Effects of multiple-trough sampling design and algorithm on the estimation of population and individual pharmacokinetic parameters / 药学学报

ABSTRACT

The purpose of this study is to investigate the effects of multiple-trough sampling design and nonlinear mixed effect modeling (NONMEM) algorithm on the estimation of population and individual pharmacokinetic parameters. Oxcarbazepine and tacrolimus were used as one-compartment and two-compartment model drugs, respectively. Seven sampling designs were investigated using various number of trough concentrations per individual ranging from 1-4. Monte Carlo simulations were performed to produce state-steady trough concentrations. One-compartment model was used to fit simulated data from oxcarbazepine and tacrolimus. The accuracy and precision of the estimated parameters were evaluated using the median prediction error (PE), the median absolute PE and boxplot. The results indicated that trough concentrations could yield reliable estimates of apparent clearance (CL/F). For oxcarbazepine, as the number of trough concentrations per subject increased, the accuracy and precision of CL/F, between-subject variability (BSV) of CL/F and residual variability (RUV) tended to be improved. For tacrolimus, however, although no improvement were observed in the accuracy of CL/F and BSV of CL/F, the PE distribution ranges were significantly narrowed and the RUV estimates were less bias and imprecise. In terms of algorithm, Monte Carlo importance sampling (IMP) and IMP assisted by mode a posteriori estimation (IMPMAP) were consistently better than other methods. Additionally, the sampling design had no significant effects on the individual parameter estimates, which were only depended on the interaction between BSV and RUV in various algorithms. Decreased in BSV and RUV levels can improve the accuracy and precision of the estimation for both population and individual pharmacokinetic parameter estimates.