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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

2013-10-02 Dutch CC symposium on Personalized Healthcare, Ede

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Page 1: 2013-10-02 Dutch CC symposium on Personalized Healthcare, Ede

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

Page 2: 2013-10-02 Dutch CC symposium on Personalized Healthcare, Ede

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”

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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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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

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Companion Diagnostics

Metabolism

Efficacy or

safety

Source: www.fda.gov

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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)

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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

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

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Some numbers

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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

Page 9: 2013-10-02 Dutch CC symposium on Personalized Healthcare, Ede

Needed: A Biomarker Development Pipeline

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• 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

Page 10: 2013-10-02 Dutch CC symposium on Personalized Healthcare, Ede

Personal profiles

Source: Barabási 2007 NEJM 357; 4}

• People are different

• Different networks influences

• Different risk factors

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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 (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}

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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

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EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

Oncology

CVD, neuro, immune

Diabetes

Personal profiles differ per disease phenotype

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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, 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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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

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EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

Oncology

CVD, neuro, immune

Diabetes

Personal profiles differ per disease phenotype

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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

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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.

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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

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

Page 23: 2013-10-02 Dutch CC symposium on Personalized Healthcare, Ede

EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

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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

Page 24: 2013-10-02 Dutch CC symposium on Personalized Healthcare, Ede

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

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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”

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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

Page 27: 2013-10-02 Dutch CC symposium on Personalized Healthcare, Ede

EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

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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EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

Redefining disease

{Nature Reviews Drug Discovery 2011, 10: 641}

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Underlying concept: a chronic disease = a collection of rare diseases

Dutch CC meeting ‘Personalized Health Care”

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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

Page 30: 2013-10-02 Dutch CC symposium on Personalized Healthcare, Ede

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}

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Incomplete glycosylation Complete glycosylation

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Personalized Health Care by Food + Lifestyle + Pharma

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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

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[email protected]

[email protected]

Dutch CC meeting ‘Personalized Health Care”

Ede, 2 October 2013

Alain van Gool