2014 09-25 miptec europe's drug discovery event

Biomarkers in Personalized Health(care), from Discovery to Clinical Diagnostics

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

MipTec 2014 Basel

25 September 2014 Prof Alain van Gool

2

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)

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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}

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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”

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Source: 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 • Better therapies following Personalized Medicine strategies are needed

Need for Personalized Medicines

Analysis of 108 failures in phase II

Reason for failure Therapeutic area

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EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

System biology in:

Diagnosis Prognosis Treatment Monitoring

People are complex biological systems which requires a systems biology approach

Metabolic health and disease

Type 2

Diabetes

Diabetes

complications

An increasing health problem globally !

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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

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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}

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Alain van Gool



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

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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

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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

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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

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Alain van Gool


Effects on atherosclerosis

Still increased atherosclerosis in Nutrition

switch group

T0901317 (LXR agonist) strongly

induces atherosclerosis

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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 and/or Nutrition?

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Alain van Gool


Relating Tissue pharmacology – Biomarker - Therapy

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Potential synergy of Pharma-Nutrition

Ongoing: Shared Innovation Programs through public-private consortia

Higher efficacy / less side effects

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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

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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

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)

present

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Personalized Healthcare, more than pathways only

Source: Barabási 2007 NEJM 357; 4}

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

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Personalized Healthcare in a systems view

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Most important in Personalized Healthcare:

Include the patient as partner

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A 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)

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A changing world: Personalized Medicine @Europe

European Science Foundation

30 Nov 2012

Innovative Medicine Initiative 2

8 July 2013

EC Horizon2020

10 Dec 2013

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Radboud university medical center

• Nijmegen, The Netherlands

• Mission: “To have a significant impact on healthcare”

• Strategic focus on Personalized Healthcare through “the patient as partner”

• Core activities:

• Patient care

• Research

• Education

• 11.000 colleagues

• 52 departments

• 3.300 students

• 1.000 beds

• First academic centre outside US to fully implement EPIC

Personalized Healthcare @ Radboudumc

People are different Stratification by multilevel diagnosis

+ Patient’s preference of treatment

Exchange experiences in care communities Select personalized therapy

Population

Patient

Molecule

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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

Data stewardship

Molecule

Flow cytometry

(Aug 2014)

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

www.radboudumc.nl/research/technologycenters/

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Personalized genomic diagnostics

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

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Human samples

Plasma, CSF (urine) Controls vs. patient

QTOF Mass Spectrometry

- Reverse phase liquid chromatography - Positive and negative mode - Features

XCMS

Alignment

Peak comparison

> 10,000 Features

Personalized metabolic diagnostics

Xanthine Uric acid

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Example of omics-enabled 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

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Personalized self-monitoring

Need to change development process for Personalized Healthcare therapies

• Randomized Clinical Trials won’t be good enough (= groups)

• n=1 clinical trial designs needed whereby:

• Multiple monitoring in same person

• Use different types of biodata (molecular, non-molecular)

• Normalize data per individual

• Combine separate data through meta-analysis

• Output:

• Responders vs non-responders

• Tight data per subgroup

• Clear conclusions on therapy

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healthy disease disease + treatment

Different trial outcomes in Personalized Healthcare

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100%

Normalisation Subgroups

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

Partners Biobanks

Databank

Coordinator: prof Lutgarde Buydens,

However …

Knowledge and Innovation gap:

1. What to measure?

2. How much should it change?

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

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Translation is key in Personalized Healthcare !

Personal profile data

Knowledge

Understanding

Decision

Action

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Translation 1: Data to usable tests

• 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

Biomarker Innovation Gap

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Some numbers

Data obtained from Thomson Reuters Integrity Biomarker Module

Eg Biomarkers in time: Prostate cancer

May 2011: 2,231 biomarkers

Nov 2012: 6,562 biomarkers

Oct 2013: 8,358 biomarkers

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

EU: CE marking

USA: LDT, 510(k), PMA

Translation 2: Science to patient

“I’m afraid you’re

suffering from an

increased IL-1”

Adapted from:

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Personalized Health(care) model

Ho

meo

sta

sis

A

llo

sta

sis

D

isease

Time

Disease

Health

Personalized Intervention

of patients-like-me

Big Data

Risk profiles of persons-like-

me

Molecular Non-molecular Environment …

Personal profile

Selfmonitoring

Adapted from Jan van der Greef (2013)

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Person-centered Health(care)

Ways forward:

• Patients included

• Participation + collaboration

• Personal profiles

• System biology

• Health informatics

• Personal preferences

• Personalized therapies in

Lifestyle + Nutrition + Pharma

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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

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Patient

Radboud Personalized Healthcare

A significant impact

on healthcare

Molecule

Population

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2014 09-25 miptec europe's drug discovery event

Health & Medicine

prof alain van gool2

prof alain van gool3

prof alain van gool4

prof alain van gool6

prof alain van gool8

prof alain van gool9

prof alain van gool10

ben van ommen