Personalized healthcare, a view in the near future

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

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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

EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

Working in complex human biological systems requires a systems biology approach

System biology in:

Diagnosis Prognosis Treatment Monitoring

Patient

Radboud Personalized Healthcare

A significant impact

on healthcare

Molecule

Population

5

Personalized Healthcare @ Radboudumc

People are different Stratification by multilevel diagnosis

+ Patient’s preference of treatment

Exchange experiences in care communities

Select personalized therapy

6

Population

Man

Molecule

Translation is key in Personalized Healthcare !

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Personal profile data

Knowledge

Understanding

Decision

Action

Translation in Personalized Healthcare

“I’m afraid you’re

suffering from an

increased IL-1”

Adapted from:

EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

Systems view on metabolic health and disease β-cell Pathology

gluc Risk factor

{Source: Ben van Ommen, TNO}

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

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

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

9

EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

Relating tissue pharmacology – biomarker - therapy

Personalized interventions by Pharma-Nutrition

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Ongoing: Shared Innovation Programs through public-private consortia

Higher efficacy / less side effects

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 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

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}

Knowledge and Innovation gap:

1. What to measure?

2. How much should it change?

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

Personalized Health(care) model

N=1 personalized health / healthcare Following P4 medicine: Participatory, Personalized, Predictive, Preventive

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)

16

Personalized Healthcare model (2)

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

Concept: • Continuous monitoring (n=1) • Routine biomarkers to alert • Omics to explain • Early intervention

17

Selfmonitoring

18

The future is nearly there …

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Personalized advice

Action

Selfmonitor Cloud

Lifestyle Nutrition Pharma

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

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

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Way forward: shared innovation network projects

Standardisation, harmonisation, knowledge sharing needed in:

1. Assay development

2. Clinical validation

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Example: Biomarker Development Center Dutch PPP grant 4.3M Eur

Personalized Healthcare

Ways forward:

• Patients included

• Participation + collaboration

• Selfmonitoring

• Personal profiles

• System biology

• (Big) Data sharing

• Personal preferences

• Personalized therapies

• Lifestyle + Nutrition + Pharma

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}

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“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

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