2015 09-14 Precision Medicine 2015, London, 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

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)

4 years med school (NL)

(personalized healthcare, Omics, biomarkers)

4 years applied research institute (NL, EU)

(biomarkers, personalized health, nutrition)

A person / citizen / family man

(adventures in EU, USA, Asia)

1991-1996 (PhD)

1996-1998 (post-doc)

2009-2012 (visiting prof)

1999-2007 2007-2009 2009-2011

2011-now

2011-now

2 Alain van Gool, 2nd Precision Medicine Congress, London, 15 Sept 2015

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

Principle of Personalized/Precision/Targeted Medicine


EC DG for Research and Innovation

Alain van Gool

Brussels, 11 Sept 2012

“But dad, people are more than linear pathways, right ?”

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

Optimal Personalized / Precision / Targeted Medicine

Humans 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

7

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

Personalized health(care)

Is more than ‘just’ targeted medicines

It’s personal !

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


Partners in personalized health(care)


Societal need in efficient personalized health(care)

Source: prof Jan Kremer

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Towards cost effective care, less cure

Alain van Gool, 2nd Precision Medicine Congress, London, 15 Sept 2015

Radboud university medical center

• Nijmegen, The Netherlands

• Mission: “To have a significant impact on healthcare”

• Strategic focus on Personalized and Participatory 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

Alain van Gool, 2nd Precision Medicine Congress, London, 15 Sept 2015 12

Involvement of patients throughout our core activities 1 september 2015: Opening of new curriculum by famous T1DM patient


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


Patient

Radboud Personalized Healthcare

A significant impact

on healthcare

Molecule

Population

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Cell

Tissue

Subgroup


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, ~1600 users (internal, external), ~140 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

• 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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Working with other Technology Networks Region, Netherlands, Europe, world

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About 250 dedicated people working in 18 Technology Centers, ~1600 users (internal, external), ~140 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

• 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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Emerging protein biomarkers


Currently:

• Mostly protein abundance

Emerging:

• Ratio protein isoforms

• Post-translational modifications

• Protein complexes

Connect patient to clinical lab to patient


https://www.youtube.com/watch?v=yhLbuX0H7rg

TML.mp4

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


Example of diagnostic glycoprotein biomarker • Rare metabolic disease cases

• 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}


Discovering new glycoprotein biomarkers

• 1D LC-MS/MS 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:


Intact protein analysis


Bottum-up proteomics

Top-down proteomics

Intact complexome analysis as new biomarker?

• 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

Research Biomarkers Diagnostics

Department of Laboratory Medicine, Radboud univerity medical center 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 • Pediatric oncology • 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

Efficient translation to novel diagnostics

*CCKL accreditation/RvA/EFI

www.laboratorymedicine.nl Alain van Gool, 2nd Precision Medicine Congress, London, 15 Sept 2015 27

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New data !



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

Lucien Engelen

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Specialized validated Apps as ‘stress coach’ {Victor Kallen}


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

Knowledge and Innovation gap:

1. What to measure?

2. How much can it change?

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


Step 1 in Personalized Health(care):

Focus on the end user: the patient / citizen


Translation is key in Personalized Healthcare !

“I’m afraid you’re

suffering from an

increased IL-1β and

an aberrant miR843

expression”

Adapted from:

?



Personal profile data

Knowledge

Understanding

Decision

Action


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


Select personalized therapy

Treatment options

Succ

ess

rate

s

Example shared decision making In prostate cancer



Treatment options

Pro’s

Con’s

Select personalized therapy


The route to Personalized Health


Analogy: route planner

GPS to a location

Amsterdam

Traffic jam

Amsterdam

Route 1 Route 2

= Default Traffic jam near Utrecht Alternative route


Personalized Health(care) planner

GPS to health

Health

Route 1 Route 2

= Default First signs of disease risk

Alternative route

Now

Health risk

Health

Now

Health


Personalized Health(care) model

Analogies to GPS route planner:

• Technology enabled

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

• Success through participation of user

• Personal choice to actively monitor or not

• Commercial competition of tool builders to become 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

Know your personal thresholds and intervention options

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, TNO (2013) Personalized, Participatory, Pre-emptive


Simulate and visualise health interventions {Albert de Graaf}


Open up to system interventions

Higher efficacy / less side effects

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Eg by Pharma-Nutrition


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

Collaboration in Health Informatics

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

Knowledge and Innovation gap:

1. What to measure?

2. How much can it change?

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


Accelerate translation from research to application

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

Gap 3

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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 14 Sept 2015: n = 11,805 biomarkers

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

Gap 3


Build biomarker validation pipelines

Standardisation, harmonisation, knowledge sharing needed in:

1. Assay development

2. Clinical validation

3. Regulatory acceptance

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

Move towards EU funding (2016)

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

Copy best practice

•Nation wide coverage

•66 regional networks

•3000 trained experts

•12 disciplines

prof Bas Bloem dr Marten Munneke



5. Supportive technology

1. Network of experts

2. The patient as partner

4. Transparant quality controle

3. Integral reward for outcome, not production

5 key components of ParkinsonNet


Demonstrated added value

Regular care

ParkinsonNet care

% Hip fracture Cost per patient*

*Hospitals, medication, care at home


Dutch export product …

King Willem Alexander

Bas Bloem

Marten Munneke

Queen Maxima


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Spread the word



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Spread the word


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Spread the word



Spread the word

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

Peter Wielinga

Robert Kleemann

Suzan Wopereis

and many others And funders

CarTarDis


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

2015 09-14 Precision Medicine 2015, London, Alain van Gool

Health & Medicine

Transcript of 2015 09-14 Precision Medicine 2015, London, Alain van Gool