Robust parameter estimation for physiologically based pharmacokinetic model of Tegafur with dissolution dynamics

Abstract

Physiologically based pharmacokinetics (PBPK) model can predict absorption, distribution, elimination and metabolism in a drug delivery system. While PBPK model is generally expressed as a set of ordinary differential equations with a large number of parameters, in vivo experimental data are often noisy and sparse. This makes it difficult to estimate parameters with conventional least squares approach. To address this problem and improve prediction accuracy of PBPK model, this work proposes a covariance based parameter estimation scheme and dissolution dynamics model for PBPK of Tegafur after oral administration. Unknown parameters of the PBPK model are estimated by a maximum a posteriori method where the covariance matrix of the parameter estimates is calculated by simulation. Its diagonal entries represent the prior information of the parameters, while off-diagonal entries represent correlations among the parameters. The proposed estimation scheme demonstrated an improved performance in terms of variance of the parameter estimates and concentration predictions. In addition, incorporation of dissolution dynamics provided more accurate prediction than conventional PBPK models.