Evaluation and optimization of sample size of neonates and infants for pediatric clinical studies on cefiderocol...

Area of Science:

• Pharmacometrics
• Pediatric Pharmacology
• Clinical Trial Design

Background:

• Pediatric clinical trials face challenges in sample size optimization due to enrollment difficulties in neonates and infants.
• Accurate pharmacokinetic parameter estimation is vital for drug development in pediatric populations.

Purpose of the Study:

• To evaluate sample size optimization for pediatric pharmacokinetic studies of cefiderocol using a model-based optimal design approach.
• To assess the impact of adult pharmacokinetic data and various age categorizations on estimation performance in neonates and infants.

Main Methods:

• Stochastic simulation and estimation were employed to assess population pharmacokinetic parameter estimation performance.
• The study simulated different sample size allocations across age categories, including gestational age, postnatal age, and postmenstrual age (PMA).
• The influence of incorporating adult pharmacokinetic data on parameter estimation accuracy and variance was evaluated.

Main Results:

• Inclusion of adult pharmacokinetic data improved estimation performance, reducing the coefficient of variation (CV) range from 4.9%-593.7% to 2.3%-17.3%.
• Estimating pediatric pharmacokinetic parameters required 15 neonates/infants when using gestational and postnatal age groups (<20% CV).
• Utilizing postmenstrual age (PMA) reduced the required sample size to 7-9 subjects, depending on PMA thresholds (<32, >32 weeks or <37, >37 weeks).

Conclusions:

• A model-based optimal design approach efficiently evaluates sample size for pediatric pharmacokinetic studies.
• Postmenstrual age (PMA) is a key factor in reducing sample size requirements for pharmacokinetic parameter estimation in neonates and infants.
• This methodology provides a valuable framework for designing pediatric clinical trials, particularly those involving very young subjects.