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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
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
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
Takehome message
• Strategic focus on implementing Personalized Healthcare
• Strong technological and methodological infrastructure
• Continuous exploration of functional networks
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Patient
Radboud Personalized Healthcare
A significant impact
on healthcare
Molecule
Population
5
Personalized Healthcare @ Radboudumc
People are different Stratification by multilevel diagnosis
+ Patient’s preference of treatment
Exchange experiences in care communities
Select personalized therapy
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Personalized diagnostics @ Radboudumc
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Personalized genomic diagnostics
{Nature, July 17 2014, 511: 344-}
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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
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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
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Full metabolite profile: Highly suspected of xanthinuria
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
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Diagnostic power in departments:
Example: Department of Laboratory Medicine
Orientation across the spectrum from molecule to man to population
Ori
enta
tio
n a
cro
ss
the
spec
tru
m o
f d
isea
ses
PI
Research theme
Te
chn
olo
gy
C
ente
rs
Research support by Technology Centers
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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.
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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
Technology Platforms UMC St Radboud
(Potential) Technology Platforms
Genomics
RPC
CMBI PRIME
MIC
CDL
CRCN
Radboud Biobank Malaria lab
Flow cytometry
TR&CT
TNU
MITeC
PDRC
December 2013
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Inventory phase
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
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Repositioning phase
• 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
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Feb-Oct 2014
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
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• 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
Cross-technology interactions
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www.radboudumc.nl/research/technologycenters/
Combination of • Science • Business • Innovation • Impact in health
Working together on the Radboud campus
(Spin-out) companies
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Radboud Research Facilities www.ru.nl/radboudresearchfacilities/
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Working with other Technology Networks Region, nation, Europe, world
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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 & Education Com & 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
Apply to personalized healthcare
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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
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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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“Selfmonitoring = Trend of 2014” The future of medicine
‘insideables’
‘wearables’
• 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
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• Measure your brain waves (EEG)
• Recognize conditions for maximal concentration or relaxation.
• Use device to train.
DIY brainwave monitoring
DIY blood biomarker analysis
But …
Knowledge and Innovation gap:
1. What to measure?
2. How much should it change?
3. What should be the follow-up for me?
Most important for biomarkers in Personalized Healthcare:
Focus on the end user: the patient
38
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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?
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
Translation is key in Personalized Healthcare !
Personal profile data
Knowledge
Understanding
Decision
Action
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Translation is key in Personalized Healthcare !
Select personalized therapy
Treatment options
Succ
ess
rate
s
Example from Prostate cancer patient guide
Translation is key in Personalized Healthcare !
Treatment options
Pro’s
Con’s
Select personalized therapy
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
How to move forward?
Way forward: shared innovation
Standardisation, harmonisation, knowledge sharing needed in:
1. Assay development
2. Clinical validation and qualification
45
How to move forward?
Start small, think big
46
How to move forward?
Collaboration in Health Informatics
47
Lucien Engelen et al, Radboud Reshape Center for Innovation
How to move forward?
Be passionate !
My personal drivers:
Personalized Health(care)
Biomarkers
Molecular Profiling (Omics)
Future of medicine
48
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
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
