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Personalized Health(care): more than just targeted medicines
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
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My mixed perspectives in personalized health(care)
8 years academia (NL, UK)
(molecular mechanisms of disease)
13 years pharma (EU, USA, Asia)
(biomarkers, Omics)
4 years med school (NL)
(personalized healthcare, Omics, biomarkers)
4 years applied research institute (NL, EU)
(biomarkers, personalized health, nutrition)
A person / citizen / family man
(adventures in EU, USA, Asia)
1991-1996 (PhD)
1996-1998 (post-doc)
2009-2012 (visiting prof)
1999-2007 2007-2009 2009-2011
2011-now
2011-now
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Source: Chakma Journal of Young Investigators. Vol 16, 2009.
Principle of Personalized/Precision/Targeted Medicine
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EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
“But dad, people are more than linear pathways, right ?”
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5 Alain van Gool, NanoNext.NL, 3 July 2015
Optimal Personalized / Precision / Targeted Medicine
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Humans are more than linear pathways
Source: Barabási 2007 NEJM 357; 4}
• People are different • Different networks and influences • Different risk factors • Different preferences
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Personalized health(care) in a systems view
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Personalized health(care)
Is more than ‘just’ targeted medicines
It’s personal !
‘I want to stay healthy.’ ‘If not, how do I get healthy?’
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Partners in personalized health(care)
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Partners in personalized health(care)
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Societal need in efficient personalized health(care)
Source: prof Jan Kremer
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Towards cost effective care, less cure
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Radboud university medical center
• Nijmegen, The Netherlands
• Mission: “To have a significant impact on healthcare”
• Strategic focus on Personalized and Participatory 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
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Involvement of patients throughout our core activities 1 september 2015: Opening of new curriculum by famous T1DM patient
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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
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Patient
Radboud Personalized Healthcare
A significant impact
on healthcare
Molecule
Population
15
Cell
Tissue
Subgroup
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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 studies
EHR-based research
Statistics
Human performance
Data stewardship
Molecule
Flow cytometry
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About 250 dedicated people working in 18 Technology Centers, ~1600 users (internal, external), ~140 consortia www.radboudumc.nl/research/technologycenters/
• 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 • Cyclotron/radio-isotopes • Malaria parasites
• Management • Analysis • Sharing • Cloud computing
• DNA • RNA
• Internal • External
• 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
• iPSC • Organoids
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Working with other Technology Networks Region, Netherlands, Europe, world
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About 250 dedicated people working in 18 Technology Centers, ~1600 users (internal, external), ~140 consortia www.radboudumc.nl/research/technologycenters/
• 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 • Cyclotron/radio-isotopes • Malaria parasites
• Management • Analysis • Sharing • Cloud computing
• DNA • RNA
• Internal • External
• 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
• iPSC • Organoids
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Emerging protein biomarkers
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Currently:
• Mostly protein abundance
Emerging:
• Ratio protein isoforms
• Post-translational modifications
• Protein complexes
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Connect patient to clinical lab to patient
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https://www.youtube.com/watch?v=yhLbuX0H7rg
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Glycomics
Intact glycoproteins
Free glycans
Glycopeptides 500
750
1000
1250
1500
1750
m/z
10 15 20 25 30 35 40 Time [min]
PGM1 profile
CID fragmentation spectrum
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Example of diagnostic glycoprotein biomarker • Rare metabolic disease cases
• Combination glycoproteomics and exome sequencing
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test
{Tegtmeyer et al, NEJM 370;6: 533 (2014)}
Genomics Glycomics Metabolomics
{Monique van Scherpenzeel, Dirk Lefeber}
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Discovering new glycoprotein biomarkers
• 1D LC-MS/MS glycoproteomics in plasma • 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:
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Intact protein analysis
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Bottum-up proteomics
Top-down proteomics
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Intact complexome analysis as new biomarker?
• Native tissue biopsies
• Isolate intact membrane complexes
• Separate and isolate complexes using native gels
• LC-MS/MS analysis of intact proteins
• Data analysis
Tissue 1 (n=3)
Tissue 2 (n=3)
Subunit
Subunit – tissue 1
Subunit – tissue 2
• Identified protein sequence of subunit • Deduce simulated sequences from database • Determine fit with experimental data
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Research Biomarkers Diagnostics
Department of Laboratory Medicine, Radboud univerity medical center 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 • Pediatric oncology • 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
Efficient translation to novel diagnostics
*CCKL accreditation/RvA/EFI
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New data !
