2014 09-10 ACI biomarker utilisation & commercialisation, London

Assessing the value of biomarkers in personalized healthcare

Professor in Personalized Healthcare Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers

Head Biomarkers in Personalized Healthcare

Prof Alain van Gool

My mixed perspectives in personalized health(care)

8 years academia (NL, UK)

(molecular mechanisms of disease)

13 years pharma (EU, USA, Asia)

(biomarkers, Omics)

3 years applied research institute (NL, EU)

(biomarkers, personalized health)

3 years med school (NL)

(personalized healthcare, Omics, biomarkers)

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

2

ACI Biomarker Utilisation & Commercialisation London

10-11 September 2014 Alain van Gool

Biomarkers in Personalized Healthcare an evolving role

• From only diagnosis

• To Translational Medicine

• To Personalized/Stratified/Precision Medicine

• To Personalized Healthcare

• To Person-centered Health(care)



3

Biomarkers in Personalized Healthcare in the early days

{Kumar and van Gool, RSC, 2013}

1506:

The urine wheel

Use color, smell and taste of

urine to diagnose disease and

decide best treatment

Ullrich Pinder

Epiphanie Medicorum



Biomarkers in Translational Medicine in pharma

• Translational medicine

Exposure

Mechanism

Efficacy

Safety

• Personalized medicine

Diagnosis

Prognosis

Response prediction

• Tools for data-driven decision making

Biologically relevant

Clinically accepted

Quantitative

Different analytes/types

Fit-for-purpose application

{Source: Van Gool et al, Drug Disc Today 2010}



5

Biomarker data-driven decisions

Target engagement? Effect on disease?

yes yes !

no no

• No need to test current

drug in large clinical trial

• Need to identify a more

potent drug

• Concept may still be

correct

• Concept was not correct

• Abandon approach

• Proof-of-Concept

• Proceed to full

clinical

development

“Stop early, stop cheap”

“More shots on goal”



6

Source: John Arrowsmith: Nature Reviews Drug Discovery 2011

• Success rates of clinical proof-of-concept have dropped from 28% to 18% • Insufficient efficacy as the most frequent reason • Targeted therapy through Personalized Medicine may be the solution

Promise of Personalized Medicine

Analysis of 108 failures in phase II

Reason for failure Therapeutic area



7

Biomarkers in Personalized Medicine

• Melanoma – targeted medicine

• Metabolic health – system medicine



8

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}



9

Clinical efficacy of Vemurafenib

{Wagle et al, 2011, J Clin Oncol 29:3085}

Before Rx Vemurafenib, 15 weeks Vemurafenib, 23 weeks

• Strong initial effects vemurafenib • Emerging drug resistancy • Reccurence of aggressive tumors



10

Tumor tissue/biomarker heterogeneity

• BRAFV600D/E is driving mutation

• However, also no BRAFV600D/E mutation found in regions of primary melanomas

• Molecular heterogeneity in diseased tissue

• Biomarker levels in tissue vary

• Biomarker levels in body fluids will vary

• Major challenge for (companion) diagnostics

{Source: Yancovitz, PLoS One 2012}



11

Metabolic health and disease

Type 2

Diabetes

Diabetes

complications

time



12

Systems view on metabolic health and disease β-cell Pathology

gluc Risk factor

{Source: Ben van Ommen, TNO}

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

Chronic Stress Disruption

circadian rhythm

Parasympathetic

tone

Sympathetic

arousal

Gut

activity

Inflammatory

response

Adrenalin

Heart rate Heart rate

variability

High cortisol

α-amylase



13


gluc Risk factor


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


circadian rhythm

Parasympathetic

tone

Sympathetic

arousal

Gut

activity

Inflammatory

response

Adrenalin


variability

High cortisol

α-amylase

{Nakatsuji, Metabolism 2009}



14

EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012


gluc Risk factor


therapy

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


circadian rhythm

Parasympathetic

tone

Sympathetic

arousal

Worrying

Hurrying

Endorphins Gut

activity Sweet & fat foods

Sleep disturbance

Inflammatory

response

Adrenalin

Fear

Challenge

stress


variability

High cortisol

α-amylase

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



15


Alain van Gool


Challenging metabolic equilibrium by Pharma-Nutrition

Age-matched “healthy” control group

t=16 w

(sampling)

