Biomarkers in a changing world Prof Alain van Gool Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers Head Biomarkers in Personalized Healthcare
May 07, 2015
Biomarkers in a changing world
Prof Alain van Gool
Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers
Head Biomarkers in Personalized Healthcare
Mixed perspectives
8 years academia (NL, UK)
(molecular mechanisms of disease)
13 years pharma (EU, USA, Asia)
(biomarkers, Omics)
2.5 years applied research institute (NL, EU)
(biomarkers, personalized health)
2.5 years med school (NL)
(Omics, biomarkers, personalized healthcare)
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
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9th Annual Biomarker Congress Oxford Global, Manchester
25th February 2014 Alain van Gool
Biomarkers in a changing world
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• From Personalized Medicine to Personalized Healthcare
• Disruptive technologies for biomarker R&D
• Need to accelerate the development of useful biomarkers and tools
9th Annual Biomarker Congress Oxford Global, Manchester
25th February 2014 Alain van Gool
TNO = Netherlands Organisation for Applied Scientific Research Mission = to drive ideas to reach their full market value.
We partner with:
Governmental & regulatory organisations
Universities
Pharma, chemical and food companies
International consortia
Knowledge
development
Knowledge
application
Knowledge
exploitation
Develop
fundamental
knowledge
With
universities
With
partners
With
customers
Embedded in the
market
TNO TNO companies
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TNO
Netherlands Organisation for Applied Scientific Research Founded in 1932 Non-for-profit research institute Member of EARTO ~3500 employees
19 sites in Netherlands + 18 sites/countries globally Funding: • Government (NL) • Contract research (world) • Public-private partnerships (world) 7 main themes
www.tno.nl
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TNO in European public-private partnerships
Healthy Living
Defence, Safety & Security
Transport & Mobility
Information Society
Industrial Innovation
Energy
Built Environment
Participation in EU projects: (Jan 2013)
260 projects (3100 partners)
Roles of TNO:
Technical expertise
Focus on applications
PPP management skills
(in 10% role as coordinator)
32% success rate
(average FP7 is 21%)
TNO’s applied biomarker tool box
Widely used preclinical translational models
Pharma, nutrition and chemical industry, academia
Focus on etiology of disease and mechanism of action
Human studies
Experimental medicine through CRO’s
Microdosing
Validated analytical platforms
Metabolomics profiling and targeted analysis, with focus on
lipids, ceramids, cannabinoides
Genomics, transcriptomics, proteomics and imaging through
a wide network of selected partners
Clinical chemistry
Data analysis
Network biology for mechanistic understanding
Multiparameter statistics and chemometrics
PK/PD translational modelling
Comprehensive system dynamics modelling
Biomarker expertise
Best practise strategies and approaches
A wide network with biomarker academia and industry
Metabolic Syndrome
• Atherosclerosis
• Diabetes
• Obesity
• Vascular inflammation
• NASH, fibrosis
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Year 1
Applying lessons learned across fields
e.g. System Biology @TNO
Year 2
Year 3
Radboudumc • Mission: “To have a significant impact on healthcare” • Strategic focus on Personalized Healthcare • Core activities:
• Patient care • Research • Education
• 11.000 colleagues • 50 departments • 3.000 students • 1.000 beds • First academic centre outside US to fully implement EPIC
Radboud Personalized Healthcare
Stratification by multilevel diagnosis
Exchange experiences in care communities
+ Patient’s preference of treatment
People are different
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Select personalized therapy
Translational medicine @ Radboudumc
Radboudumc Technology Centers
Genomics
Bioinformatics Preclinical therapies
Flow cytometry
Translational neuroscience
Novel concepts in surgery
Imaging
Microscopy
Biobank
Data stewardship
Proteomics Metabolomics
Radboudumc Technology
Centers
GMP products
Clinical trials
(February 2014)
Biomarkers in a changing world
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• From Personalized Medicine to Personalized Healthcare
• Disruptive technologies for biomarker R&D
• Need to accelerate the development of useful biomarkers and tools
9th Annual Biomarker Congress Oxford Global, Manchester
25th February 2014 Alain van Gool
Personalized Medicine
Right patient with right drug at right dose at right time for right outcome
Only part of the biomarker use in pharmaceutical development. Driven by the need to develop better drugs that work optimal in a selection of patients, rather than work mediocre in a larger patient group. Often translated to: Co-develop (molecular) biomarkers as diagnostic companions of a drug. In changing world: biomarkers are diagnostic companions of a person.
