Personalized health(care), moving

beyond just targeted medicine

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

Oudejaarssymposium “Personalized Healthcare” Groningen 6 January 2015

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 med school (NL)

(personalized healthcare, Omics, biomarkers)

3 years applied research institute (NL, EU)

(biomarkers, personalized health)

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

2

Biomarkers in Personalized Healthcare an evolving role

• From only diagnosis

• To Translational Medicine

• To Personalized/Precision/Targeted Medicine

• To Personalized Healthcare

• To Person-centered Health(care)

3

Diagnostic biomarkers 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

4

Exponential developments in biomarker technologies

• Next generation sequencing • Large level of detail on genome level (DNA, RNA) • Sequencing per patient is becoming practice • Allows risk analysis and therapy selection

• Mass spectrometry

• Large level of detail on metabolic level (proteins, metabolites)

• Analysis of blood, urine, cells, tissues, hair, etc all possible • Allows monitoring of disease and treatment effects

• Imaging • Large level of detail on intact in vivo level • Analysis of any tissue, real time

• Allows spatial view of intact organs and organisms

5

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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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 • Promising examples in oncology

Promise of Personalized Medicine

Analysis of 108 failures in phase II

Reason for failure Therapeutic area

8

Case study: Development BRAF inhibitors for melanoma

{Miller and Mihm, 2006}

Rationale:

• BRAF mutation first

event in melanoma

• Mutation makes

BRAF kinase

constitutively active

• BRAF-MEK-ERK

pathway leads to

cell proliferation

• Inhibition BRAF

pathway leads to

tumor cell death

• High frequency

BRAF mutation in

melanoma

9

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}

10

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

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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}

12

‘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

13

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

Biomarkers in Personalized Healthcare an evolving role

• From only diagnosis

• To Translational Medicine

• To Personalized/Precision/Targeted Medicine

• To Personalized Healthcare

• To Person-centered Health(care)

present

15

Personalized Healthcare, more than pathways only

Source: Barabási 2007 NEJM 357; 4}

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

16

Personalized Healthcare in a systems view

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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

19

Most important in Personalized Healthcare:

Include the patient as partner

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Patient

Radboud Personalized Healthcare

A significant impact

on healthcare

Molecule

Population

Personalized Healthcare @Radboudumc

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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

22

Personalized Healthcare @ Radboudumc

People are different Stratification by multilevel diagnosis

+ Patient’s preference of treatment

Exchange experiences in care communities

Select personalized therapy

Population

Man

Molecule

23

Personalized genomic diagnostics

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

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2012

Patient Targeted

Metabolic

screen

Targeted

gene

analysis

Diagnosis

+ follow-up

2013 / 2014

Patient

Whole

exome

sequencing Targeted

confirmatory

metabolite +

enzyme

testing

Diagnosis

+ follow-up

Targeted assays vs holistic approach

Next

generation

metabolic

screening

Times are changing… add functional genome diagnostics

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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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Full metabolite profile:

Highly suspected of xanthinuria

Research Biomarkers Diagnostics

Department of Laboratory Medicine, Radboudumc Integrated Translational Research and Diagnostic Laboratory, 220 fte, yearly budget ~ 28M euro. Close interaction with Dept of Genetics, Pathology and Medical Microbiology

Specialities: • Proteomics, glycomics, metabolomics • Enzymatic assays • Neurochemistry • Cellulair immunotherapy • Immunomonitoring

Areas of disease: • Metabolic diseases • Mitochondrial diseases • Lysosomal /glycosylation disorders • Neuroscience • Nefrology • Iron metabolism • Autoimmunity • Immunodeficiency • Transplantation

In development: • ~500 Biomarkers • Early and late stage • Analytical development • Clinical validation

Assay formats: • Immunoassay • Turbidicity assays • Flow cytometry • DNA sequencing • Mass spectrometry • Experimental human (-ized)

invitro and invivo models for inflammation and immunosuppression

Validated assays*: • ~ 1000 assays • 3.000.000 tests/year

Areas of application: • Personalized healthcare • Diagnosis • Prognosis • Mechanism of disease • Mechanism of drug action

