Efficient strategy for obtaining reliable pharmacokinetic parameters in population compartmental approaches.

Abstract

This study aimed to suggest efficient strategies for obtaining reliable pharmacokinetic (PK) parameters in population compartmental approach (PCA) a early-phase or resource-limited clinical trials with limited data. This study employed plasma concentration of olanzapine, an antipsychotic drug, from a bioequivalence study. To assess bias and precision of PK parameters that were estimated from limited data, this study utilized simulations with the generation of small-size datasets (SSD) and minimal-sampling datasets (MSD) that consisted of limited volunteers and PK samplings per volunteer, respectively. Clearance (CL) estimates were the most robust, volume of the central (Vc) and peripheral compartment (Vp) were moderately affected, and absorption rate constant (Ka) and intercompartmental clearance (Q) were very sensitive with limited dataset. MSD had more impact on the bias and precision of PK parameter estimation than SSD. Performance of PK parameter estimation evaluated by bias and precision from simulation datasets was better in SSD than MSD. This finding implies that collecting more PK samplings is a more efficient strategy than recruiting more volunteers in order to obtain informative results in performing PCA.