Limited sampling models and Bayesian estimation for mycophenolic acid area under the curve prediction in stable renal transplant patients co-medicated with ciclosporin or sirolimus.

BACKGROUND AND OBJECTIVE: Mycophenolate mofetil is a prodrug of mycophenolic acid (MPA), an immunosuppressive agent used in combination with corticosteroids and calcineurin inhibitors or sirolimus for the prevention of acute rejection after solid organ transplantation. Although MPA has a rather narrow therapeutic window and its pharmacokinetics show considerable intra- and interindividual variability, dosing guidelines recommend a standard dosage regimen of 0.5-1.0 g twice daily in adult renal, liver and cardiac transplant recipients. The main objective of the present study was to develop a method to predict the MPA area under the plasma concentration-time curve during one 12-hour dosing interval (AUC(12)) by using multiple linear regression models and maximum a posteriori (MAP) Bayesian estimation methods in patients co-medicated with ciclosporin or sirolimus, aiming to individualize the dosage regimen of mycophenolate mofetil. PATIENTS AND METHODS: Pharmacokinetic profiles of MPA and mycophenolic acid glucuronide (MPAG), the main metabolite of MPA, were obtained from 40 stable adult renal allograft recipients on three different occasions: the day before switching from ciclosporin to sirolimus co-medication (+/-7.4 months post-transplantation; period I) and at 60 days and 270 days after the switch (periods II and III). Blood samples for determination of MPA and MPAG concentrations in plasma were taken at 0 hours (pre-dose) and at 0.33, 0.66, 1.25, 2, 4, 6, 8 and 12 hours after oral intake of mycophenolate mofetil. The MPA AUC(12) was calculated by the trapezoidal method (the observed AUC(12)). Patients were randomly divided into (i) a model-building test group (n = 27); and (ii) a model-validation group (n = 13). Multiple linear regression models were developed, based on three sampling times after drug administration. Subsequently, a population pharmacokinetic model describing MPA and MPAG plasma concentrations was developed using nonlinear mixed-effects modelling and a Bayesian estimator based on the population pharmacokinetic model was used to predict the MPA AUC(12) based on three sampling times taken within 2 hours following dosing. RESULTS: Fifty-two percent of the observed AUC(12) values (three periods) in the 40 patients receiving a fixed dose of mycophenolate mofetil 750 mg twice daily were outside the recommended therapeutic range (30-60 microg x h/mL). The failure of the standard dose to yield an AUC(12) value within the therapeutic range was especially pronounced during the first study period. Of the multiple linear regression models that were tested, the equation based on the 0-hour (pre-dose), 0.66- and 2-hour sampling times showed the best predictive performance in the validation group: r2 = 0.79, relative root mean square error (rRMSE) = 14% and mean relative prediction error (MRPE) = 0.9%. The pharmacokinetics of MPA and MPAG were best described by a two-compartment model with first-order absorption and elimination for MPA, plus a compartment for MPAG, also including a gastrointestinal compartment and enterohepatic cycling in the case of sirolimus co-medication. The ratio of aminotransferase liver enzymes (AST and ALT) and the glomerular filtration rate significantly influenced MPA glucuronidation and MPAG renal excretion, respectively. Bayesian estimation of the MPA AUC(12) based on 0-, 1.25- and 2-hour sampling times predicted the observed AUC(12) values of the patients in the validation group, with the following predictive performance characteristics: r2 = 0.93, rRMSE = 12.4% and MRPE = -0.4%. CONCLUSION: Use of the developed multiple linear regression equation and Bayesian estimator, both based on only three blood sampling times within 2 hours following a dose of mycophenolate mofetil, allowed an accurate prediction of a patient's MPA AUC(12) for therapeutic drug monitoring and dose individualization. These findings should be validated in a randomized prospective trial