2016 01-19 University Twente, Enschede, Alain van Gool

Personalized Health(care): more than just targeted medicines

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

Senior Scientist Integrator Biomarkers

Prof Alain van Gool

University Twente Enschede, 19 Jan 2016

My background in personalized health(care)

8 years academia (NL, UK)

(molecular mechanisms of disease)

13 years pharma (EU, USA, Asia)

(biomarkers, Omics)

4 years med school (NL)

(personalized healthcare, Omics, biomarkers)

4 years applied research institute (NL, EU)

(biomarkers, personalized health, nutrition)

1991-1996 (PhD)

1996-1998 (post-doc)

2009-2012 (visiting prof)

1999-2007 2007-2009 2009-2011

2011-now

2011-now (prof)

2

A person / citizen / family man (adventures in EU, USA, Asia)

Alain van Gool, University Twente, Enschede, 19 Jan 2016

3 Alain van Gool, University Twente, Enschede, 19 Jan 2016

Consider individual differences in life science research

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Source: Chakma, Journal of Young Investigators, 16, 2009

Principle of Personalized/Precision/Targeted Medicine

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Molecular biomarkers as key drivers to select right patient for right drug at right dose at right time


Example: Personalized Medicine in melanoma

Key biomarkers: Stratification: BRAFV600E DNA mutation assay 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}


Emerging companion diagnostics

Good examples personalized medicine in Oncology and Neurosciences:

• Cyp450, Her2/neu, BRCA, BRAF, EGFR, EML4/ALK, etc

Emerging companion diagnostics, also linked to non-drug therapies:

• Volker: Intestinal surgery → XIAP → Cord blood

• Beery twins: Cerebral palsy → SPR → Diet 5HTP

• Wartman: Leukemia → FLT3 → Sunitinib

• Gilbert: Healthy → BRCA → Mas/Ovarectomy

• Snyder: T2Diabetes → GCKR, KCNJ11 → Diet, exercise

• Lauerman: Scotoma, leg → JAK2 → Aspirin

• Bradfield: Healthy → CDH1 → Gastrectomy

Coming up: metabolic biomarkers, imaging biomarkers


Rational selection of best targets and biomarkers works

The 5R’s assessment:

• Right Target

• Right Tissue

• Right Safety

• Right Patients

• Right Commercial Potential


Adopt rational target/biomarker selection in pharma research

CarTarDis = Cardiovascular Target Discovery Public-private partnership, 13 partners, 8 countries, project budget 8.0M Eur Started 1 Oct 2013 for 4 years Adopting AstraZeneca’s 5R strategy in drug target selection

(Coordinator)

CarTarDis.eu

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Optimal Personalized / Precision / Targeted Medicine

People are more than linear pathways

People are more than linear pathways

{Source: Barabási 2007 NEJM 357; 4}

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


Personalized health(care) in a systems view

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Societal need in efficient personalized health(care)

{Source: prof Jan Kremer}

Towards cost effective care, less cure


Personal need in efficient personalized health(care)

It’s personal !

‘I want to stay healthy.’ ‘If not, how do I get healthy?’


A changing world in 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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My route to Personalized Health(care)


Analogy: TOMTOM

GPS to a location

Amsterdam

Traffic jam

Amsterdam

Route 1 Route 2

= Default Traffic jam near Utrecht Alternative route


Personalized Health(care) model

GPS to health

Health

Route 1 Route 2

= Default First signs of disease risk

Alternative route

Now

Disease risk

Health

Now

Health


Key aspects of personalized health(care)

‘I want to stay healthy. If not, how do I get healthy?’

1. What to measure?

2. How much can it change?

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


1. What to measure?

Exponential technological developments • Next generation sequencing

• DNA, RNA • Risk analysis and therapy selection

• Mass spectrometry • Proteins, metabolites • Monitoring of disease and treatment effects

• Imaging • Non invasive images, real time • Spatial view of intact organs and organisms

500

1000

1500

2000

m/z

5 10 15 20 25 30 35 40 Time [min]


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

Department of Laboratory Medicine

*CCKL accreditation/RvA/EFI

www.laboratorymedicine.nl

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Alain van Gool, University Twente, Enschede, 19 Jan 2016 23

Emerging protein biomarkers

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Current diagnostic protein assays:

• Mostly protein abundance

Emerging:

