A Stochastic Simulation and Estimation-based approach for study design optimization in the context of paediatric extrapolation: A step toward better decision making for drug sponsors and regulators.

Happy Djokoto

A Stochastic Simulation and Estimation-based approach for study design optimization in the context of paediatric extrapolation: A step toward better decision making for drug sponsors and regulators.

Résultats de recherche: Contribution à un événement scientifique (non publié) › Poster › Revue par des pairs

Résumé

Background: Extrapolation is a widely used concept in paediatric drug development, where information from one or more source populations is extended to a target population with limited knowledge. This often involves assessing the (favourable) benefit/risk balance (BRB) based on the similarity of pharmacokinetic (PK) exposures between adults and children. However, PK-based extrapolation requires generating PK data in children, necessitating appropriate clinical studies. Given the ethical and practical challenges in conducting such trials, it is essential to ensure the relevance and informativeness of the collected data.
Aim: In this study, we introduce a model-based approach aimed at optimizing the key factors in study design for paediatric PK studies conducted in extrapolation scenarios. These key factors encompass the number of patients, the frequency of sampling, and the sampling times.
Methods: Using a case study, we employ stochastic simulation estimation (SSE) within the context of drug development to meet regulatory requirements. We used an available Population Pharmacokinetics (Pop PK) model developed for an intravenous small molecule in adult patients. We tested 133 scenarios, ranging from 1 to 100 patients and from 3 to 12 PK samples drawn over 12 hours post-dose with different sampling schemes. We assessed their impact on parameter estimation precision and accuracy using Normalized Root Mean Squared Error (NRMSE) and Relative Bias (Rbias).
Results: Our results showed that with the chosen scenarios, it was possible to describe the impact of the combination of the 3 design factors on values of PK parameter (clearance (CL), volumes of distribution (V1 and V2) and intercompartmental clearance (Q)) estimation as illustrated in the figure below for the accuracy characterized using Mean Absolute Percentage Error (MAPE(%)).

langue originale	Anglais
Etat de la publication	Publié - 1 nov. 2023

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title = "A Stochastic Simulation and Estimation-based approach for study design optimization in the context of paediatric extrapolation: A step toward better decision making for drug sponsors and regulators.",

abstract = "Background: Extrapolation is a widely used concept in paediatric drug development, where information from one or more source populations is extended to a target population with limited knowledge. This often involves assessing the (favourable) benefit/risk balance (BRB) based on the similarity of pharmacokinetic (PK) exposures between adults and children. However, PK-based extrapolation requires generating PK data in children, necessitating appropriate clinical studies. Given the ethical and practical challenges in conducting such trials, it is essential to ensure the relevance and informativeness of the collected data.Aim: In this study, we introduce a model-based approach aimed at optimizing the key factors in study design for paediatric PK studies conducted in extrapolation scenarios. These key factors encompass the number of patients, the frequency of sampling, and the sampling times.Methods: Using a case study, we employ stochastic simulation estimation (SSE) within the context of drug development to meet regulatory requirements. We used an available Population Pharmacokinetics (Pop PK) model developed for an intravenous small molecule in adult patients. We tested 133 scenarios, ranging from 1 to 100 patients and from 3 to 12 PK samples drawn over 12 hours post-dose with different sampling schemes. We assessed their impact on parameter estimation precision and accuracy using Normalized Root Mean Squared Error (NRMSE) and Relative Bias (Rbias).Results: Our results showed that with the chosen scenarios, it was possible to describe the impact of the combination of the 3 design factors on values of PK parameter (clearance (CL), volumes of distribution (V1 and V2) and intercompartmental clearance (Q)) estimation as illustrated in the figure below for the accuracy characterized using Mean Absolute Percentage Error (MAPE(%)).",

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N2 - Background: Extrapolation is a widely used concept in paediatric drug development, where information from one or more source populations is extended to a target population with limited knowledge. This often involves assessing the (favourable) benefit/risk balance (BRB) based on the similarity of pharmacokinetic (PK) exposures between adults and children. However, PK-based extrapolation requires generating PK data in children, necessitating appropriate clinical studies. Given the ethical and practical challenges in conducting such trials, it is essential to ensure the relevance and informativeness of the collected data.Aim: In this study, we introduce a model-based approach aimed at optimizing the key factors in study design for paediatric PK studies conducted in extrapolation scenarios. These key factors encompass the number of patients, the frequency of sampling, and the sampling times.Methods: Using a case study, we employ stochastic simulation estimation (SSE) within the context of drug development to meet regulatory requirements. We used an available Population Pharmacokinetics (Pop PK) model developed for an intravenous small molecule in adult patients. We tested 133 scenarios, ranging from 1 to 100 patients and from 3 to 12 PK samples drawn over 12 hours post-dose with different sampling schemes. We assessed their impact on parameter estimation precision and accuracy using Normalized Root Mean Squared Error (NRMSE) and Relative Bias (Rbias).Results: Our results showed that with the chosen scenarios, it was possible to describe the impact of the combination of the 3 design factors on values of PK parameter (clearance (CL), volumes of distribution (V1 and V2) and intercompartmental clearance (Q)) estimation as illustrated in the figure below for the accuracy characterized using Mean Absolute Percentage Error (MAPE(%)).

AB - Background: Extrapolation is a widely used concept in paediatric drug development, where information from one or more source populations is extended to a target population with limited knowledge. This often involves assessing the (favourable) benefit/risk balance (BRB) based on the similarity of pharmacokinetic (PK) exposures between adults and children. However, PK-based extrapolation requires generating PK data in children, necessitating appropriate clinical studies. Given the ethical and practical challenges in conducting such trials, it is essential to ensure the relevance and informativeness of the collected data.Aim: In this study, we introduce a model-based approach aimed at optimizing the key factors in study design for paediatric PK studies conducted in extrapolation scenarios. These key factors encompass the number of patients, the frequency of sampling, and the sampling times.Methods: Using a case study, we employ stochastic simulation estimation (SSE) within the context of drug development to meet regulatory requirements. We used an available Population Pharmacokinetics (Pop PK) model developed for an intravenous small molecule in adult patients. We tested 133 scenarios, ranging from 1 to 100 patients and from 3 to 12 PK samples drawn over 12 hours post-dose with different sampling schemes. We assessed their impact on parameter estimation precision and accuracy using Normalized Root Mean Squared Error (NRMSE) and Relative Bias (Rbias).Results: Our results showed that with the chosen scenarios, it was possible to describe the impact of the combination of the 3 design factors on values of PK parameter (clearance (CL), volumes of distribution (V1 and V2) and intercompartmental clearance (Q)) estimation as illustrated in the figure below for the accuracy characterized using Mean Absolute Percentage Error (MAPE(%)).

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