2014 03-13 Health Valley 2014, Nijmegen

Biomarkers in personalized healthcare, a changing world

Health Valley Event 2014

13 March 2014 Nijmegen

Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers

Head Biomarkers in Personalized Healthcare

Prof Alain van Gool

Mixed perspectives in personalized healthcare

8 years academia (NL, UK)

(molecular mechanisms of disease)

13 years pharma (EU, USA, Asia)

(biomarkers, Omics)

2.5 years applied research institute (NL, EU)

(biomarkers, personalized health)

2.5 years med school (NL)

(Omics, biomarkers, personalized healthcare)

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

Health Valley Event 2014 Nijmegen

13 March 2014 Alain van Gool

Biomarkers in personalized healthcare, a changing world

• From Personalized Medicine to Personalized Healthcare

• Disruptive technologies

• Need to accelerate the development of useful tools



Personalized diagnostics in early days

This urine wheel was published

in 1506 by Ullrich Pinder, in his

book Epiphanie Medicorum.

The wheel describes the

possible colors, smells and

tastes of urine, and uses them

to diagnose disease.



Source: wikipedia {Kumar and van Gool, RSC, 2013}

Personalized Medicine

Right patient with right drug at right dose at right time for right outcome

Only part of the biomarker use in pharmaceutical development. Driven by the need to develop better drugs that work optimal in a selection of patients, rather than work mediocre in a larger patient group. Often translated to: Co-develop (molecular) biomarkers as diagnostic companions of a drug. In changing world: biomarkers are diagnostic companions of a person.



Pharmaceutical 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 (many genetic)

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



Companion Diagnostics

Metabolism

Efficacy or safety

Source: www.fda.gov {Kumar and van Gool, RSC, 2013}



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}



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 • Emerging drug resistancy • Reccurence of aggressive tumors



Tumor tissue heterogeneity

• BRAFV600D/E is driving mutation

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

• Molecular heterogeneity in diseased tissue

• Biomarker levels in tissue will vary

• Biomarker levels in body fluids will vary

• Major challenge for (companion) diagnostics

{Source: Yancovitz, PLoS One 2012}



‘Complicating’ factors in oncology therapy

Source: 11 Sept 2013 @de Volkskrant

• Biological clock

• Smoking

• Pharma-Nutrition

• Drug-drug interaction

• Alternative medicine

• Genetic factors

• …

Interview with Prof Ron Matthijssen, ErasmusMC, Rotterdam



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, 1 nov 2013)



Changing world: Personalized Medicine@ EU

(ESF, 30 Nov 2012) (IMI2, 8 July 2013) (EC, draft Nov 2013)



Emerging: Personalized Healthcare in a systems view

Source: Barabási 2007 NEJM 357; 4}

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



Personalized Healthcare in a systems view



Personalized Healthcare in a systems view



Patient participation and empowerment

included !!

Radboud Personalized Healthcare

Stratification by multilevel diagnosis

Exchange experiences in care communities

+ Patient’s preference of treatment

People are different

Select personalized therapy



System biology model for Personalized Health(care) (a.k.a. Next Generation Life Sciences)

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)



Example System-based Personalized Healthcare

{Chen et al, Cell 2012, 148: 1293}

Concept:

• Continuous monitoring (n=1)

• Routine biomarkers to alert

• Omics to explain

• Early intervention



The big current bottleneck in Next Generation Life Sciences:

(Big) data

Knowledge

Understanding

Decision

Action

Translation !



Systems view on metabolic health and disease

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

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

{Source: Ben van Ommen, TNO}

therapy


Nijmegen

13 March 2014

Alain van Gool

EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

Relating tissue pharmacology – biomarker - therapy


Nijmegen

13 March 2014

Alain van Gool

Translating knowledge to field labs

• 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

Alliance “Expedition Sustainable Care,

starting with diabetes”


Nijmegen

13 March 2014

Alain van Gool

https://www.mediq-apotheek.nl/default.aspx

Pharma-Nutrition potential

Effect

Dose

Horizon2020 consortium, call PHC-13

Higher efficacy / less side effects

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

However …

The world is changing and doesn’t wait for scientific rigor to catch up



Next Generation Life Sciences in USA



Singularity University’s FutureMed 2013 speakers

Exponential technologies

Digital medicine

Integrated care

Artifical intelligence

Robotics Patients included

Lifestyle

Self quantification

Global health

Watson Artifical intelligence

Regenerative medicine

23andme Robotics

and Jamie Heywood (Patientslikeme)



