Extrapolation framework Status quo and issues to be resolved EMA extrapolation workshop 2015-09 Christoph Male Austrian alternate PDCO delegate Medical University of Vienna, Department of Paediatrics An agency of the European Union
Objectives • Outline of extrapolation framework (concept paper) • Rationale for extrapolation • Status quo of extrapolation (in PIPs) • Agreed principles • Issues to be resolved 1 Extrapolation framework – status quo
Extrapolation definition Extending information and conclusions available from studies in one or more subgroups of the patient population (source population), or in related conditions or with related medicinal products , to make inferences for another subgroup of the population (target population), or condition or product , thus minimizing the need to generate additional information (types of studies, number of patients required) to reach conclusions for the target population. EMA 2013, Concept paper on extrapolation of efficacy and safety in medicine development
Rationale for extrapolation 1. Avoid ‚unnecessary‘ studies – if extrapolation from other sources is scientifically justified - Ethics / efficiency / ressource allocation 2. Feasibility restrictions - Apply extrapolation principles for rational interpretation of the limited evidence in the context of data available from other sources
Status quo: Evidence base for medicine use in children Full paediatric Off-label Use Extrapolation development Some Reduced PIP Reduced PIP Reduced PIP Full paediatric based on due to based on paediatric data, explicit feasibility expert study practical scientific restrictions jugdement set rationale experience Adult data
Extrapolation Framework 1. Rationale for extrapolation 1. Clinical Context - scientific, clinical practice, ethical issues - feasibility 2. Develop quantitative assumptions on the similarity 2. Extrapolation Concept of the disease, PK/PD and clinical response 3. Define tools (e.g. M&S) and studies needed to 3. Extrapolation Plan complete the knowledge gap and to validate the assumptions 4. In light of emerging data test previous 4. Validation assumptions and if needed modify assumptions 5. Interpretation of the limited data in the target 5. Extrapolation population in the context of information extrapolated from the source population 6. Evaluate impact of violation of the assumptions. Define strategies to mitigate 6. Dealing with uncertainty and risk risks and further evaluate assumptions Adapted from E. Manolis
Pharmacology Disease Clinical response Age-related differences in Age-related differences in Age-related Mechanisms - ADME - aetiology - differences, SOURCE POULATION - mode of action - pathophysiology - applicability, - PD effects (E-R) - manifestation - validation Adults - toxicity - progression of efficacy & safety endpoints - Indicators PB-PK/PD models Quantitative synthesis of natural Quantitative synthesis or Quantitative evidence Extrapolation concept Pop-PK/PD models history data meta-analysis of treatment data Covariates: Disease progression models Disease response models - age, maturation, etc - disease, comorbidity Covariates: Covariates: - age - age existing data - disease types, severity - disease types, severity progressive input of emerging - comorbidity - comorbidity different paediatric age groups data Predict doses to achieve Describe/predict differences in Given similar drug exposure or PD TARGET POPULATION - similar exposure, or natural course of disease response, predict degree of Prediction - similar PD effect, and progression differences in Children, - acceptable safety - efficacy - safety per age group by age group - benefit-risk balance by age group PK studies or Epidemiological data - Design of clinical studies polation PK/PD studies needed for - natural history data - Sample size(s) Extra- plan confirmation of doses - SOC treatment required in target population to conclude on benefit-risk balance in target population in target population
Extrapolation concept Issues to be resolved How to … judge the quality and quantity of existing data? weigh the strength of prior information? quantify similarity of PK/PD, disease progression, clinical response to tx? quantify the uncertainty of extrapolation assumptions? integrate expert judgement in the extrapolation concept?
Pharmacology Disease Clinical response Age-related differences in Age-related differences in Age-related Mechanisms - ADME - aetiology - differences, SOURCE POULATION - mode of action - pathophysiology - applicability, - PD effects (E-R) - manifestation - validation Adults - toxicity - progression of efficacy & safety endpoints - Indicators PB-PK/PD models Quantitative synthesis of natural Quantitative synthesis or Quantitative evidence Extrapolation concept Pop-PK/PD models history data meta-analysis of treatment data Covariates: Disease progression models Disease response models - age, maturation, etc - disease, comorbidity Covariates: Covariates: - age - age existing data - disease types, severity - disease types, severity progressive input of emerging - comorbidity - comorbidity different paediatric age groups data Predict doses to achieve Describe/predict differences in Given similar drug exposure or PD TARGET POPULATION - similar exposure, or natural course of disease response, predict degree of Prediction - similar PD effect, and progression differences in Children, - acceptable safety - efficacy - safety per age group by age group - benefit-risk balance by age group PK studies or Epidemiological data - Design of clinical studies polation PK/PD studies needed for - natural history data - Sample size(s) Extra- plan confirmation of doses - SOC treatment required in target population to conclude on benefit-risk balance in target population in target population
Extrapolation plan Generate a set of rules and methodological tools for the reduction of data requirements ( types of studies, design modifications, number of patients ) in accordance with Predicted degree of similarities Strength of existing evidence (≠ uncertainty) Should confirm the extrapolation concept Should complement the information extrapolated from source population(s)
Inventory of extrapolation approaches used in PIPs • PK/PD studies only (including M&S) • Dose-ranging or dose-titration studies • Non- controlled ‚descriptive‘ efficacy / safety study Data requirements Extrapolation • Controlled study but ‚arbitrary‘ sample size • Larger significance level, lower %age confidence intervals • Studies powered on surrogate endpoint • Intrapolation (bridging) • Modelling prior information from existing data sets (Bayesian, meta-analytic predictive) • etc
Extrapolation plan Issues to be resolved Algorithm(s) linking degree of similarity with reduction in data requirement
EMA extrapolation decision tree (proposal) PBPK/PD models PopPK/PD models to predict age-related differences in PK, PD, toxicity Use data to predict for younger age groups PHARMACOLOGY Exposure-response relationship assumed different N Y PK studies PK/PD studies Validate modelling approaches and assumptions alternatively, adapt predictions and study plan Establish doses to achieve - similar exposure or similar PD response as in source population - and acceptable safety
EMA extrapolation decision tree (continued) Quantitative synthesis or modelling of disease data and clinical response data to predict age-specific differences in - Disease progression - Clinical response to treatment (efficacy, safety, benefit-risk) Use data to predict for younger age groups CLINICAL RESPONSE No Potentially Predicted proof-of-concept qualitatively different quantitatively different Predicted similar from adults (degree) Variable degree of Fully powered reduced study Descriptive pivotal trial measures efficacy & safety study (design, sample size) Confirm predicted differences in disease progression and clinical response Establish positive alternatively, adapt EP concept and plan benefit-risk balance in target population Establish positive benefit-risk balance in target population FULL NO PARTIAL EXTRAPOLATION EXTRAPOLATION EXTRAPOLATION
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