BackgroundCiclosporin A (CsA) is an important part of the immunosuppressive regimen in the treatment of renal transplant patients. CsA is typified by a great inter- and intraindividual pharmacokinetic variability, and narrow therapeutic window. Concentrations over the therapeutic window are associated with serious side effects, while concentrations under the therapeutic window are associated with risk of organ rejection. Therapeutic drug monitoring of CsA is therefore necessary.
A pharmacokinetic population model predicts individual pharmacokinetic parameters not only based on patient observations, but also upon population data. The large pharmacokinetic variability of CsA seen in the population as well as significant patient demographics are implemented in such a model. A pharmacokinetic population model of CsA can therefore be a valuable tool used to optimize CsA dosing. The purpose of this study was to develop a pharmacokinetic population model for CsA.
MethodsTwelve hour concentration-time profiles of CsA from 17 renal transplant recipients were used to develop a pharmacokinetic population model using the nonlinear mixed effect approach as implemented in NONMEM. Different compartment models and especially different absorption processes were examined in order to find the best pharmacokinetic population model for CsA. Influence of covariates on the pharmacokinetic parameters was examined in accordance with traditional methods. The complete model was validated using both internal and external methods.
ResultsA 2-compartment model with Erlang distribution as an absorption process was found to describe the pharmacokinetic data best. For the Erlang distribution, the optimal number of lag compartments placed upstream to the central compartment was six. Among the different covariates investigated, only age had a significant influence on the estimation of clearance.
The internal validation process found no individuals with large influence on the pharmacokinetic parameters and the model showed great robustness. In addition, the population model was able to predict individual AUC0-12 in patients excluded from the dataset using limited samplings points within the absorption phase.
An external validation in 10 new renal transplant recipients showed that the pharmacokinetic population model also could predict individual AUC0-12 in an external population with same accuracy as in the internal validation process.
ConclusionA 2-compartment model with Erlang distribution as an absorption process and age as a covariate on clearance described the CsA data best. This population model provides a good basis for the development of a model that can serve as a Bayesian prior when designing dosing regimens in new kidney transplant patients.