Population pharmacokinetics and Bayesian estimation of cyclosporine in a Tunisian population of hematopoietic stem cell transplant recipient
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- Eljebari, H., Gaies, E., Fradj, N.B. et al. Eur J Clin Pharmacol (2012) 68: 1517. doi:10.1007/s00228-012-1275-9
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Therapeutic drug monitoring of cyclosporine minimizes the risk of toxicity and acute rejection after transplantation. Areas under the curve (AUCs) rather than trough concentration-based monitoring are recommended. Population pharmacokinetics (PopPK) modeling and Bayesian estimation seem to be the best way to predict cyclosporine disposition and dose requirements to achieve the therapeutic target in an individual patient because of the possibility of predicting cyclosporine AUC using only a few blood samples. Our objectives were to build a PopPk model for cyclosporine in a Tunisian population of HSCT patients and to develop a Bayesian method for the estimation of individual cyclosporine AUC.
Patients and methods
The PopPk of cyclosporine was studied using nonlinear mixed effects modeling (NONMEM) in 30 patients (index group) receiving cyclosporine on a twice-daily basis. Ten blood samples were collected after steady-state morning cyclosporine dose. Bayesian estimation of individual AUC was made on the basis of three blood concentration measurements in an independent group of 30 patients (test group).
A two-compartment model with first-order absorption and a lag time provided the best fitting. The population mean estimate and interindividual variability from the final model for CL, Ka, Tlag, V1, V2, and Q were 25.4 L/h (CV = 38.72 %), 0.214 h−1(CV = 28.5 %), 0.382 h, 10.9 L (85.73 %), 496 L, and 5 L/h, respectively. Covariates had no discernible effects on cyclosporine pharmacokinetics in our population. Bayesian estimation provided an accurate estimation of AUC, although a bias was observed leading to slight underprediction of AUC (bias −1.03 %). A very satisfactory precision was observed (RMSE 12.07 %).
We report a PopPK model for cyclosporine in Tunisian HSCT patients. Bayesian estimation using only three concentrations provides good prediction of cyclosporine exposure. These tools allow us to routinely estimate cyclosporine AUC in a clinical setting.