Objectives: The selection of sample times for a pharmacokinetic study is important when trapezoidal integration (e.g. non-compartmental analysis) is used to determine the area under the concentration-time curve (AUC). The aim of this study was to develop an algorithm that determines optimal times that provide the most accurate AUC by minimising trapezoidal integration error.
Methods: The algorithm required initial single individual or mean pooled concentration data but did not specifically require a prior pharmacokinetic model. Optimal sample intervals were determined by minimising trapezoidal error using a genetic algorithm followed by a quasi-Newton method. The method was evaluated against simulated and clinical datasets to determine the method's ability to estimate the AUC.
Key findings: The sample times produced by the algorithm were able to accurately estimate the AUC of pharmacokinetic profiles, with the relative AUC having 90% confidence intervals of 0.919-1.05 for profiles with two-compartment kinetics. When comparing the algorithm with rich sampling (e.g. phase I trial), the algorithm provided equivalent or superior sample times with fewer observations.
Conclusions: The creation of the algorithm and its companion web application allows users with limited pharmacometric or programming training can obtain optimal sampling times for pharmacokinetic studies.
Keywords: area under the curve; non-compartmental analysis; optimal sampling; pharmacokinetics; simulation studies.
© 2019 Royal Pharmaceutical Society.