Population pharmacokinetics and bayesian estimator of cyclosporine in pediatric renal transplant patients.

摘要

Cyclosporine A (CsA) is an immunosuppressive drug widely used in pediatric renal graft recipients. Its large interindividual pharmacokinetic variability and narrow therapeutic index render therapeutic drug monitoring necessary. However, information about CsA pharmacokinetics is scarce and no population pharmacokinetic (popPK) studies in these populations have been reported so far. to the objectives of this study were 1) to develop a PKpop model and identify the individual factors influencing the variability of CsA pharmacokinetics in pediatric kidney recipients; and 2) to build a Bayesian estimator allowing the estimation of the main PK parameters and exposure indices to CsA on the basis of a limited sampling strategy (LSS). The popPK analysis was performed using the NONMEM program. A total of 256 PK profiles of CsA collected in 98 pediatric renal transplant patients (mean age 9.7 +/- 4.5 years old) within the first year posttransplantation were studied. A 2-compartment model with first-order elimination, and Erlang distribution to describe the absorption phase, fitted the data adequately. For Bayesian estimation, the best LSS was determined based on its performance in estimating area under the concentration-time curve (AUC0-12h) and validated in an independent group of 20 patients. The popPK analysis identified body weight and posttransplant delay as individual factors influencing the apparent central volume of distribution and the apparent clearance, respectively. Bayesian estimation allowed accurate prediction of AUC0-12h using predose, C1h, and C3h blood samples with a mean bias between observed and estimated AUC of 0.5% +/- 11% and good precision (root mean square error = 10.9%). This article reports the first popPK study of CsA in pediatric renal transplant patients. It confirms the reliability and feasibility of CsA AUC estimation in this population. The body weight and the posttransplantation delay were identified to influence PK interindividual variability of CsA and were included in the Bayesian estimator developed, which could be helpful in further clinical trials.