Population pharmacokinetics and Bayesian estimator of mycophenolic acid in children with idiopathic nephrotic syndrome
Article first published online: 8 JAN 2010
© 2010 The Authors. Journal compilation © 2010 The British Pharmacological Society
British Journal of Clinical Pharmacology
Volume 69, Issue 4, pages 358–366, April 2010
How to Cite
Zhao, W., Elie, V., Baudouin, V., Bensman, A., André, J. L., Brochard, K., Broux, F., Cailliez, M., Loirat, C. and Jacqz-Aigrain, E. (2010), Population pharmacokinetics and Bayesian estimator of mycophenolic acid in children with idiopathic nephrotic syndrome. British Journal of Clinical Pharmacology, 69: 358–366. doi: 10.1111/j.1365-2125.2010.03615.x
- Issue published online: 11 MAR 2010
- Article first published online: 8 JAN 2010
- Received 29 September 2009Accepted6 December 2009
- Bayesian estimation;
- nephrotic syndrome;
WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT
• MMF has been proposed as a treatment of steroid-dependent nephrotic syndrome and in the recent years, several studies have suggested its positive effect in preventing relapses.
WHAT THIS PAPER ADDS
• The population pharmacokinetics of MPA was first evaluated in children with idiopathic nephrotic syndrome and data fitted well with a two-compartment model with first-order absorption and lag time.
• Body weight and serum albumin had a significant impact on oral clearance.
• A three-point (T0, T1 and T4h) Bayesian estimator of AUC0–12 was developed.
AIMS To develop a population pharmacokinetic model for mycophenolic acid (MPA) in children with idiopathic nephrotic syndrome (INS) treated with mycophenolate mofetil (MMF), identify covariates that explain variability and determine the Bayesian estimator of the area under the concentration–time curve over 12 h (AUC0–12).
METHODS The pharmacokinetic model of MMF was described from 23 patients aged 7.4 ± 3.9 years (range 2.9–14.9) using nonlinear mixed-effects modelling (NONMEM) software. A two-compartment model with lag–time and first-order absorption and elimination was developed. The final model was validated using visual predictive check. Bayesian estimator was validated using circular permutation method.
RESULTS The population pharmacokinetic parameters were apparent oral clearance 9.7 l h−1, apparent central volume of distribution 22.3 l, apparent peripheral volume of distribution 250 l, inter-compartment clearance 18.8 l h−1, absorption rate constant 5.16 h−1, lag time 0.215 h. The covariate analysis identified body weight and serum albumin as individual factors influencing the apparent oral clearance. Accurate Bayesian estimation of AUC0–12 was obtained using the combination of three MPA concentrations measured just before (T0), 1 and 4 h (T1 and T4) after drug intake with a small error of 0.298 µg h−1 ml−1 between estimated and reference AUC0–12.
CONCLUSIONS The population pharmacokinetic model of MPA was developed in children with INS. A three-point (T0, T1 and T4h) Bayesian estimator of AUC0–12 was developed and might be used to investigate the relation between MPA pharmacokinetic and pharmacodynamics in children with INS and determine if there is any indication to monitor MPA exposure in order to improve patient outcome based on individual AUC-controlled MMF dosing.