centers
GMP products
Clinical
trials
January 2014
16
Repositioning phase

17. • Align with the needs of the Research and Education, and contribute to
output and quality of those
• Organise each Technology Center as a single portal
• Add other Technology Centers when needed and useful
• Keep improving efficiency and funding
Radboudumc Technology Centers
Improving phase
17
Feb-Oct 2014

18. 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
Mar 2015

19. 19
• 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
• Early 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 250 dedicated people working in 18 Technology Centers, ~1600 users (internal, external), ~140 consortia
www.radboudumc.nl/research/technologycenters/
• iPSC
• Organoids

20. Cross-technology interactions
20

21. www.radboudumc.nl/research/technologycenters/
Combination of
• Science
• Business
• Innovation
• Impact in health

22. Working together on the Radboud campus
(Spin-out)
companies
22

23. Radboud Research Facilities
www.ru.nl/radboudresearchfacilities/
23

24. Working with other Technology Networks
Region, nation, Europe, world
24

25. The EATRIS operational strategy
Consortia of centres of excellence in a 3D matrix model
Experts Product
Platforms
QA & RA
RPM &
Clinical
Legal &
Ethical
compliance
Training & EducationCom & IT
Biomarkers
Group
Vaccine
Group
Tracer &
Imaging
Group
ATMP’s
Group
Small
Molecules
Group
Optimise translational
trajectory
Maximise spillovers
Disease
expertise
Alain
van
Gool
Marien
de
Jonge
Wim
Oyen
Carl
Figdor

26. Apply to personalized healthcare
26

27. Example: Personalized Healthcare in rare disease
• 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
27

28. 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
28

29. 29
“Selfmonitoring = Trend of 2014”The future of medicine

30. ‘insideables’
‘wearables’

31. • 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

32. 32
• Measure your brain waves (EEG)
• Recognize conditions for maximal
concentration or relaxation.
• Use device to train.
DIY brainwave monitoring

33. DIY blood biomarker analysis

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

38. Most important for biomarkers in Personalized Healthcare:
Focus on the end user: the patient
38

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:
39
?

40. 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

41. Translation is key in Personalized Healthcare !
Personal profile data
Knowledge
Understanding
Decision
Action
41

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

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

44. Biomarker innovation gaps
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
44
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
Gap 3

45. How to move forward?
Way forward: shared innovation
Standardisation, harmonisation,
knowledge sharing needed in:
1. Assay development
2. Clinical validation and qualification
45

46. How to move forward?
Start small, think big
46

47. How to move forward?
Collaboration in Health Informatics
47
Lucien Engelen et al, Radboud Reshape Center for Innovation

48. How to move forward?
Be passionate !
My personal drivers:
Personalized Health(care)
Biomarkers
Molecular Profiling (Omics)
Future of medicine
48

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
49
And funders