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Background <p>Extrapolation-based approaches are widely used in paediatric drug development. These often rely on pharmacokinetic (PK) matching to support inference on the benefit–risk balance (BRB). PK-based extrapolation requires generation of PK data in children. Given ethical and practical challenges in paediatric trials, collected data must be relevant and informative.</p> Methods <p>This work proposes a stochastic simulation–estimation (SSE) approach to optimise key study design factors (number of patients, samples per patient, and sampling times) for paediatric PK studies in extrapolation contexts. Using three illustrative case studies a subcutaneous monoclonal antibody (mosunetuzumab) and two small molecules with intravenous (meropenem) and oral (olanzapine) administration we demonstrate how SSE can be used in paediatric drug development and how it meets regulatory requirements.</p> Results <p>SSE enabled prospective optimisation of paediatric study designs using adult drug development data. For each case drug, designs were identified where key model parameter imprecision (normalised root mean squared error, NRMSE) remained below 30% and bias (relative bias, RBias) below 20%.</p> Conclusions <p>These case studies illustrate how SSE can be used to evaluate paediatric PK study design options in extrapolation settings and provides drug sponsors and regulators with a practical tool to decision-making related to study design optimization.</p>

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Application of a Stochastic Simulation–estimation Approach to Optimize Pharmacokinetic Study Design in the Context of Paediatric Extrapolation: A Tool to Support Decision-making by Drug Sponsors and Regulators

  • 期刊论文

  • 发表日期：

    2026年2月21日

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  • Happy Phanio Djokoto,
  • Lara Ernst,
  • Jean-Michel Dogné,
  • Flora T. Musuamba

摘要

Background

Extrapolation-based approaches are widely used in paediatric drug development. These often rely on pharmacokinetic (PK) matching to support inference on the benefit–risk balance (BRB). PK-based extrapolation requires generation of PK data in children. Given ethical and practical challenges in paediatric trials, collected data must be relevant and informative.

Methods

This work proposes a stochastic simulation–estimation (SSE) approach to optimise key study design factors (number of patients, samples per patient, and sampling times) for paediatric PK studies in extrapolation contexts. Using three illustrative case studies a subcutaneous monoclonal antibody (mosunetuzumab) and two small molecules with intravenous (meropenem) and oral (olanzapine) administration we demonstrate how SSE can be used in paediatric drug development and how it meets regulatory requirements.

Results

SSE enabled prospective optimisation of paediatric study designs using adult drug development data. For each case drug, designs were identified where key model parameter imprecision (normalised root mean squared error, NRMSE) remained below 30% and bias (relative bias, RBias) below 20%.

Conclusions

These case studies illustrate how SSE can be used to evaluate paediatric PK study design options in extrapolation settings and provides drug sponsors and regulators with a practical tool to decision-making related to study design optimization.