[Population pharmacokinetics of tacrolimus in Chinese renal transplant patients]
Author(s): Zhang GM, Li L, Chen WQ, Bi SS, Liu X, Zhang XL, Lu W
Affiliation(s): School of Pharmaceutical Science, Peking University, Beliing 100083, China.
Publication date & source: 2008-07, Yao Xue Xue Bao., 43(7):695-701.
Publication type: English Abstract
The goal of this study is to investigate the population pharmacokinetics of oral tacrolimus in Chinese renal transplant patients and to identify possible relationship between covariates and population parameters. Details of drug dosage history, sampling time and concentration of 802 data points in 58 patients were collected retrospectively. Before analysis, the 58 patients were randomly allocated to either the model building group (n=41) or the validation group (n=17). Population pharmacokinetic data analysis was performed using the nonlinear mixed-effects model (NONMEM) program on the model building group. The pharmacokinetics of tacrolimus was best described by a one compartment model with first-order absorption and elimination. Typical values of apparent clearance (CL/F), apparent volume of distribution (V/F) were estimated. A number of covariates including demographic index, clinical index and coadministration of other drugs were evaluated statistically for their influence on these parameters. The final population model related clearance with POD (post operative days), HCT (haematocrit), AST (aspartate aminotransferase) and coadministration of nicardipine and diltiazem. Predictive performance of the final model evaluated with the validation group showed insignificant bias between observed and model predicted concentrations. Typical value of CL/F and V/F was 21.7 L x h(-1) and 241 L, inter-patient variability (RSD) in CL/F and V/F was 41.6% and 49.7%, respectively. The residual variability (SD) between observed and model-predicted concentrations was 2.19 microg x L(-1). The population pharmacokinetic model of tacrolimus in Chinese renal transplant patients was established and significant covariates on the tacrolimus model were identified.