Background and objective: The pharmacokinetic disposition of ciclosporin shows great intra- and interpatient variability, and that combined with a narrow therapeutic window makes therapeutic drug monitoring of ciclosporin necessary. The nonlinear mixed-effects population pharmacokinetic program NONMEM predicts individual pharmacokinetic parameters based not only on individual patient observations but also on population characteristics and the patient's covariates. The aim of this model development is to potentially use it in the clinical setting to optimize ciclosporin dosing in renal transplant recipients.
Methods: A population pharmacokinetic model of ciclosporin has been developed with NONMEM using full 12-hour pharmacokinetic profiles from 29 renal transplant recipients, 3 months of daily follow-up data from an additional 11 recipients, and both 3 months of follow-up data and full 12-hour pharmacokinetic profiles from nine patients. The internal validation of the model was based on data splitting and jack-knife methods. In addition, the model was validated for its clinical applicability on standard trough and 2-hour post-dose concentration data from 12 additional patients with 3 months of follow-up.
Results: The model that best described the ciclosporin data was a two-compartment model with first-order absorption process with lagged time. The population pharmacokinetic parameters were oral clearance (CL/F) = 26.9 L/h; central volume of distribution after oral administration (V(1)/F) = 24.4 L; absorption rate constant (k(a)) = 0.544 h-1; lag time = 0.460 h; peripheral volume of distribution = 1119 L and intercompartmental clearance after oral administration (Q/F) = 19.6 L/h. Three covariates had significant effect on a total of six pharmacokinetic parameters. These were bodyweight on V(1)/F and k(a), time after transplantation on k(a), and age on CL/F, k(a) and V(1)/F. Cytochrome P450 3A5 genotype was also a significant covariate but was not included in the final model since such information is not available in clinical practice. The external validation showed that the model was able to predict ciclosporin concentrations in the 12 new patients with an average predictive error of 17.4 +/- 14% when the standard sample concentrations from the previous week were given.
Conclusion: A NONMEM pharmacokinetic model for ciclosporin in renal transplant recipients was successfully developed and validated for the first 3 months post-transplantation. The model showed good predictability in a new patient cohort. After further clinical validation, the model may be applicable as a clinical tool for optimizing ciclosporin dosing in renal transplant recipients in the early post-transplant period.