Curvature-adjusted optimal design of sampling times for the inference of pharmacokinetic compartment models

In pharmacokinetics, compartment models are often used to describe the time course of blood concentration after the administration of a drug. In this article, we propose an optimal design criterion for precise estimation of parameters included in the compartment model and illustrate the non-sequential design of sampling times of blood drug concentration data in individual pharmacokinetics. The proposed optimal design criterion minimizes the determinant of the mean-squared error matrix of the parameter estimator that is quadratically approximated by the curvature array. Therefore, the proposed criterion considers the intrinsic and parameter-effects nonlinearity underlying the compartment model, and so is applicable in a pharmacokinetic experiment where the sample size of the blood drug concentration data is quite small.