Franc Andreu (1), Helena Colom (1), Núria Lloberas (2), Ana Caldés (2), Joan Torras (2), Josep Maria Grinyó (2)
(1) Department of Pharmacokinetics, Faculty of Pharmacy, University of Barcelona. (2) Nephrology service, Hospital de Bellvitge, Barcelona.
Objectives: The aims of this study were (1) to develop a population pharmacokinetic (PPK) model for tacrolimus (TAC) in renal transplant recipients, (2) to identify demographic, biochemical and pharmacogenetic determinants of TAC exposure; and (3) to establish a Limited Sampling Strategy (LSS) to predict the area under the concentration-time curve (AUC) from 0 to 12 hours.
Methods: 16 patients received oral doses of TAC (1-4mg/day) together with mycophenolate mofetil (2g/day). The demographic, biochemical and genotyping for ABCB1 protein (C3435T and G2677T) were recorded. Full pharmacokinetic profiles from 5 occasions (1 week and 1, 3, 6 and 12 months post-transplant) were simultaneously analyzed with NONMEM ver. 7.2 using Perl-Speaks-NONMEM (PsN) and R code version 2.15.2. The final model predictive performance was evaluated with a validation group according to the method proposed by Sheiner and Beal. A LSS was established by the Bayesian estimation method.
Results: TAC PK was best described by a two-compartment model combined with a 3 transit-compartment absorption model, parameterized in terms of clearance (CL), central and peripheral volume (Vc, Vp), intercompartmental clearance (CLD), absorption constant (Ka) and mean transit time (MT). The FOCE interaction estimation method was used. Between-patient variability (BPV) was associated with CL (39%), Ka (35%) and MT (32%). Between-occasion variability was associated with CL (29%). Residual error consisted of a proportional error of (20%). None of the covariates tested were statistically significant (p<0.05) excepting total bilirubin on CL. However, BPV reduction was <10% and it was removed from the final model. The final PK parameters estimates were 16.5L/h (CL), 9.89L (Vc), 526L (Vp), 35.56L/h (CLD), 0.47h-1 (Ka) and 0.89h (MT). External validation provided a media prediction error (bias) =0.37 µg/L and median root squared error (precision)=0.38 µg/L. A LSS of two sampling times, at 0h [pre-dose] and 1.5 hour post-dose, allowed accurate and precise prediction of the AUC0-12h with a non significant median AUC percentage bias of 4% and good precision (median absolute percentage prediction error=7.8%).
Conclusions: A PPK model for TAC in renal transplant recipients was successfully modeled with a 3 transit compartment absorption model. External validation confirmed its predictive ability. It allowed a Bayesian estimator development suitable for clinical practice being able to accurately predict TAC AUC0-12h
Reference: PAGE 22 (2013) Abstr 2745 [www.page-meeting.org/?abstract=2745]
Poster: Other Drug/Disease Modelling