Use of Clinical Trial Simulations to Compare the Performance of Different Approaches for Population Analyses of Pediatric Pharmacokinetic Data.

The adequate characterization of the pharmacokinetics of a drug used in pediatrics is a mainstay of pediatric development programs and is critical for accurate dose selection in pediatrics. Analysis approaches can impact the estimation and characterization of pediatric pharmacokinetic parameters. Simulations were conducted to compare the performance of different approaches for analyzing pediatric pharmacokinetic data in the presence of extensive data from adult studies. Simulated clinical trial datasets were generated encompassing different scenarios that might be encountered in pediatric drug development. For each scenario, 250 clinical trials were simulated and analyzed using each of the following approaches: (1) estimating pediatric parameters using only pediatric data; (2) fixing specific parameters to adult estimates and estimating the remaining pediatric parameters using only pediatric data; (3) estimating pediatric parameters using adult parameters as informative Bayesian priors; (4) estimating pediatric parameters using combined adult and pediatric datasets with exponents for body weight effects estimated using adult and pediatric data; and (5) estimating pediatric parameters using combined adult and pediatric datasets with exponents for body weight effects estimated using pediatric data only. Each analysis approach was evaluated for its success in the estimation of true pediatric pharmacokinetic parameter values. Results demonstrated that analyzing pediatric data using a Bayesian approach generally performed best and had the lowest probability of significant bias in the estimated pediatric pharmacokinetic parameters among different scenarios evaluated. This clinical trial simulation framework can be used to inform the optimal approach for analyses of pediatric data for other pediatric drug development program scenarios beyond the cases evaluated in these analyses.