Biomarker development workflow @ Radboudumc

*CCKL accreditation/RvA/EFI

www.laboratorymedicine.nl

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Example: Personalized Healthcare in rare diseases

• 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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Biomarkers in Personalized Healthcare an evolving role

• From only diagnosis

• To Translational Medicine

• To Personalized/Precision/Targeted Medicine

• To Personalized Healthcare

• To Person-centered Health(care)

next

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30

Selfmonitoring

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The future is nearly here …

Personalized advice

Action

Selfmonitor Cloud

Lifestyle Nutrition Pharma

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The future is nearly here …

Measure your brain waves (EEG)

Recognize conditions for maximal concentration or relaxation.

Use device to train.

33

The future is nearly here …

• DIY sequence your genome and/or your microbiome genome

• at a provider, at a pharmacy, at home

• Take your genome to the doctor

• Have a personalized healthcare advice

34

But …

Knowledge and Innovation gap:

1. What to measure?

2. How much should it change?

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

37

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Translation is key in Personalized Healthcare !

Personal profile data

Knowledge

Understanding

Decision

Action

39

Translation is key in Personalized Healthcare !

“I’m afraid you’re

suffering from an

increased IL-1β and

an aberrant miR843

expression”

Adapted from:

40

?

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

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Biomarker innovation gap: some numbers

Data from Thomson Reuters Integrity, 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

Nov 2014: 10,350 biomarkers with 33,863 biomarker uses

42

How to move forward? 1. System Biology

43

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

EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

Relating tissue pharmacology – biomarker - therapy

44

Translating knowledge to field labs

1. Implementation-plan ‘Personalized diagnosis of (pre)diabetic and their lifestyle treatment in Dutch Health care’.

2. Use of Oral Glucose Tolerance Test as a stratification biomarker for (pre)diabetic patients

3. Advice a tailored treatment (lifestyle and/or medical)

4. Monitor added value of stratification

5. Communicate results and lessons learned

Being implemented in 1st line care (region Hillegom, Netherlands)

Alliance “Expedition Sustainable Care,

starting with diabetes”

45

Personalized interventions by Pharma-Nutrition

Ongoing: Shared Innovation Programs through public-private consortia

Higher efficacy / less side effects

46

How to move forward? 2. Interdisciplinary team work

Standardisation, harmonisation, knowledge sharing needed in:

1. Assay development

2. Clinical validation

Example: Biomarker Development Center

Open Innovation Network !

Roadmap Molecular Diagnostics

PPP Grant 4.3M Euro

47

www.radboudumc.nl/research/technologycenters

How to move forward? 2. Interdisciplinary team work

48

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• Proteins • Metabolites • Drugs • PK-PD • Preclinical

• Clinical

• Behavioural • Preclinical

• Animal facility • Systematic review

• Cell analysis • Sorting

• Pediatric • Adult • Phase 1, 2, 3, 4

• Vaccines • Pharmaceutics • Radio-isotopes • Malaria parasites

• Management • Analysis • Sharing • Cloud computing

• DNA • RNA

• Internal • External

• HTA • Evidence-based

surgery • Field lab

• Statistics • Biological • Structural

• Preclinical • Clinical

• Economic viability

• Decision analysis

• Experimental design • Biostatistical advice

• Electronic Health Records • Big Data • Best practice

• In vivo • Functional

diagnostics

About 240 dedicated people working in 17 Technology Centers, ~1500 users (internal, external), ~130 consortia

www.radboudumc.nl/research/technologycenters/

How to move forward? 2. Interdisciplinary team work

50

How to move forward? 2. Interdisciplinary team work

Patient

Caregiver

Insurer

Self-monitoring

Patient

Caregiver

Insurer

Participatory

research

Bas Bloem

Marten Munneke

et al

51

Central

data point

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)

52

Personalized Participatory Pre-emptive

Personalized Health(care)

Ways forward to the Future of Medicine:

• Patients included

• Molecular profiling

• Personal preferences

• System biology

• Personal profiles

• Health informatics

• Personalized therapies by

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)

alain.vangool@tno.nl

alain.vangool@radboudumc.nl

www.linkedIn.com

Biomarker Development Center

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

54