t=9 w t=0

Induction of Diabetes intervention period

High-fat (HF) diet

High-fat diet “diseased” control group

Nutrition/Life style switch

HF + Drug 1

HF + Drug 2

HF + Drug 3

…. HF + Drug 10



16


Alain van Gool


clinica

l chem

istry

Syste

m n

etw

ork

s M

eta

bolo

me

Tra

nscrip

tom

e

fluxe

s Analysis: high throughput, multi organ, multi level

High-end data mining and warehousing

Extensive histological and molecular phenotyping



17


Alain van Gool


Effects on total adipose tissue weight

Full reversal of obese phenotype by Nutrition

switch, not by all drug treatments

T0901317 (LXR agonist) also

reverses obese phenotype



18


Alain van Gool


Effects on atherosclerosis

Still increased atherosclerosis in Nutrition

switch group

T0901317 (LXR agonist) strongly

induces atherosclerosis



19


Alain van Gool


{Nolan, Lancet 2011}

A sure need for systems medicine • Multiple interactions and

flexibilities in human

system

(tissues, cells, proteins)

• Blocking one pathway will

shift equilibrium and create

new problems

• System medicine approach

needed for maximal effect

• High value of biomarkers

but how to translate to

combination therapy?

• Pharma-Nutrition?



20


Alain van Gool


Relating tissue pharmacology – biomarker - therapy



21

Personalized interventions by Pharma-Nutrition

Ongoing: Shared Innovation Programs through public-private consortia

Higher efficacy / less side effects



22

http://www.google.nl/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=p6QFYUiIdmmdJM&tbnid=AHqgRrahI7o0JM:&ved=0CAUQjRw&url=http://adipexonlinestore.com/adipex-weight-loss-pill/&ei=jTOoUvmsB_CV0QXczYC4Cw&bvm=bv.57799294,d.d2k&psig=AFQjCNG9bijnRgqj4XZ0p1VYfKoUEB2cqw&ust=1386841349162938

http://www.google.nl/url?sa=i&source=images&cd=&cad=rja&docid=6NudAkL9vc6L6M&tbnid=mHBo-lThLziKUM:&ved=0CAgQjRw&url=http://guardianlv.com/2013/09/the-hunger-games-the-government-bill-to-take-away-americans-food/&ei=7DOoUpKsF6PF0QXR8IGgBQ&psig=AFQjCNF9L60SG9Xi0ZY7RLCPVh0Kvz6fnw&ust=1386841452435426

Data

mining

Models

Modelling

Analytics

(Mx, Px, Tx)

Organ-on-

a-chip

Imaging

Academic/ Clinical Industry

Shared Innovation Programs

20+ partners

Diagnostics

Pharma Nutrition

20+ partners

Better diagnosis and interventions

Personalized !

20+ partners

10+ partners



23

http://www.google.nl/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&docid=GLlMQNx7xxNOkM&tbnid=oeRT6Z2yJBtKpM:&ved=0CAUQjRw&url=http://www.recruitingroundtable.nl/2013/08/07/vind-de-juiste-kandidaten-met-big-data-recruitment-analyses/&ei=X-eiU9rzOMeK0AXMp4DQBQ&bvm=bv.69411363,d.d2k&psig=AFQjCNGAa4xX2FLpC1thGlNdmV0WLjgwWQ&ust=1403271369865137









24

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, October 2013)



25

A changing world: Personalized Medicine @Europe

European Science Foundation

30 Nov 2012

Innovative Medicine Initiative 2

8 July 2013

EC Horizon2020

10 Dec 2013



26

Patient

Radboud Personalized Healthcare

A significant impact

on healthcare

Molecule

Population

Personalized Healthcare @ Radboud university medical center



27

Personalized Healthcare @ Radboudumc

People are different Stratification by multilevel diagnosis

+ Patient’s preference of treatment

Exchange experiences in care communities

Select personalized therapy

28

Population

Man

Molecule



28

Translational medicine @ Radboudumc

SelectBio Biomarkers 2014

Cambridge

8 July 2014

Alain van Gool

Biomarkers in Personalized Diagnostics

{Nature, July 17 2014, 511: 344-}



30

Translation is key in Personalized Healthcare !