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Companion Diagnostics – some numbers
At present in pharmaceutical development:
40.000 clinical trials ongoing
16.000 trials in oncology
8.000 trials in oncology have a companion diagnostic (many genetic)
At present on market:
113 Biomarker in drug label (2012; up from 69 in 2010 = +64%)
16 CDx testing needed (2012; up from 4 in 2010 = +400%)
Costs of development:
>1.000 MUSD per drug
~10 MUSD per diagnostic Source: www.fda.gov
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Companion Diagnostics
Metabolism
Efficacy or safety
Source: www.fda.gov {Kumar and van Gool, RSC, 2013}
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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}
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Clinical effects of Vemurafenib
{Wagle et al, 2011, J Clin Oncol 29:3085}
Before Rx Vemurafenib, 15 weeks Vemurafenib, 23 weeks
• Strong initial effects vemurafenib • Drug resistancy • Reccurence of tumors
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Tumor tissue heterogeneity
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• BRAFV600D/E is driving mutation
• However, also no BRAFV600D/E mutation found in regions of a primary melanoma
• Molecular heterogeneity in diseased tissue
• Biomarker levels in tissue will vary
• Biomarker levels in body fluids will vary
• Major challenge for (companion) diagnostics
{Source: Yancovitz, PLoS One 2012}
9th Annual Biomarker Congress Oxford Global, Manchester
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‘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
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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, 1 nov 2013)
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Changing world: Personalized Medicine@ EU
(ESF, 30 Nov 2012) (IMI2, 8 July 2013) (EC, draft Nov 2013)
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Emerging: Personalized Healthcare in a systems view
Source: Barabási 2007 NEJM 357; 4}
• People are different • Different networks and influences • Different risk factors
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Personalized Healthcare in a systems view
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System biology model for Personalized Health(care) (a.k.a. Next Generation Life Sciences)
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 (2013)
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Example personal profile-based healthcare
{Chen et al, Cell 2012, 148: 1293}
Concept:
• Continuous monitoring (n=1)
• Routine biomarkers to alert
• Omics to explain
• Early intervention
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The big current bottleneck in Next Generation Life Sciences:
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(Big) data
Knowledge
Understanding
Decision
Action
Translation !
9th Annual Biomarker Congress Oxford Global, Manchester
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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
β-cell Pathology
gluc Risk factor
Heart rate Heart rate
variability
High cortisol
α-amylase
Systems view on metabolic health and disease
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
{Source: Ben van Ommen, TNO}
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Important processes in
T2D
Diagnosis
Potential interventions
Dietary/Lifestyle Pharma 1.Pancreatic β-cell function
(impaired insulin secretion)
*OGTT: I/ΔG and DI(0)
*PYY, Arg, His, Phe, Val, Leu
Lifestyle; β-cell
protective nutrients
(MUFA/isoflavonoids);
β -cell protective
medication (TZDs,
GLP-1 analogs,
DPP4-inhibitors)
2.Muscle insulin resistance
(decreased glucose uptake)
*OGTT: Muscle insulin resistance index,
Insulin secretion/insulin resistance index
*Val, Ile, Leu, Gamma-glutamylderivates,
Tyr, Phe, Met
PUFA/SFA balance;
Physical activity;
Weight loss;
TZDs (e.g.PPARγ)
3.Hepatic insulin resistance
(decreased glucose uptake and
increased hepatic glucose
production-HGP)
*Hepatic insulin resistance index *OGTT:
Hepatic insulin sensitivity index
*ALAT, ASAT, bilirubine, GGT, ALP, ck-18
