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 drugin right patientat right doseat 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 meetingUtrecht, 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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Utrecht, 23 April 2013Alain van Gool

Companion Diagnostics – some numbers

At present in pharmaceutical development:40.000 clinical trials ongoing16.000 trials in oncology8.000 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


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Companion Diagnostics

Metabolism

Efficacy or safety

Source: www.fda.gov


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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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Clinical efficacy of Vemurafenib (PLX-4032, Zelbora f)

Key biomarkers:

Stratification: BRAFV600E mutationMechanism: P-ERK

Cyclin-D1Efficacy: Ki-67

18FDG-PET, CTClinical 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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EC DG for Research and InnovationAlain 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


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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

Diagnostictest

Number ofbiomarkers

Gap 1

Gap 2


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EmergingDiscovery Clinical validation/

confirmation

Diagnosticapplication

Number ofbiomarkers

ExperimentalDiscovery

Assay kitdevelopment

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 applicati on

<10 biomarkers

Eg prostate cancerMay 2011: 2,231 biomarkersNov 2012: 6,562 biomarkers{Source: Thomson Reuters Biomarkers Module}



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 careResearch/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 biomarkersLimited by careful / conservative attitude of clinicians (added value of new biomarker?)

Limited by reimbursement options by insurers (increasingly important)



Personal profiles

Source: Barabási 2007 NEJM 357; 4}

• People are different• Different networks influences• Different risk factors



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


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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



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

Dietaryintervention

{Dirk Lefeber et al,submitted}

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Incomplete glycosylation Complete glycosylation


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Oncology

CVD, neuro, immune

Diabetes

Personal profiles differ per disease phenotype


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• 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 diseasesDisturbed equilibrium between multiple pathways and key components

A system biology approach is neededFor discovery research, diagnosis and treatment

Continuous monitoring really pays offMost effective therapy is ‘eat better, move more’ (lifestyle change)

Nutriceuticals / Lifestyle Food

Pharma

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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 interactionwithin the whole system of each person.


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Working in complex human biological systems requires a systems biology approach

Way forward:1. Focus on key processes2. Measure key node biomarkers3. Convert to a functional fingerprint assay panel4. Make actionable personalized decision on health / disease management5. Test added value in real life through field labs



Important processes in T2D

Diagnosis Potential interventionsDietary/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, hypothalamus7B. 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: 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”


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Oncology

CVD, neuro, immune

Diabetes

Personal profiles differ per disease phenotype


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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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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


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Human DiseasomesFrom Barabási 2007 NEJM 357:4

Redefining disease

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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 interventionNL: Proposal submitted (10 yrs, 30MEur)EU: Align with IMI and Horizon2020





Network medicine

Proposed procedure for network-based drug discovery for personalized therapy

Source: Schadt et al, 2009, Nature, 8:268}

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Personalized Health = Food + Lifestyle + Pharma

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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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[email protected]


Alain van Gool

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

Health & Medicine

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