2015 07-03 Nanonext NL Alain van Gool, Amsterdam

Implementing Personalized Health(care): the need to bridge fields

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

Head Biomarkers in Personalized Healthcare

Prof Alain van Gool

NanoNextNL Medicine Theme Day 3 July 2015, Amsterdam

My mixed perspectives in personalized health(care)

8 years academia (NL, UK)

(molecular mechanisms of disease)

13 years pharma (EU, USA, Asia)

(biomarkers, Omics)

3,5 years med school (NL)

(personalized healthcare, Omics, biomarkers)

3,5 years applied research institute (NL, EU)

(biomarkers, personalized health, nutrition)

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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2 Alain van Gool, NanoNext.NL, 3 July 2015


scanadu.m4v

Key aspects of personalized health(care)

‘Let’s stay healthy. If not, how to get healthy?’

1. What to measure?

2. How much can it change?

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


Consider individual differences in life science research

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

Principle of Personalized/Precision/Targeted Medicine

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Example: Oncology - Vemurafenib (PLX-4032, Zelboraf)

Key biomarkers: Disease: Melanoma 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}

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Optimal Personalized / Precision / Targeted Medicine

//tsn.tno.nl/Data/Users/goolajv/Home/Documents/Conferences/2015-04-13 PharmaNutrition, Philadelphia/wasserrutsche.wmv

EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

System biology approach needed in:

Diagnosis Prognosis Treatment Monitoring

People are complex biological systems

Personalized health(care), 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 health(care) 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

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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

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Societal goal of personalized health(care)

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Source: prof Jan Kremer


Partners in personalized health(care)


Key aspects of personalized health(care)

1. What to measure?




Exponential technologies

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

increasing”

We are at the knee of the exponential curve


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Demo room


Exponential developments in biomarker technologies

• 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


Next Generation Sequencing

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

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Mass spectrometry • Example: Glycoproteomics in plasma • Detection of ~12.000 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:

Monique van Scherpenzeel, Dirk Lefeber, Hans Wessels, Alain van Gool Translational Metabolic Laboratory, Radboudumc, unpublished data 23 Alain van Gool, NanoNext.NL, 3 July 2015

Imaging

Slide courtesy of Maroeska Rovers, Peter Friedl, Otto Boerman, Radboudumc

Example: Image-guided surgery: • Use (auto)fluorescence to highlight tumor cells • Specific removal of tumor tissue

• Extend to other imaging modalities in operation room (eg MRI)


The epigenome


The microbiome


‘New’ data

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Personalized advice

Action

Selfmonitor Cloud

Lifestyle Nutrition Pharma

DIY monitoring of vital signs


• 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

DIY sequencing


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• Measure your brain waves (EEG)

• Recognize conditions for maximal concentration or relaxation.

• Use device to train.

DIY brainwave monitoring


DIY blood biomarker analysis

• Measure key biomarkers in one drop of blood at few $ per test panel

• Download data to your smartphone to monitor your own trend


‘insideables’

‘wearables’



Try-outs at REshape Center of Health(care) Innovation

Lucien Engelen


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But …

Knowledge and Innovation gap:

1. What to measure?




Biomarker innovation gaps

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Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

Gap 3

1. Imbalance between biomarker discovery, validation and application

2. Many more biomarkers discovered than available as diagnostic test

3. Limited translation to point-of-care devices


Biomarker innovation gaps: some numbers

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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

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

Gap 3


Way forward: Open Innovation Networks

Shared R&D in biomarkers:

1. Assay development of (diagnostic) biomarkers

2. Clinical biomarker validation (quantification/confirmation, multicenter)

Leading to standardised clinical applications

(Source model: TNO’s Holst Center)


Interdisciplinary biomarker validation

Standardisation, harmonisation, knowledge sharing in:

1. Assay development

2. Clinical validation

Biomarker Development Center

Open Innovation Network !

Roadmap Molecular Diagnostics

PPP Grant 4.3M Euro

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

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 trials

EHR-based research

Statistics

Human physiology

Data stewardship

Molecule

Flow cytometry


Good example of multi-center biomarker validation


But …

Knowledge and Innovation gap:

1. What to measure?




Most important in Personalized Healthcare:

Focus on the end user: the patient / citizen

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The route to Personalized Health(care)


Analogy: TOMTOM

GPS to a location

Utrecht

Amsterdam

Utrecht

Amsterdam

Route 1 Route 2

= Default Traffic jam near Utrecht Alternative route


Personalized Health(care) model

GPS to health

Risk

Health

Route 1 Route 2

= Default First signs of disease risk

Alternative route

Now

Risk

Health

Now

Health



Analogies:

• Technology enabled

• Personal choice to actively monitor or not

• Success through participation of user

• Monitoring should be on the background; only alert when risk

• Commercial competition of tool builders to standard of market leader(s)

• Implementation as standard in society

GPS to health


http://www.google.nl/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRxqFQoTCJ6kuJXIvcYCFYFcFAodO2oP3A&url=http://arstechnica.com/apple/2014/03/ios-in-the-car-becomes-carplay-coming-to-select-dashboards-this-year/&ei=TsSVVZ7FO4G5UbvUveAN&bvm=bv.96952980,d.d24&psig=AFQjCNHK-5180CQ5Wim0GGTOe8N0tIZRIQ&ust=1435964869551370


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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Personalized Participatory Pre-emptive


Translation is key in Personalized Healthcare !

Personal profile data

Knowledge

Understanding

Decision

Action

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System biologist’s world

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β-cell Pathology

gluc Risk factor

{Source: Ben van Ommen, TNO}

therapy



“I’m afraid you’re

suffering from an

increased IL-1β and

an aberrant miR843

expression”

Adapted from:

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?



Select personalized therapy

Treatment options

Succ

ess

rate

s

Example from Prostate cancer patient guide



Treatment options

Pro’s

Con’s

Select personalized therapy


Explore personalized interventions by Pharma-Nutrition

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

Interact – Educate – speak each other language

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Interact – Educate – speak each other language

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Acknowledgements

Ron Wevers

Jolein Gloerich

Hans Wessels

Monique Scherpenzeel

Dirk Lefeber

Leo Kluijtmans

Lucien Engelen

Paul Smits

Maroeska Rovers

Nathalie Bovy

Bas Bloem

and many others

www.radboudumc.nl/personalizedhealthcare

www.radboudumc.nl/research/technologycenters

www.radboudresearchfacilities.nl

[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

Robert Kleemann

Suzan Wopereis

and many others

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And funders

CarTarDis


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