fragments, lactate, α-hydroxybutyrate,
β-hydroxybutyrate
Decrease SFA and n-
6 PUFA, and increase
n-3 PUFA;
Weight loss;
Metformin;
TZDs;
Exenatide (GLP-1
analog);
DPP4 inhibitors
4. Adipocyte insulin resistance
and lipotoxicity
*basal adipocyte insulin resistance index
*FFA platform, glycerol
α-lipoic acid;
PUFA/SFA balance;
Omega 3 fatty acids;
Chitosan/plantsterols;
TZDs; Acipimox
5. GI tract (incretin
deficiency/resistance)
*ivGTT vs OGTT
*GLP-1, GIP, glucagon, galzuren
MUFA; Dietary fibre
(pasta/rye bread);
Exenatide
6. Pancreatic α-cell
(hyperglucagonemia)
*fasting plasma glucagon ? Glucagon receptor
antagonists;
Exenatide;
DPP4 inhibitors
7A.Chronic low-grade
inflammation in pancreas,
muscle, liver, adipose tissue,
hypothalamus
7B. Vascular inflammation
*CRP, total leucocytes
* V-CAM, I-CAM, Oxylipids, cytokines
Fish oil/n-3 fatty
acids; Vit. C/Vit.
E/Carotenoids;
Salicylates; TNF-α
inhibitors and others
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Field labs: implementation in 1st line health care
• Implementation-plan ‘personalized diagnosis
of (pre)diabetic and their lifestyle treatment in
Dutch Health care’.
• Use of OGTT as a stratification biomarker for
subgroups of (pre)diabetic patients
• Use diagnosis for a tailored lifestyle
(and medical) treatment
for these subgroups
Being implemented in
1st line care
regio Hillegom
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Pharma-Nutrition
Effect
Dose
Currently consortium building in Horizon2020, call PHC-13
Coordinator Bas Kremer ([email protected])
However …
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The world is changing and doesn’t wait for scientific rigor to catch up
9th Annual Biomarker Congress Oxford Global, Manchester
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Learn from Next Generation Life Sciences in USA
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Singularity University’s FutureMed 2013 speakers
Exponential technologies
Digital medicine
Integrated care
Artifical intelligence
Robotics Patients included
Lifestyle
Self quantification
Global health
Watson Artifical intelligence
Regenerative medicine
23andme Robotics
and Jamie Heywood (Patientslikeme)
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Singularity University’s FutureMed 2013 conference
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Exponential progress
“The only constant is change, and the rate of change is
increasing”
We are at the knee of the exponential curve
of progress
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1. Imaging of every part of human body in high resolution
2. Smartphone as the most important pieve of clothing
3. Self-diagnosis as a continous monitoring to quantified self
4. Artifical intelligence and robots
5. Digital medicine, Big Data and wisdom of the crowd
6. Our body as a lego box using 3D printing for spare parts
7. Our brain online using brainsensing headbands to transfer thoughts
Exponential trends
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Digital medicine
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Self-diagnosis
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The future is nearly there …
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Personalized advice
Action
Selfmonitor Cloud
Lifestyle Nutrition Pharma
9th Annual Biomarker Congress Oxford Global, Manchester
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Big Data
Exponential health(care) technologies
• IBM Watson
• AI system on top of recorded medical data + connected to Big Data clouds
• Independent data-driven clinical diagnosis with very high accuracy
• Artifical intelligence
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9th Annual Biomarker Congress Oxford Global, Manchester
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9th Annual Biomarker Congress Oxford Global, Manchester
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3 days high speed innovation in one slide
• Buzzwords:
• Exponential technologies
• Disruptive innovation
• Progress and beyond
• Digital quantified self
• Focus on:
• Where will we be in 5-20 years?
