Pharma-Nutrition:

From separate silos towards synergy

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

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, 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

2

2

Outline

3

• Paradigm shifts in pharma

• Personalized Medicine to Personalized Health(care)

• Pharma-Nutrition

4

The development of medicines (past)

• Little understanding of cause of disease

• Use of natural compounds from plant and animal

• Limited testing in laboratory + trial and error in clinic

• Frequently not effacious and/or side effects in patients

• Unacceptable approach (ethical, financial)

5

Example: hormone replacement therapy

• Postmenopausal complaints in women ≥45 years old

• eg hot flushes, loss of concentration and memory

• 1924 First drug for treatment : dried powder of animal ovaria

• Risks of estrogen treatment emerged

• Induction breast and endometrium cancer, cardiovascular risk

• Optimal profile:

• Estrogen-like on CNS and bone

• Anti-estrogen like on breast, endometrium, cardiovascular

• 1929 Discovery of estrogens

• Decrease of estrogens in menopause causes complaints

• Main component of 1924 drug was estrogen

• Estrogen Receptor α (1958) and β (1996), and cofactors

• Needed: Selective Estrogen Receptor Modulators

6

The development of medicines (present)

• A rational and step-wise approach

• ‘reverse pharmacology’

• Cleaner and more specific drugs

which disease?

mechanism? drug target?

activity activity

side effect

activity

production, marketing

active compound

(cell)

active, safe compound

(animal)

safe compound

(healthy human)

active, safe compound

(patiënt)

(reumatoid arthritis)

side effect

side effect

7

Successes of drug development

Antibiotics Vaccins

Reproductive medicine Oncology

8

The development of medicines (present)

which disease?

mechanism? drug target?

activity activity

side effect

activity

production, marketing

active compound

(cell)

active, safe compound

(animal)

safe compound

(healthy human)

active, safe compound

(patiënt)

(reumatoid arthritis)

side effect

side effect

• Per marketed drugs: average 14 years R&D at costs of 1.700.000.000 USD • Return investment of 20% of net income in pharma R&D

60 projects one

successful medicine

Translation laboratory → patient only 1 in 10 projects success

Translational Medicine in pharma

{Source: Van Gool et al, Drug Disc Today 2010}

9

Biomarker-based translational medicine

• Does the compound get to the site of action?

• Does the compound cause its intended pharmacological/ functional effects?

• Does the compound have beneficial effects on disease or clinical pathophysiology?

• What is the therapeutic window (how safe is the drug)?

• How do sources of variability in drug response in target population affect efficacy and safety?

Exposure ?

Mechanism ?

Efficacy ?

Safety ?

Responders ?

Source: van Gool et al, Drug Disc Today 2010

Kumar, van Gool, RSC 2013

10

activity

side

effect

Biomarker data-driven decisions

Target engagement? Effect on disease?

yes yes !

no no

• No need to test current

drug in large clinical trial

• Need to identify a more

potent drug

• Concept may still be

correct

• Concept was not correct

• Abandon approach

• Proof-of-Concept

• Proceed to full

clinical

development

“Stop early, stop cheap”

“More shots on goal”

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Source: Kumar, van Gool, RSC 2013

Rational selection of best targets and drugs works

The 5R’s assessment:

• Right Target

• Right Tissue

• Right Safety

• Right Patients

• Right Commercial Potential

Adopt lessons learned 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

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

Need for Personalized Medicine

Analysis of 108 failures in phase II

Reason for failure Therapeutic area

14

15

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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Optimal targeted / precision medicine

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Precision medicine @USA

President Obama State of Union 2015

Subapproaches of Personalized Medicine

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Diagnosis & prognosis

Dosing Source: Kumar, van Gool, RSC 2013

Subapproaches of Personalized Medicine

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Patient selection

Source: Kumar, van Gool, RSC 2013

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Paradigm shifts in pharma

• 1990’s Genomics revolution: decipher disease mechanisms

From trial and error to ‘reverse pharmacology’

Translational medicine

• 2000’s Biomarker-driven decision making

From blockbuster model to smaller PoC

Pharma R&D model: from internal to external

• 2010’s Improve diagnosis and knowledge of disease

Personalized (targeted, precision) medicine

• 2020 Elucidate individual health/disease status - Big Data

Combine pharma with other therapies

Personalized Health(care)

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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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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Exponential developments in life science 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

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Next Generation Sequencing

Good examples personalized medicine in Oncology:

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

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

Mass spectrometry

• Example: Glycoproteomics in plasma • Optimized procedure: 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

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)

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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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

The epigenome

The microbiome

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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’

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But …

Knowledge and Innovation gap:

1. What to measure?

2. How much should it change?

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

Most important in Personalized Healthcare:

Focus on the end user: the patient

45

Translation is key in Personalized Healthcare !

“I’m afraid you’re

suffering from an

increased IL-1β and

an aberrant miR843

expression”

Adapted from:

46

?

Translation is key in Personalized Healthcare !

Personal profile data

Knowledge

Understanding

Decision

Action

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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

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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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

March 2015

Lab values Clinical outcomes

Patient important outcomes

Pain

Pubmed Search query

Critical appraisal tool

Mobility Fatigue

INTEGRATE-HTA

Intervention

Focus on the end user: the patient

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

Clinical outcomes

Translation is key in Personalized Healthcare !

Select personalized therapy

Treatment options

Su

cce

ss

rate

s

Example from Prostate cancer patient guide

Translation is key in Personalized Healthcare !

Treatment options

Pro

’s

Co

n’s

Select personalized therapy

Explore personalized interventions by Pharma-Nutrition

Shared Innovation Programs through public-private consortia

Higher efficacy / less side effects

55

Explore personalized interventions by Pharma-Nutrition

Double inhibition

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No inhibition

Next: increase system biology knowledge

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β-cell Pathology

gluc Risk factor

{Source: Ben van Ommen, TNO}

therapy

Next: cross-field collaborations in Pharma-Nutrition

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Data

mining

Models

Modelling

Analytics

(Mx, Px, Tx)

Organ-on-

a-chip

Imaging

Academic/ Clinical

Industry

20+ partners

Diagnostics

Pharma Nutrition

20+ partners

Better diagnosis and interventions

Personalized !

20+ partners

10+ partners

Next: cross-field collaborations in Pharma-Nutrition

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Mixed diner 12th April:

• Pharma – Nutrition

• Public – Private

• Netherlands - Spain

Next: bridge innovations across fields

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Year 1

TNO’s system biology projects

Year 2

Year 3

Innovation

Cost-benefit analysis

Stakeholder map

Regulatory landscape map

Biological feasibility

Clinical need/issue

PharmaNutrition

business case

Carlien ter Mors

Laura Han

Jochem Jansen

Next: design sensible Pharma-Nutrition business cases

Finally, be passionate !

My professional passions:

Personalized Health(care)

Biomarkers

Molecular Profiling (Omics)

Future of medicine

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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 others

www.radboudumc.nl/personalizedhealthcare

www.radboudumc.nl/research/technologycenters

www.Radboudresearchfacilities.nl

alain.vangool@tno.nl

alain.vangool@radboudumc.nl

www.linkedIn.com

Slides on 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 others

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And funders

CarTarDis