2015 06-02 Steering group 'Personalized Medicine: eligible or not'

Implementation of Personalized Medicine

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

Head Biomarkers in Personalized Healthcare

Bijeenkomst begeleidingscommissie “Personalized medicine: eligible or not?” 2 June 2015

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)

3,5 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, “Personalized Medicine: eligible or not?”, 2 June 2015

Consider individual differences in personalized medicine

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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

Subapproaches of Personalized Medicine

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Prognosis & prediction

Dosing Source: Kumar, van Gool, RSC 2013


Subapproaches of Personalized Medicine

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Patient selection

Source: Kumar, van Gool, RSC 2013


Next Generation Sequencing

Good examples personalized medicine in Oncology:

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

• Issue with drug resistancy in several solid tumors

Also beyond the oncology field:

• 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


Rational selection of best targets and biomarkers works

The 5R’s assessment:

• Right Target

• Right Tissue

• Right Safety

• Right Patients

• Right Commercial Potential


Adopt Personalized Medicine 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

Companion Diagnostics in drug labels

Metabolism

Efficacy or safety

Source: www.fda.gov (June 2013)


= molecular biomarker that indicates the right patient for right drug at right dose at right time

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


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 12 Alain van Gool, “Personalized Medicine: eligible or not?”, 2 June 2015

Example: Personalized Medicine 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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Imaging

Slide courtesy of Profs 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)



Optimal personalized / targeted / precision medicine

Biomarkers in Personalized Health(care) an evolving role

• From only diagnosis

• To Translational Medicine

• To Personalized/Precision/Targeted Medicine

• To Personalized Health(care)

present

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Goal of Personalized Health(care)

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Source: prof Jan Kremer


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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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


Personalized Health(care) model

Analogies:

• Technology enabled

• Personal choice to actively monitor or not

• Success through participation of user

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

• Implementation as standard in society

P4 medicine: Participatory, Predictive, Preventive, Personalized

GPS to health


‘New’ data

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But …

Knowledge and Innovation gap:

1. What to measure?

2. How much can it change?

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


Biomarker innovation gaps

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

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Gap 3


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


Drive biomarker validation through consortia

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

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

Most important for biomarkers in Personalized Healthcare:

Focus on the end user: the patient

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Translation is key in Personalized Healthcare !

“I’m afraid you’re

suffering from an

increased IL-1β and

an aberrant miR843

expression”

Adapted from:

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?



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Personal profile data

Knowledge

Understanding

Decision

Action

Translation is key in Personalized Healthcare ! Treatment options

Pro’s

Con’s

Select personalized therapy


Personalized dietary advice

YOU HAVE:

Variation in gene

FADS1

YOU NEED:

Increase omega-3 /

omega-6 ratio

Personal information Health management

program Personalized advice

ADVICE:

Include fatty fish,

pacific oysters, oils,

seeds and vegetables

in your diet


Source: Suzan Wopereis et al, TNO

http://www.google.nl/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=-9Tx2pPQEUuJlM&tbnid=wtcQDMKz-EzpJM:&ved=0CAUQjRw&url=http://www.biologicnr.com/human-alternatives-natural-drug-sciences-hands/&ei=nzVmUqD7K4XpswbJh4CQCA&bvm=bv.55123115,d.d2k&psig=AFQjCNGiAXmKd_FZrNBPUVpxDWjwgjksAQ&ust=1382516497785237

http://cdniphone.i-culture.nl/wp-content/uploads/2012/04/Delhaize.jpg

Patient

Radboud Personalized Healthcare

A significant impact

on healthcare

Molecule

Population

Personalized Healthcare @Radboudumc

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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

Suzan Wopereis

Bas Kremer

Lars Verschuren

Ivana Bobeldijk

Marjan van Erk

Carina de Jongh

Peter van Dijken

Robert Kleemann

and many others

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And funders

CarTarDis


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

2015 06-02 Steering group 'Personalized Medicine: eligible or not'

Health & Medicine

Transcript of 2015 06-02 Steering group 'Personalized Medicine: eligible or not'