• Technologies, genomics, robotics, Big Data, eHealth, patient empowerment
• Less focus on:
• What to do next year?
• Biomarkers, robustness assays for decision, translating data to knowledge, innovation in clinical drug testing
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9th Annual Biomarker Congress Oxford Global, Manchester
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A problem in biomarker land
Imbalance between biomarker discovery and application.
• Gap 1: Strong focus on discovery of new biomarkers, few biomarkers progress beyond initial publication to multi-center clinical validation.
• Gap 2: Insufficient demonstrated added value of new clinical biomarker and limited development of a commercially viable diagnostic biomarker test.
Discovery Clinical validation/confirmation
Diagnostic test
Number of biomarkers
Gap 1
Gap 2
The innovation gap in biomarker research & development
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9th Annual Biomarker Congress Oxford Global, Manchester
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Some numbers
Data obtained from Thomson Reuters Integrity Biomarker Module (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 24 Feb 2014: 9,240 biomarkers with 28,538 biomarker uses
EU: CE marking
USA: LDT, 510(k), PMA
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9th Annual Biomarker Congress Oxford Global, Manchester
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Reasons for biomarker innovation gap
• Not one integrated pipeline of biomarker R&D
• Publication pressure towards high impact papers
• Lack of interest and funding for confirmatory biomarker studies
• Hard to organize multi-lab studies
• Biology is complex on organism level
• Data cannot be reproduced
• Bias towards extreme results
• Biomarker variability
• …
{Source: John Ioannidis, JAMA 2011} {Source: Khusru Asadullah, Nat Rev Drug Disc 2011}
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“It is simply no longer possible to believe much of the clinical
research that is published, or to rely on the judgment of trusted
physicians or authoritative medical guidelines.
I take no pleasure in this conclusion, which I reached slowly and
reluctantly over my two decades as an editor of The New
England Journal of Medicine.”
Marcia Angell, MD Former Editor-in-Chief NEJM Oct 2010
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Shared biomarker research through open innovation
We need to set up a open innovation network to share biomarker knowledge and expertise to jointly develop and validate biomarkers :
1. Assay development of (diagnostic) biomarkers
2. Clinical biomarker quantification/validation/confirmation
Shared knowledge,
technologies and objectives
through public-private partnerships (national, European, world-wide)
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Biomarker Development Center (Netherlands)
STW perspectief grant
Biomarker Development Center
Public-private partnership 4 years
Project grant 4.3M Eur of which 2.2M government,
and 2.1M industry (0.9M cash/1.2M kind)
Close interactions with:
- Clinicians (biomarker application)
- Industry
- Patient stakeholder associations
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9th Annual Biomarker Congress Oxford Global, Manchester
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Personalized healthcare
Ways forward:
• Participation + collaboration
• Selfmonitoring
• Personal profiles
• System biology
• (Big) Data sharing
• Personal preferences
• Personalized therapies
• Lifestyle + Nutrition + Pharma
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Acknowledgements
Jan van der Greef
Ben van Ommen
Peter van Dijken
Ton Rullmann
Lars Verschuren
Bas Kremer
Marijana Radonjic
Thomas Kelder
Robert Kleemann
Suzan Wopereis
William van Dongen
and others
Ron Wevers
Jolein Gloerich
Hans Wessels
Dirk Lefeber
Monique Scherpenzeel
Leo Kluijtmans
Udo Engelke
Ulrich Brandt
Lucien Engelen
and others
Lutgarde Buydens
Jasper Engel
Lionel Blanchet
Jeroen Jansen
and others
Radboud umc Personalized Healthcare Taskforce:
Paul Smits, Andrea Evers, Alain van Gool, Maroeska Rovers,
Joris Veltman, Jan Kremer, Bas Bloem, Jack Schalken, Gerdi
Egberink, Nathalie Bovy, Bob de Jonge, Viola Peulen, Marcel
Wortel, Martijn Hoogboom, Martijn Gerretsen
www.linkedIn.com
And external collaborators
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