• Post-translational modifications

• Ratio protein isoforms

• Protein complexes


Glycomics

Intact glycoproteins

Free glycans

Glycopeptides 500

750

1000

1250

1500

1750

m/z

10 15 20 25 30 35 40 Time [min]

PGM1 profile

CID fragmentation spectrum


Discovering new glycoprotein biomarkers

• 1D LC-MS/MS glycoproteomics in plasma • Detection of 100K features in one scan • ~20.000 unique deconvoluted monoisotopic masses per single analysis

(> 50% are glycopeptides)

500

1000

1500

2000

m/z

5 10 15 20 25 30 35 40 Time [min]

Proof of principle study:


New diagnostic glycoprotein biomarker • Rare metabolic disease cases (liver disease and dilated cardiomyopathy)

• Combination glycoproteomics and exome sequencing

• Outcome 1: Explanation of disease

• Outcome 2: Dietary intervention as succesful personalized therapy

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

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

Genomics Glycomics Metabolomics

{Monique van Scherpenzeel, Dirk Lefeber}


Intact protein analysis

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Bottum-up proteomics

Top-down proteomics


Intact complexome analysis as new biomarker?

Proof of principle study: • Native tissue biopsies • Isolate intact membrane complexes • Separate and isolate complexes using native gels • LC-MS/MS analysis of intact proteins • Data analysis

Tissue 1 (n=3)

Tissue 2 (n=3)

Subunit

Subunit – tissue 1

Subunit – tissue 2

• Identified protein sequence of subunit • Deduce simulated sequences from database • Determine fit with experimental data

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Next Generation Sequencing

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


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 Full metabolite profile: Highly suspected of xanthinuria

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New data (generators, owners)


However …

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

Gap 3

• Too much biomarker discovery • Too little development to application

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Biomarker innovation gaps!


Biomarker innovation gaps: some numbers

5 biomarkers/ working day

1 biomarker/ 1-3 years

1 biomarker/ 3-10 years

?

Eg Biomarkers in time: Prostate cancer May 2011: n= 2,231 biomarkers Nov 2012: n= 6,562 biomarkers Oct 2013: n= 8,358 biomarkers Nov 2014: n= 10,350 biomarkers Oct 2015: n = 11,856 biomarkers

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

Gap 3


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}


Build biomarker validation pipelines

Standardisation, harmonisation, knowledge sharing in:

1. Assay development

2. Clinical validation

NL Roadmap Molecular Diagnostics (2012) NL Grant 4.3M Eur (2014)


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

Ongoing independent biomarker activities

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Europe

USA

{Asadullah et al, Nature Reviews Drug Discovery, Dec 2015}


The Good Biomarker Practice initiative

Join forces among Europe’s major academic infrastructures + industry to:

1. Establish “Good Biomarker Practice” guidelines

- on translational research, biomarker technologies, biobanking, data stewardship.

2. Efficiently execute high quality biomarker projects

- work together in clinical validation and development of probable biomarkers.

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1. What to measure?




2. How much can it change? Personalized Intervention

of patients-like-me Personal thresholds of persons-like-me

Big Biomarker Data

Molecular Non-molecular Environment …

Ho

meo

sta

sis

A

llo

sta

sis

D

isease

Time

Disease

Health

Selfmonitoring

Adapted from Jan van der Greef, TNO (2013)

Personal profile

Personalized health

Personalized medicine


Example personal profile-based patient assessment

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

Concept:

• Continuous monitoring (n=1)

• Routine biomarkers to alert

• Omics to explain

• Early intervention


healthy disease disease + treatment

Interpret data with self-normalisation

Subgroups

100%

Normalisation of responders


Quality of self-testing

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Test, interpret, advice

“Post-traumatic Test Syndrome” ?




1. What to measure?





Personal profile data

Knowledge

Understanding

Decision

Action


Translation is key in Personalized Healthcare !

“I’m afraid you’re

suffering from an

increased IL-1β and

an aberrant miR843

expression”

Adapted from:

?


Lab values Clinical outcomes

Pain Mobility Fatigue

INTEGRATE-HTA

Objectives patient and clinican may be different

R van Hoorn, W Kievit, M Tummers, GJ van der Wilt

How to do optimal shared decision making?

