A modeling and simulation perspective on extrapolation EMA - - PowerPoint PPT Presentation
A modeling and simulation perspective on extrapolation EMA Workshop on extrapolation of efficacy and safety in medicine development across age groups, 17 18 May 2016, European Medicines Agency, London Ine Skottheim Rusten on behalf of the
EMA Workshop on extrapolation of efficacy and safety in medicine development across age groups, 17 – 18 May 2016, European Medicines Agency, London
Ine Skottheim Rusten
The synergetic value of adding information and means of interpretation to the pool of knowledge
Integrate existing evidence Use tools to enable translation between the population and the individual patient
Expert opinion = estimation or prediction Warning of past events: A change in paradigm!
The philosophy of M&S and why should clinicians and regulators encourage explicit quantitative modeling?
A method to test our understanding
The sign of a mature science
Dose Exposure PD Efficacy and safety Response Response Paediatric models
BSA, allometry)
models
models
C = C(0)*e^(t*k) C = C(0)*e^(t*CL/V) CLchild=CLadult*(BWchild/BWadult)0.75
The value of modelling system data extends beyond product specific product development questions and can facilitate drug development as a whole.
Empirical (Top-down)
Population PK-PD Cross sectional D-R or E-R Longitudinal D-E-R Interventional disease models
Mechanistic (Bottom-up)
Physiologically based PK-PD
Quantitative systems pharmacology Combine methods to use all existing knowledge Optimal design and clinical trial simulations to optimize trial design
Impact on regulatory decision
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Scientific Advice, Supporting Documentation, Regulatory Scrutiny
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Scientific Advice, Supporting Documentation, Regulatory Scrutiny
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Scientific Advice, Supporting Documentation, Regulatory Scrutiny
From EMA-EFPIA Modelling and Simulation Workshop, December 2011
Justify Describe Replace
validation of endpoints
powering, inclusion of PD endpoints, addressing the clinically important gaps with appropriate methodologies)
risk evaluations and extrapolation purposes?
the disease level?
Dose exposure response data Availability of qualified biomarkers and modeling approaches Systems data Methodology to assure continued qualification
models
EMA Workshop on extrapolation of efficacy and safety in medicine development across age groups 17 – 18 May 2016, European Medicines Agency, London
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N=9 paediatric
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100% PK
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1. In mathematics, extrapolation is the process of estimating, beyond the original observation range, the value of a variable on the basis of its relationship with another variable. It is similar to interpolation, which produces estimates between known observations, but extrapolation is subject to greater uncertainty and a higher risk of producing meaningless results. 2. Extrapolation may also mean extension of a method, assuming similar methods will be applicable. 3. Extrapolation may also apply to human experience to project, extend, or expand known experience into an area not known or previously experienced so as to arrive at a (usually conjectural) knowledge of the unknown (e.g. a driver extrapolates road conditions beyond his sight while driving).
DOSE* RESPONSE
DOSE DOSE
*Concentrati
Dose response/PK PD MBMA QSP
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DRUG SYSTEM
European Society for Developmental Perinatal and Pediatric Pharmacology (ESDPPP), Belgrade, 23-26th June 2015
17 7
What it took to extrapolate a compound class:
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19
tested dose) is fundamentally different from between-population extrapolation (BPE; i.e. predicting paediatric PKPD from adults):
could or could not work in BPE
pharmacological responses have been:
in paediatric drug development
paradigm to a systems one:
focus
Professor of Systems Pharmacology University of Manchester, Manchester, UK
Matter of HOW not Matter of IF
(for ADME)
An age‐related trend in the magnitude
However, the study highlighted the clear paucity of the data in children younger than 2 years. Care should be exercised when applying the knowledge of DDIs from adults to children younger than 2 years of age.
Why the trend? Latest fad? Or a true need?
Public Interest: Answ er to an Unmet Need
Filling the void Stopping guess-w ork
How it is done? Integrating system information
Permeability-limited model are available for the intestine, liver, kidney, brain and lung.
What are the challenges? Variable ontogeny (enzymes/transporters)
0.1 1.0 10.0 4 Days 36 Days 1 Year 10 Years
Ratio X(adult/Paed):CYP1A2 (Adults/Paed)
Age
X vs CYP1A2
Renal (male) CYP2D6 CYP3A4 CYP2B6
0.5 2.0 3.0 8.0 20.0 40.0 0.3 1 Day
Relative Importance of Pathw ays: “Ratio of Ratios”!
Pathway A in Paediatrics Pathway A in Adults Pathway B in Paediatrics Pathway B in Adults
Relative Ontogeny =
0.01 0.10 1.00 1 Day 4 Days 36 Days 1 Year 10 Years
Ratio X(adult/Paed):CYP29 (Adults/Paed)
Age
X vs CYP2C9
CYP1A2 CYP3A4 CYP2B6 CYP2D6 CYP2E1 CYP2C8 Renal CYP2C18/19
0.20 0.40 0.50 0.60 2.00 3.00
J Clin Pharmacol 2013; 53: 857–865
Hysteresis
PD Basic Response Compound PK
Effect compartment
X(t) Xe(t)
What are the challenges? Reference point (systemic vs organ)
( )
− × + =
adult adult adult neonate neonate
fu fu 1 ] P [ ] P [ 1 1 fu
In the absence of changes in dynamics
Serum Albumin & Age Serum AAG & Age
10 20 30 40 50 60 0.1 1 10 100 1000 10000 100000
Age (days) Albumin (g/L)
0.2 0.4 0.6 0.8 1 1.2 1.4 0.1 1 10 100 1000 10000 100000
Age (days) AAG (g/L)
What are the challenges? Reference point (free vs bound)
Ontogeny of Plasma Proteins, Albumin and Binding of Diazepam, Cyclosporine and Deltamethrin Sethi; et al Pediatric Research accepted article preview online 16 November 2015;
Plasma Binding Deltamethrin
Absence of info on free local concentrations: Sensitivity???
True vs Apparent PD Differences in Paediatrics
Effect Log Conc Effect Log Conc
Tyrosine hydroxylase(TH)
Rothmond et al., 2012