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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2015 04-13 Pharma Nutrition 2015 Philadelphia Alain van Gool
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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
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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2
Outline
3
• 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
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
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
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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
• 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