Intervention

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Objectives patient and clinican may be different

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Select personalized therapy


Shared decision making


Treatment options

Succ

ess

rate

s

Example from Prostate cancer patient guide

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{Source: Peep Stalmeier}


Shared decision making

Treatment options

Pro

’s

Co

n’s


Example from Prostate cancer patient guide

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{Source: Peep Stalmeier}


Flipping the coin

Via the ónly constante in

healthcare: The patient Via HC ICT systems

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{Source: Lucien Engelen}


http://www.hereismydata.com/

• 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

Radboud university medical center

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Radboud campus: - Radboudumc - Radboud University - Hogeschool Arnhem Nijmegen - Max Planck Institute - Multiple companies


Patient

Radboud Personalized Healthcare

A significant impact

on healthcare

Molecule

Population


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


Research themes and institutes

www.radboudumc.nl/Research/Themes/Pages/default.aspx


Orientation across the spectrum from molecule to man to population

Ori

enta

tio

n a

cro

ss

the

spec

tru

m o

f d

isea

ses

Researcher

Research Theme

Te

chn

olo

gy

C

ente

r

Research support by Technology


External role

Internal role

• Knowledge hub for technological expertise • Maximise use of available technical capabilities and knowledge (‘duurzaamheid’) • Advise scientists with technological expertise • Advise management on strategic investments and opportunities • Drive innovations by working with each other, theme’s and Valorisation

• Easy access to Radboudumc’s technological expertise • Represent Radboudumc as one in external technology networks • Increase funding (grants, contract research) with Valorisation

Internal / external role

Radboudumc Technology Centers

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www.radboudumc.nl/research/technologycenters

Genomics

Bioinformatics

Animal studies

Stem cells

Translational neuroscience

Image-guided treatment

Imaging

Microscopy

Biobank

Health economics

Mass Spectrometry

Radboudumc Technology

Centers Investigational

products

Clinical studies

EHR-based research

Statistics

Human performance

Data stewardship

Molecule

Flow cytometry


About 250 dedicated people working in 18 Technology Centers, ~2000 users (internal, external), ~150 consortia www.radboudumc.nl/research/technologycenters/

• 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 • Cyclotron • Radio-isotopes • Malaria parasites

• Management • Analysis • Sharing • Cloud computing

• DNA • RNA

• Internal • External

• Early 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

• iPSC • Organoids

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Access

Combination of • Science • Technology • Business • Innovation • Impact in health

www.radboudumc.nl/research/technologycenters/

(1H2015: 1.900 unique visitors, 26.000 page views) 62 Alain van Gool, University Twente, Enschede, 19 Jan 2016

Working together on the Radboud campus

(Spin-out) companies


https://www.google.nl/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRxqFQoTCO74-vG96ccCFQK1FAodwiYJnw&url=https://curastagao.wordpress.com/2013/05/22/jaarlijks-zelfde-aantallen-van-nhtv-en-han/&psig=AFQjCNHwYTFOi0x4_qdtM16YHQ3oiXdhkw&ust=1441871985852225

Radboud Research Facilities • Shared facilities across Radboud campus, also made part of Gelderland facilities • Initiated by funding 6.2M Eur Gelderland + 6.2M Eur Radboud University/ Radboudumc

www.ru.nl/radboudresearchfacilities/ 64 Alain van Gool, University Twente, Enschede, 19 Jan 2016

Working with other networks Region, nation, Europe, world


Takehome message

• Strategic focus on implementing Personalized Healthcare

• Strong technological and methodological infrastructure

• Continuous exploration of functional networks


Acknowledgements

Ron Wevers

Jolein Gloerich

Hans Wessels

Dirk Lefeber

Monique Scherpenzeel

Leo Kluijtmans

Lucien Engelen

Nathalie Bovy

Paul Smits

Maroeska Rovers

Bas Bloem

the Technology Centers

and many others

www.radboudumc.nl/personalizedhealthcare

www.radboudresearchfacilities.nl

www.radboudumc.nl/research/technologycenters

[email protected]

[email protected]

www.linkedIn.com

www.slideshare.net/alainvangool

Many collaborators

Jan van der Greef

Ben van Ommen

Bas Kremer

Lars Verschuren

Ivana Bobeldijk

Marjan van Erk

Carina de Jongh

Peter van Dijken

Peter Wielinga

Robert Kleemann

Suzan Wopereis

and many others And funders

CarTarDis


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

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www.scanadu.com

scanadu.m4v

2016 01-19 University Twente, Enschede, Alain van Gool

Health & Medicine

Transcript of 2016 01-19 University Twente, Enschede, Alain van Gool