A compartmental pharmacokinetic model of cyclosporin and its predictive performance after Bayesian estimation in kidney and simultaneous pancreas-kidney transplant recipients.

Background: Therapeutic drug monitoring of cyclosporin A (CsA) is an obvious necessity because of its unpredictable absorption and narrow therapeutic window. The use of limited sampling models (LSMs) has improved the estimation of the systemic exposure [area under curve (AUC)] compared with C(0h) monitoring, but these equations are rigid and not reliable in patients with an abnormal absorption profile. We developed and validated a limited sampling (t=0, 2 and 3 h) strategy, based on a compartmental population pharmacokinetic (PK) model for CsA after kidney transplantation alone (KTA) and simultaneous pancreas-kidney transplant (SPKT) recipients, a group of patients with unpredictable absorption kinetics.

Methods: A two-compartment model with lag time and first-order absorption was calculated using a PK software package from data of 20 KTA and SPKT recipients and validated prospectively in 20 KTA and 20 SPKT recipients. Calculated population PK parameters were individualized for each of the remaining 40 patients based on their CsA dosing and on one or a combination of measured CsA blood concentrations using the Bayesian fitting method. AUCs were calculated from individualized PK parameters. AUCs were also calculated using previously published LSMs. Relationships between AUCs calculated by the models and the 'golden standard' AUC (trapezoidal rule) were investigated by Pearson correlation test.

Results And Conclusions: A population two-compartment model is presented to reliably estimate the CsA AUC in KTA and SPKT recipients. The performance of the model to estimate the AUC is comparable to the performance of two published LSMs in KTA patients, but markedly better in SPKT patients. Combined with Bayesian fitting, the model is very flexible since sampling times are not rigid and can be varied as long as dosing and sampling times are recorded accurately. The model has already proven to be clinically useful and is currently used to further investigate CsA in an integrated pharmacokinetic/pharmacodynamic model.