Population pharmacokinetic analysis of meropenem in Japanese adult patients

Meropenem is frequently employed as an empirical treatment for serious infections, but there has been no report on its population pharmacokinetic parameters for Japanese patients. Our aim is to undertake a population pharmacokinetic analysis of meropenem using non-linear mixed effects model (NONMEM).

Data from 68 patients were analysed via NONMEM with the first-order method. The participants' covariates, including gender, age, actual body weight, serum creatinine, serum albumin, serum total protein and creatinine clearance, were analyzed by the forward inclusion and backward elimination method to identify their potential influence on meropenem pharmacokinetics. The adequacy of the constructed model was assessed by goodness-of-fit plots and the precision of the parameter estimated at each step of the model development. To assess the robustness of the estimated parameter, bootstrap analysis was performed.

The data were best described by a one-compartment model. The serum creatinine values modified by the below normal limit in our hospital (mSCR) were an influential covariate for clearance (CL): CL (L/h) = 11·1 × (mSCR/0·7)(-1). The volume of distribution was estimated as 33·6 L. The coefficient of variation of the inter-individual variability of CL and the residual variability were 52·1% and 0·827% μg/mL, respectively. A comparison of the population pharmacokinetic parameters of meropenem in the final model estimated in NONMEM with original data, and 1000 bootstrap samples shows that both sets of estimates were comparable, thereby indicating the robustness of the proposed model.

A population pharmacokinetic model that satisfactorily described the disposition and variability of meropenem in our Japanese population is described. NONMEM analysis showed that the clearance of meropenem depended on modified serum creatinine. The results of this study should help Japanese patients on meropenem by improving the prediction accuracy of dosing using the Bayesian method.