2016 03-17 diesviering maasziekenhuis, boxmeer, alain van gool
TRANSCRIPT
The future is nearly here: Personalized Health(care)
Professor of Personalized Healthcare Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers
Senior Scientist Integrator Biomarkers
Prof Alain van Gool
Diesviering Maasziekenhuis Pantein, Boxmeer 17 March 2016
Scientific lead DTL-Technologies Head Biomarker Platform
My background 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)
1991-1996 (PhD)
1996-1998 (post-doc)
2009-2012 (visiting prof)
1999-2007 2007-2009 2009-2011
2011-now
2011-now (prof)
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A person / citizen / family man (adventures in EU, USA, Asia)
Alain van Gool, Maasziekenhuis Pantein, Boxmeer, 17 Mar 2016
2016 (Scientific lead DTL-Technologies)
2016 (Head Biomarker Platform)
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Consider individual differences in life science research
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Source: Chakma, Journal of Young Investigators, 16, 2009
Principle of Personalized Medicine
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• The right drug for right patient at right dose at right time • Molecular biomarkers as key drivers of patient selection • = Precision medicine or Targeted medicine
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Crash course in molecular biology
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DNA, protein, cell, tissue, system biology
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Biomarkers
{Biomarkers definition working group, 2001 }
‘a characteristic that is objectively measured and
evaluated as an indicator of normal biological
processes, pathogenic processes, or pharmacologic
responses to a therapeutic intervention’
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
Lecture LKCH, UMC Utrecht
29 October 2013
Alain van Gool
DNA, RNA proteins biochemicals images
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Example: Personalized medicine in melanoma
B-RAFV600E mutation Strong growth of cell Growth of tumor
• B-RAFV600E cells always grow and become cancer cells
• RAF inhibitors will block pathway, block cell growth and inhibit cancers that have a B-RAFV600E mutation
• 60% of melanoma patients have B-RAFV600E mutation
• Basis for a personalized medicine !
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Personalized medicine in melanoma
Treat patients with
B-RAFV600E mutation Inhibit growth of cell
Patients live longer Tumors disappear Cells stop growing
B-RAF inhibitor
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Emerging Personalized / Precision / Targeted Medicine
2010:
5% of drugs in pipeline had companion diagnostic biomarker test
2015:
80%
50%
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Optimal Personalized / Precision / Targeted Medicine
People are more than linear pathways
Moving to Personalized Health(care)
“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)
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Societal need in efficient personalized health(care)
{Source: prof Jan Kremer}
Towards cost effective care, less cure
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Personal need in efficient personalized health(care)
It’s personal !
‘I want to stay healthy.’ ‘If not, how do I get healthy?’
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My route to Personalized Health(care)
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Analogy: TOMTOM
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) model
GPS to health
Health
Route 1 Route 2
= Default First signs of disease risk
Alternative route
Now
Disease risk
Health
Now
Health
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3 key aspects of personalized health(care)
‘I want to stay healthy. If not, how do I get healthy?’
1. What to measure?
2. How much can it change?
3. What should be the follow-up for me?
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Exponential technological developments • Next generation sequencing
• DNA, RNA • Risk analysis and therapy selection
• Mass spectrometry • Proteins, metabolites • Monitoring of disease and treatment effects
• Imaging • Non invasive images, real time
• Spatial view of intact organs and organisms
500
1000
1500
2000
m/z
5 10 15 20 25 30 35 40 Time [min]
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News from a molecular biomarker lab • Mass spectrometry analysis of glycoproteins in human plasma • 1 microliter analysis: detection of 100.000 signals in one scan • ~40.000 peptides of which >80% contain sugar modification • Diagnose patients and identify new biomarkers
500
1000
1500
2000
m/z
5 10 15 20 25 30 35 40 Time [min]
Proof of principle study:
{Translational Metabolic Laboratory, Radboudumc, unpublished data} 22 Alain van Gool, Maasziekenhuis Pantein, Boxmeer, 17 Mar 2016
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 pipeline @ Radboudumc
*CCKL accreditation/RvA/EFI
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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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Implementation phase
Nov 2014 -
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
• 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
• iPSC • Organoids
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Working with other networks Region, nation, Europe, world
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New data (generators, owners)
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Demo room
Science fiction ?
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Science reality
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Personalized advice
Action
Selfmonitor Cloud
Lifestyle Nutrition Pharma
DIY monitoring of vital signs
DIY monitoring of glucose levels
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• Measure your brain waves (EEG)
• Recognize conditions for maximal concentration or relaxation.
• Use device to train.
DIY brainwave monitoring
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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
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• 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
23% chance blond hair
What does my DNA tell me?
3.1% Neanderthaler DNA
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Genetic risk lung cancer → don’t smoke !
What does my DNA tell me?
No expected adverse reaction to Warfarin
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‘insideables’
‘wearables’
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Try-outs at REshape Center of Health(care) Innovation
Lucien Engelen
… but not all data is useful data !
Need for optimal quality in health biomarker analyses
Test, interpret, advice
“Post-traumatic Test Syndrome” ?
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3 key aspects of personalized health(care)
‘I want to stay healthy. If not, how do I get healthy?’
1. What to measure?
2. How much can it change?
3. What should be the follow-up for me?
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Translation is key in personalized health(care)
Personal profile data
Knowledge
Understanding
Decision
Action
Shared decision making
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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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Personalized health(care) model
Personalized Intervention
of patients-like-me Personal thresholds of persons-like-me
Big Biomarker Data
Molecular Non-molecular Environment …
Time
Disease
Health
Selfmonitoring
Adapted from Jan van der Greef, TNO
Personal profile
Personalized health
Personalized medicine
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Personalized health(care) monitoring as part of our lives
• Monitor on background
• Alert when you are at risk
• Advice what to do
Examples from car dashboard:
• Empty gas tank
• Traffic jam ahead
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What is health?
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What’s next?
Simulating YOUR health
Heleen Wortelboer Herman van Wietmarschen Jan van der Greef Esther Zondervan Wim van Hartingsveldt
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What’s next?
Simulating YOUR health + Personalized advice + Feedback
View in the (very near) future
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Acknowledgements
Ron Wevers
Jolein Gloerich
Hans Wessels
Dirk Lefeber
Monique Scherpenzeel
Leo Kluijtmans
Lucien Engelen
Nathalie Bovy
Paul Smits
Maroeska Rovers
Bas Bloem
the Technology Centers
and many others
www.radboudumc.nl/personalizedhealthcare
www.radboudresearchfacilities.nl
www.radboudumc.nl/research/technologycenters
www.linkedIn.com
www.slideshare.net/alainvangool
Many collaborators and funders
Jan van der Greef
Ben van Ommen
Bas Kremer
Cyrille Krul
Ivana Bobeldijk
Lars Verschuren
Marjan van Erk
Peter van Dijken
Peter Wielinga
Ronald Mooij
Suzan Wopereis
and many others
CarTarDis
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