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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
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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
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Outline
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• Paradigm shifts in pharma
• Personalized Medicine to Personalized Health(care)
• Pharma-Nutrition
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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)
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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
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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
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Successes of drug development
Antibiotics Vaccins
Reproductive medicine Oncology
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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
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Translational Medicine in pharma
{Source: Van Gool et al, Drug Disc Today 2010}
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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
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activity
side
effect
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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
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Rational selection of best targets and drugs works
The 5R’s assessment:
• Right Target
• Right Tissue
• Right Safety
• Right Patients
• Right Commercial Potential
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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
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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
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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
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Subapproaches of Personalized Medicine
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Diagnosis & prognosis
Dosing Source: Kumar, van Gool, RSC 2013
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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)
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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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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
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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
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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)
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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
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The epigenome
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The microbiome
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Personalized advice
Action
Selfmonitor Cloud
Lifestyle
Nutrition
Pharma
DIY monitoring of vital signs
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• 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
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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
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‘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?
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Most important 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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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
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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
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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
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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
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Translation is key in Personalized Healthcare !
Select personalized therapy
Treatment options
Su
cce
ss
rate
s
Example from Prostate cancer patient guide
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Translation is key in Personalized Healthcare !
Treatment options
Pro
’s
Co
n’s
Select personalized therapy
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Explore personalized interventions by Pharma-Nutrition
Shared Innovation Programs through public-private consortia
Higher efficacy / less side effects
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Explore personalized interventions by Pharma-Nutrition
Double inhibition
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No inhibition
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Next: increase system biology knowledge
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β-cell Pathology
gluc Risk factor
{Source: Ben van Ommen, TNO}
therapy
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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
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Next: cross-field collaborations in Pharma-Nutrition
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Mixed diner 12th April:
• Pharma – Nutrition
• Public – Private
• Netherlands - Spain
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Next: bridge innovations across fields
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Year 1
TNO’s system biology projects
Year 2
Year 3
Innovation
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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
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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
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