Personal profile data

Knowledge

Understanding

Decision

Action



31

Translation in Personalized Healthcare

“I’m afraid you’re

suffering from an

increased IL-1”

Adapted from:



32

Need for interdisciplinary team work



33

(Aug 2014)

Genomics

Bioinformatics

Animal studies Translational

neuroscience

Image-guided treatment

Imaging

Microscopy

Biobank

Health economics

Mass Spectrometry

Radboudumc Technology

Centers Investigational

products

Clinical trials EHR-based

research

Statistics

Human physiology

About 250 dedicated people working in 17 Technology Centers, ~1500 users (internal, external), ~130 consortia

www.radboudumc.nl/research/technologycenters/

Data stewardship

Molecule



34

Flow cytometry

Cross-technology interactions

SelectBio Biomarkers 2014

Cambridge

8 July 2014

Alain van Gool

Biomarkers in Personalized Healthcare

• 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

ChipCube-LC- Q-tof MS

• Outcome 1: Explanation of disease

• Outcome 2: Dietary intervention as succesful personalized therapy

• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test

• Genetic defect in glycosylation enzyme (PGM1) identified via exome sequencing

{Tegtmeyer et al, NEJM 370;6: 533 (2014)}

Genomics Glycomics Metabolomics



36

healthy disease disease + treatment

Identify subpopulations in Personalized Healthcare

healthy disease disease + treatment

• Biomarkers in populations often have a wide range • Within this range, subpopulations can behave quite differently • Chemometric methods dealing with multiple biomarker data points are needed to

define subgroups • Leading to n=1 clinical trials

(Source: Jasper Engel, Lionel Blanchet, Udo Engelke, Ron Wevers and Lutgarde Buydens)

37



37

H2020 PHC1 application - L’Homme Machine: Exploiting Industrial Control Techniques for Personalized Health

Partners Biobanks

Databank

Coordinator: prof Lutgarde Buydens,









39

Selfmonitoring



40

The future is nearly there …

41

Personalized advice

Action

Selfmonitor Cloud

Lifestyle Nutrition Pharma

Biomarkers in Person-centered Health(care)

Patient

Caregiver

Insurer

Self-monitoring

Patient

Caregiver

Insurer

Participatory

research

Bas Bloem

Marten Munneke

et al



42









43

However …

Knowledge and Innovation gap:

1. What to measure?

2. How much should it change?

3. What should be the follow-up for me?



44

Biomarker innovation gap

• Imbalance between biomarker discovery, validation and application

• Many more biomarkers discovered than available as diagnostic test

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2



45

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

10 Sep 2014: 9,975 biomarkers with 31,403 biomarker uses

EU: CE marking

USA: LDT, 510(k), PMA

46



46

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}

47



Way forward: shared innovation network projects

Standardisation, harmonisation, knowledge sharing needed in:

1. Assay development

2. Clinical validation



48

Open Innovation Networks (Next Generation Life Science)

(Source: Model TNO’s Holst Center)

Old New



49

Biomarker Development Center (Netherlands)

STW perspectief grant

Biomarker Development Center

Public-private partnership 4 years

Project grant €4.3M of which € 2.2M government,

and € 2.1M industry (€ 0.9M cash/ € 1.2M kind)

Close interactions with:

- Clinicians (biomarker application)

- Industry partners and stakeholders

- Patient stakeholder associations

Open slot!



50

http://www.erasmusmc.nl/index.htm

Personalized Healthcare

Ways forward:

• Patients included

• Participation + collaboration

• Selfmonitoring

• Personal profiles

• System biology

• (Big) Data sharing

• Personal preferences

• Personalized therapies

• Lifestyle + Nutrition + Pharma



51

Acknowledgements

Lucien Engelen

Jan Kremer

Paul Smits

Maroeska Rovers

Nathalie Bovy

Ron Wevers

Jolein Gloerich

Hans Wessels

Dirk Lefeber

Leo Kluijtmans

Bas Bloem

Marten Munneke

and others

Lutgarde Buydens

Jasper Engel

Jeroen Jansen

Geert Postma

and others

Members of the

Radboud umc Personalized Healthcare Taskforce (2013)

Radboud umc Technology Centers (2014)

[email protected]

[email protected]

www.linkedIn.com

Many external collaborators

Jan van der Greef

Ben van Ommen

Peter van Dijken

Bas Kremer

Lars Verschuren

Marijana Radonjic

Thomas Kelder

Robert Kleemann

Suzan Wopereis

Ton Rullmann

William van Dongen

and others



52

Health & Medicine

2014 09-10 ACI biomarker utilisation & commercialisation, London