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Companion Diagnostics in
Personalized Health Care
Prof. Alain van Gool
Netherlands Organisation for Applied Scientific Research (TNO)
Radboud University Nijmegen Medical Centre
Radboud University Nijmegen
Dutch CC meeting
Personalized Health Care, “Een haalbare kaart”
Ede, 2 October 2013
Companion Diagnostics
Right drug
in right patient
at right dose
at right time
In other words:
Apply a well characterized therapy in a biological system you know well
to treat a disease you understand well, in a way that you know works.
Use (molecular) biomarkers as diagnostic companions of a drug.
New: diagnostic companions to a person !
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
2
What type of biomarkers to use?
{Biomarkers definition working group, 2001 }
Definition: ‘a characteristic that is objectively measured and evaluated as an
indicator of normal biological processes, pathogenic processes, or
pharmacologic responses to a therapeutic intervention’
Or ‘Whatever works in adding value’
Molecular biomarkers provide a molecular impression of a biological system
(cell, animal, human)
Biomarkers can be various sorts of data, or combinations thereof
3 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
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
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
4 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
Companion Diagnostics
Metabolism
Efficacy or
safety
Source: www.fda.gov
5 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
Uptake of new biomarkers in clinical care
Research/technology push:
Biomarkers can and should provide the molecular part of the personalized healthcare
model in selection of best therapy, monitoring of effect, and follow-up
Daily practice in clinical assessment:
Combination of personal opinion (patient and physician), physical examination, clinical
chemistry to generate personal profiles
New biomarkers are added where deemed useful by physician
Costs important factor in decision on application
Act accordingly in follow-up care (more or less personalized)
Medication (a.o. personalized medicine)
Nutrition (a.o. individualized diets)
Life style (a.o. individualized exercise, counseling)
Slow uptake of new biomarkers
Limited by careful / conservative attitude of clinicians (added value of new biomarker?)
Limited by reimbursement options by insurers (increasingly important)
6 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
The innovation gap in biomarker development
7
• 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
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
Some numbers
8
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
July 2013: 7,771 biomarkers
EU: CE marking
USA: LDT, 510(k), PMA
Needed: A Biomarker Development Pipeline
9
• A focus on application of innovation, not on new technologies or biomarker discovery
• The innovation is a clinically validated biomarker that can be applied as diagnostic test
• Bring together available state-of-the art biomarker expertise in an industrial process flow
• Sponsors and end-users define objectives (a.o. pharma, diagnostics, patients)
• Shared biomarker R&D in Open Innovation Network based on Public-Private-Partnership
Shared knowledge,
technologies and objectives
In line with LS&H Topsector
Personal profiles
Source: Barabási 2007 NEJM 357; 4}
• People are different
• Different networks influences
• Different risk factors
10
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
BIODATA
PERSONALIZED
INTERVENTIONS
RISK FACTOR PATTERN
MOLECULAR LIFESTYLE / ENVIRONMENT
Metabolites RNA Protein
DNA Biochemical process
Enzymatic activity Imaging
mDNA Nutrition
Environment Social
network Attitude in life
Stress work / private
MULTIPARAMETER
PERSONAL PROFILES Statistics
Selection
Ranking
LIFESTYLE
NUTRITION
PHARMA
11 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
Example personal profile-based patient assessment (1)
4 components:
1. Number of tender joints
2. Number of swollen joints
3. Acute phase reactants
(ESR or CRP in blood)
4. Patient’s self-assessment
Disease Activity Score (DAS) 28 composite outcome measure
On line calculator:
Formula: 0.56x(TEN28) + 0.28x(SW28) + 0.70ln(ESR) + 0.014(GH)
1.0 - 3.1: low disease activity
3.2 - 5.1: moderate disease activity
> 5.1: high disease activity
{www.das-score.nl}
12 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
Example personal profile-based patient assessment (2)
{Chen et al, Cell 2012, 148: 1293}
Concept:
• Continuous monitoring (n=1)
• Routine biomarkers to alert
• Omics to explain
• Early intervention
13 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Oncology
CVD, neuro, immune
Diabetes
Personal profiles differ per disease phenotype
14 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
Companion Diagnostics in Oncology
V600D/E
Kinase domain
{Roberts and Der, 2007}
B-RAFV600D/E mutation: constitutively active kinase, oncogenic addiction
Overactivate ERK pathway drives cell proliferation
RAF inhibitors block growth of tumor xenografts with B-RAFV600D/E mutation
Prevalence of B-RAFV600D/E
Melanoma (60%), colon (15%), ovarian (30%), thyroid (30%) cancer
Develop B-RAF inhibitors with B-RAFV600D/E as companion diagnostic
15 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
16
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}
16
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
17
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
17
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
18
• BRAFV600D/E is considered the
driving mutation
• However, varying levels of
BRAFV600D/E mutation found in
regions of a primary melanoma
• Molecular heterogeneity in
diseased tissue
• Biomarker levels in tissue and
body fluids will vary
• New biomarkers are needed
• Challenge for companion
diagnostics
{Source: Yancovitz, PLoS One 2012}
Tumor tissue heterogeneity
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Oncology
CVD, neuro, immune
Diabetes
Personal profiles differ per disease phenotype
19 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
• Obesity
• Diabetes type 2
HEALTH DISEASE COMPLICATIONS
• Atherosclerosis • Nephropathy fibrosis • Osteoarthritis • Stroke • etc
Metabolic syndrome
metabolic disturbance local inflammation
Not a single cause but complex multifactorial diseases
Disturbed equilibrium between multiple pathways and key components
A system biology approach is needed
For discovery research, diagnosis and treatment
Continuous monitoring really pays off
Most effective therapy is ‘eat better, move more’ (lifestyle change)
Nutriceuticals / Lifestyle
Food
Pharma
20
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Each organ has its own
characteristics in
maintaining/loosing
flexibility and this
determines the
health to diabetes
transition.