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Try-outs at REshape Center of Health(care) Innovation
Lucien Engelen
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Specialized validated Apps as ‘stress coach’ {Victor Kallen}
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But … ‘I want to stay healthy. If not, how do I get healthy?’
Knowledge and Innovation gap:
1. What to measure?
2. How much can it change?
3. What should be the follow-up for me?
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Step 1 in Personalized Health(care):
Focus on the end user: the patient / citizen
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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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Translation is key in Personalized Healthcare !
Personal profile data
Knowledge
Understanding
Decision
Action
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Lab values Clinical outcomes
Pain Mobility Fatigue
INTEGRATE-HTA
Objectives patient and clinican may be different
R van Hoorn, W Kievit, M Tummers, GJ van der Wilt
How to do optimal shared decision making?
Intervention
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Translation is key in Personalized Healthcare !
Select personalized therapy
Treatment options
Succ
ess
rate
s
Example shared decision making In prostate cancer
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Translation is key in Personalized Healthcare !
Treatment options
Pro’s
Con’s
Select personalized therapy
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The route to Personalized Health
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Analogy: route planner
GPS to a location
Amsterdam
Traffic jam
Amsterdam
Route 1 Route 2
= Default Traffic jam near Utrecht Alternative route
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Personalized Health(care) planner
GPS to health
Health
Route 1 Route 2
= Default First signs of disease risk
Alternative route
Now
Health risk
Health
Now
Health
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Personalized Health(care) model
Analogies to GPS route planner:
• Technology enabled
• Monitoring should be on the background; only alert when risk
• Success through participation of user
• Personal choice to actively monitor or not
• Commercial competition of tool builders to become market leader(s)
• Implementation as standard in society
GPS to health
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Know your personal thresholds and intervention options
Ho
meo
sta
sis
A
llo
sta
sis
D
isease
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, TNO (2013) Personalized, Participatory, Pre-emptive
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Simulate and visualise health interventions {Albert de Graaf}
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Open up to system interventions
Higher efficacy / less side effects
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Eg by Pharma-Nutrition
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Collaboration in Health Informatics
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But … ‘I want to stay healthy. If not, how do I get healthy?’
Knowledge and Innovation gap:
1. What to measure?
2. How much can it change?
3. What should be the follow-up for me?
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Accelerate translation from research to application
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Gap 3
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Step 2 in Personalized Health(care):
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Biomarker innovation gaps: some numbers
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 14 Sept 2015: n = 11,805 biomarkers
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Gap 3
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Build biomarker validation pipelines
Standardisation, harmonisation, knowledge sharing needed in:
1. Assay development
2. Clinical validation
3. Regulatory acceptance
NL Roadmap Molecular Diagnostics (2012) NL Grant 4.3M Eur (2014)
Move towards EU funding (2016)
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Copy best practice
•Nation wide coverage
•66 regional networks
•3000 trained experts
•12 disciplines
prof Bas Bloem dr Marten Munneke
Step 3 in Personalized Health(care):
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5. Supportive technology
1. Network of experts
2. The patient as partner
4. Transparant quality controle
3. Integral reward for outcome, not production
5 key components of ParkinsonNet
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Demonstrated added value
Regular care
ParkinsonNet care
% Hip fracture Cost per patient*
*Hospitals, medication, care at home
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Dutch export product …
King Willem Alexander
Bas Bloem
Marten Munneke
Queen Maxima
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Spread the word
Step 4 in Personalized Health(care):
Alain van Gool, 2nd Precision Medicine Congress, London, 15 Sept 2015
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Acknowledgements
Ron Wevers
Jolein Gloerich
Hans Wessels
Monique Scherpenzeel
Dirk Lefeber
Leo Kluijtmans
Lucien Engelen
Paul Smits
Maroeska Rovers
Nathalie Bovy
Bas Bloem
and many others
www.radboudumc.nl/personalizedhealthcare
www.radboudumc.nl/research/technologycenters
www.radboudresearchfacilities.nl
www.linkedIn.com
www.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
Peter Wielinga
Robert Kleemann
Suzan Wopereis
and many others And funders
CarTarDis
61 Alain van Gool, 2nd Precision Medicine Congress, London, 15 Sept 2015