Singularity University’s FutureMed 2013 conference



Exponential progress

“The only constant is change, and the rate of change is

increasing”

We are at the knee of the exponential curve

of progress



1. Imaging of every part of human body in high resolution

2. Smartphone as the most important pieve of clothing

3. Self-diagnosis as a continous monitoring to quantified self

4. Artifical intelligence and robots

5. Digital medicine, Big Data and wisdom of the crowd

6. Our body as a lego box using 3D printing for spare parts

7. Our brain online using brainsensing headbands to transfer thoughts

Exponential trends



Digital medicine



Self-diagnosis



The future is nearly there …

Personalized advice

Action

Selfmonitor Cloud

Lifestyle Nutrition Pharma



Big Data

Exponential health(care) technologies

• IBM Watson

• AI system on top of recorded medical data + connected to Big Data clouds

• Independent data-driven clinical diagnosis with very high accuracy

• Artifical intelligence





3 days high speed innovation in one slide

• Buzzwords:

• Exponential technologies

• Disruptive innovation

• Progress and beyond

• Digital quantified self

• Focus on:

• Where will we be in 5-20 years?

• Technologies, genomics, robotics, Big Data, eHealth, patient empowerment

• Less focus on:

• What to do next year?

• Biomarkers, robustness assays for decision, translating data to knowledge, innovation in clinical drug testing



However …

Knowledge and Innovation gap:

1. What to measure?

2. How much should it change?

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



A problem in biomarker land

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

The innovation gap in biomarker research & development



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 24 Feb 2014: 9,240 biomarkers with 28,538 biomarker uses

EU: CE marking

USA: LDT, 510(k), PMA



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}



“It is simply no longer possible to believe much of the clinical

research that is published, or to rely on the judgment of trusted

physicians or authoritative medical guidelines.

I take no pleasure in this conclusion, which I reached slowly and

reluctantly over my two decades as an editor of The New

England Journal of Medicine.”

Marcia Angell, MD Former Editor-in-Chief NEJM Oct 2010



Shared biomarker development through open innovation

Needed: open innovation network to join forces in:

1. Assay development of (diagnostic) biomarkers

2. Clinical biomarker quantification/validation/confirmation

Shared knowledge,

technologies and objectives

through public-private partnerships (national, European, world-wide)



Biomarker Development Center (Netherlands)

STW perspectief grant

Biomarker Development Center

Public-private partnership 4 years

Project grant 4.3M Eur of which 2.2M government,

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

Close interactions with:

- Clinicians (biomarker application)

- Industry

- Patient stakeholder associations



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

Translational medicine @ Radboudumc



Radboudumc Technology Centers

Genomics

Bioinformatics Preclinical therapies

Flow cytometry

Translational neuroscience

Novel concepts in surgery

Imaging

Microscopy

Biobank

Data stewardship

Proteomics Glycomics

Metabolomics

Radboudumc Technology

Centers

GMP products

Clinical trials

(February 2014)



Example: cross-technology diagnostics development

• 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 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



Personalized Healthcare

Ways forward:

• Patients included

• Participation + collaboration

• Selfmonitoring

• Personal profiles

• System biology

• (Big) Data sharing

• Personal preferences

• Personalized therapies

• Lifestyle + Nutrition + Pharma



Acknowledgements

Jan van der Greef

Ben van Ommen

Peter van Dijken

Bas Kremer

Marijana Radonjic

Thomas Kelder

Robert Kleemann

Suzan Wopereis

Ton Rullmann

Lars Verschuren

William van Dongen

and others

Andrea Evers

Lucien Engelen

Jan Kremer

Paul Smits

Maroeska Rovers

Nathalie Bovy

Ron Wevers

Jolein Gloerich

Hans Wessels

Dirk Lefeber

Leo Kluijtmans

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



Year 1

Applying lessons learned across fields

e.g. System Biology @TNO

Year 2

Year 3

Health & Medicine

2014 03-13 Health Valley 2014, Nijmegen