{Nolan, Lancet 2011}
A sure need for system biology
High need to study the
effect of drugs/nutrition
on each of these organs
and their interaction
within the whole system
of each person.
21 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
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
Cognition Mobilizing
Reflexes
Systems view on (metabolic) health and disease
Lipids, alcohol, fructose
Carnitine, choline Omega3-fatty acids
Stannols, fibre
Low glycemic index
epicathechins
anthocyanins
Soy
Quercetin, Se, Zn, …
Metformin
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
23
Working in complex human biological systems requires a systems biology approach
Way forward:
1. Focus on key processes
2. Measure key node biomarkers
3. Convert to a functional fingerprint assay panel
4. Make actionable personalized decision on health /
disease management
5. Test added value in real life through field labs
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Important processes in
T2D
Diagnosis
Potential interventions
Dietary/LS 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
24 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Field labs: test health care concepts in real life
• Build field lab with pre-diabetic patients, physicians, dietitians, insurers, etc
• Measure individual ‘risk’ parameters for metabolic syndrome +/- challenge
• phenotypes, clinical chemistry, specific Omics, etc
• Convert data into a personal profile + personalized health advice
• life style +/- nutrition +/- pharmaceutical drugs
• Test personalized health concept in field lab following P4 medicine principle
• Alliance “Expedition Sustainable Care, starting with diabetes”
25 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
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
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Oncology
CVD, neuro, immune
Diabetes
Personal profiles differ per disease phenotype
27 Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
High attrition in most chronic diseases
{Source: Kola, 2008, Nature 83, 2: 227}
• Multifactorial causes of disease, mostly not well understood
• Risk factors include both molecular as lifestyle/environmental factors
• Treatment is often symptom-based, not mechanism-based
• System approach in diagnosis and treatment (systems medicine)
• Need improved disease definitions and understanding
28
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Redefining disease
{Nature Reviews Drug Discovery 2011, 10: 641}
29
Underlying concept: a chronic disease = a collection of rare diseases
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
8th IMI call:
Joined effort in EU to improve disease definitions and define best potential therapies
1. RA, SLE
2. AD, PD
From clinical Omics to personalized treatment:
• 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
• Genetic defect in glycosylation enzyme identified via exome sequencing
• Outcome: Explanation of disease
• Outcome: Dietary intervention as succesful personalized therapy
• Outcome: Glycoprofile developed as diagnostic test by mass spectrometry
Personalized Health Care in rare diseases
Dietary intervention
{Dirk Lefeber et al,
NEJM 2013}
30
Incomplete glycosylation Complete glycosylation
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
Personalized Health Care by Food + Lifestyle + Pharma
31
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
Acknowledgements
Jan van der Greef
Ben van Ommen
Peter van Dijken
Robert Kleemann
Lars Verschuren
Bas Kremer
Ton Rullmann
Marijana Radonjic
Thomas Kelder
Suzan Wopereis
and others
Ron Wevers
Jolein Gloerich
Dirk Lefeber
Monique Scherpenzeel
Leo Kluijtmans
Udo Engelke
and others
Lutgarde Buydens
Jasper Engel
Lionel Blanchet
Jeroen Jansen
and others
Radboud UMC Personalized Healthcare Taskforce:
Andrea Evers, Alain van Gool, Joris Veltman, Jan Kremer, Bas
Bloem, Maroeska Rovers, Jack Schalken, Paul Smits + Gerdi
Egberink, Viola Peulen, Martijn Hoogboom, Martijn Gerretsen
32
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool