2013-04-23 Top Institute Pharma Spring meeting, Utrecht

Companion Diagnostics:
an update
Prof. Alain van Gool
Netherlands Organisation for Applied Scientific Research (TNO)
Radboud University Nijmegen Medical Centre
Radboud University Nijmegen
TI Pharma Spring Meeting
Utrecht, 23rd April 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.
TI Pharma Spring meeting
Utrecht, 23 April 2013
Alain van Gool
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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
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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
Alain van Gool
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Companion Diagnostics
Metabolism
Efficacy or
safety
Source: www.fda.gov
Alain van Gool
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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
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Alain van Gool

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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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Alain van Gool

EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
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• 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
Alain van Gool

The innovation gap in biomarker development
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• 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
Alain van Gool

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Emerging
Discovery Clinical validation/
confirmation
Diagnostic
application
Number of
biomarkers
Experimental
Discovery
Assay kit
development
Assay
development
Early Late
– Many new biomarkers are panels (RNA, protein, biochemical, imaging)
– Not wise to discover yet an other biomarker
– Focus on selecting the best biomarker (panels) among those already
found (scientific and patent literature, databases, etc)
– Develop those biomarkers tot clinically applicable tests
Imbalance between biomarker discovery and application
<10 biomarkers
Eg prostate cancer
May 2011: 2,231 biomarkers
Nov 2012: 6,562 biomarkers
{Source: Thomson Reuters Biomarkers Module}
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Alain van Gool

Biomarker innovation gap highlighted in topsector
Life Sciences & Health
{www.rijksoverheid.nl}
{http://www.zonmw.nl/nl/roadmaps-lsh/}
Roadmap Molecular Diagnostics:
• Build an efficient biomarker development pipeline in Netherlands to enable fast
progress of biomarkers from discovery to clinical implementation
• Bring all stakeholders together in a functional open innovation network based on
public-private-partnerships
• Have end-users (patients, clinicians) direct biomarker development in beginning
9 TopSectors 11 Roadmaps in TopSector Life Sciences & Health
Topsectors: initiative of Netherlands government to re-define the interest and focus of industry in public-private partnerships (2012)

Uptake of new biomarkers in clinical care
Research/technology push:
Biomarkers can and should provide the molecular part of this healthcare model in
monitoring 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
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)
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Alain van Gool

Personal profiles
Source: Barabási 2007 NEJM 357; 4}
• People are different
• Different networks influences
• Different risk factors
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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
Alain van Gool
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Example personal profile-based patient assessment
{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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Alain van Gool

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 being developed as diagnostic test by mass spectrometry
Example from rare diseases
Dietary
intervention
{Dirk Lefeber et al,
submitted}
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Incomplete glycosylation Complete glycosylation
Alain van Gool

Alain van Gool
Oncology
CVD, neuro, immune
Diabetes
Personal profiles differ per disease phenotype
Alain van Gool
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Alain van Gool
• 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
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Alain van Gool

Alain van Gool
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.
Alain van Gool
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Alain van Gool
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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

Alain van Gool
Important processes in
T2D
Diagnosis Potential interventions
Dietary/LS Pharma
1.Pancreatic β-cell function
(impaired insulin secretion)
*OGTT: I/∆G 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
Alain van Gool
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Alain van Gool
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”
Alain van Gool
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Alain van Gool
Oncology
CVD, neuro, immune
Diabetes
Personal profiles differ per disease phenotype
Alain van Gool
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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
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Alain van Gool
Redefining disease
{Nature Reviews Drug Discovery 2011, 10: 641}
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8th IMI call:
Joined effort in EU to improve
disease definitions and define best
potential therapies
1. RA, SLE
2. AD, PD
Alain van Gool

Alain van Gool
Human Diseasomes
From Barabási 2007 NEJM 357:4
Redefining disease
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Alain van Gool

Alain van Gool
Redefining disease: Medicine 3.0
Concept:
• Target causes of disease rather than
symptoms
• Identify and quantify common
mechanisms of chronic diseases
• Identify new targets for intervention
NL: Proposal submitted (10 yrs, 30MEur)
EU: Align with IMI and Horizon2020
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Alain van Gool

Alain van Gool
Network medicine
Proposed procedure
for network-based
drug discovery for
personalized therapy
Source: Schadt et al, 2009, Nature, 8:268}
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Alain van Gool

Personalized Health = Food + Lifestyle + Pharma
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Alain van Gool

Acknowledgements
Jan van der Greef
Ben van Ommen
Peter van Dijken
Robert Kleemann
Bas Kremer
Tom Rullmann
Suzan Wopereis
Marijana Radonjic
Thomas Kelder
and others
Ron Wevers
Jolein Gloerich
Dirk Lefeber
Monique Scherpenzeel
Udo Engelke
and others
Lutgarde Buydens
Jasper Engel
Lionel Blanchet
Jeroen Jansen
and others
Radboud UMC Personalized Medicine Taskforce:
Andrea Evers, Alain van Gool, Joris Veltman, Jan Kremer,
Maroeska Rovers, Jack Schalken, Bas Bloem, Gerdi Egberink,
Viola Peulen, Martijn Hoogboom, Martijn Gerretsen
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alain.vangool@tno.nl
Alain van Gool

2013-04-23 Top Institute Pharma Spring meeting, Utrecht

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2013-04-23 Top Institute Pharma Spring meeting, Utrecht