Population pharmacokinetic model and Bayesian estimator for two tacrolimus formulations--twice daily Prograf and once daily Advagraf

Abstract

Aim: To investigate the differences in the pharmacokinetics of Prograf and the prolonged release formulation Advagraf and to develop a Bayesian estimator to estimate tacrolimus inter-dose area under the curve (AUC) in renal transplant patients receiving either Prograf or Advagraf.

Methods: Tacrolimus concentration-time profiles were collected, in adult renal transplant recipients, at weeks 1 and 2, and at months 1, 3 and 6 post-transplantation from 32 Prograf treated patients, and one profile was collected from 41 Advagraf patients more than 12 months post-transplantation. Population pharmacokinetic (popPK) parameters were estimated using nonmem. In a second step, the popPK model was used to develop a single Bayesian estimator for the two tacrolimus formulations.

Results: A two-compartment model with Erlang absorption (n= 3) and first-order elimination best described the data. In Advagraf patients, a bimodal distribution was observed for the absorption rate constant (K(tr) ): one group with a K(tr) similar to that of Prograf treated patients and the other group with a slower absorption. A mixture model for K(tr) was tested to describe this bimodal distribution. However, the data were best described by the nonmixture model including covariates (cytochrome P450 3A5, haematocrit and drug formulation). Using this model and tacrolimus concentrations measured at 0, 1 and 3h post-dose, the Bayesian estimator could estimate tacrolimus AUC accurately (bias = 0.1%) and with good precision (8.6%).

Conclusions: The single Bayesian estimator developed yields good predictive performance for estimation of individual tacrolimus inter-dose AUC in Prograf and Advagraf treated patients and is suitable for clinical practice.

Figure 1

Probability density function of K_tr according to tacrolimus formulation, estimated using the covariate-free model. Advagraf (); Prograf ()

Figure 2

Scatter plots of (A) population model-predicted concentrations (PRED) and (B) individual model-predicted concentrations (IPRED) vs. observed concentrations (DV), and (C) weighted residuals (WRES) vs. time for the mixture model

Figure 3

Scatter plots of (A) population model-predicted concentrations (PRED) and (B) individual model-predicted concentrations (IPRED) vs. observed concentrations (DV), and (C) weighted residuals (WRES) vs. time for the nonmixture model

Figure 4

Evaluation of the final model using a visual predictive check. Shown are comparisons between the observed data (circles) for tacrolimus (TAC) concentrations and the 5th (bottom dashed line), 50th (solid line) and 95th (top dashed line) percentiles obtained from 1000 simulations for the global population standardized to a 4.25 mg dose (A), and as a function of CYP3A5 status, standardized to a 4.13 mg dose for non-expressers (B) and to a 6.88 mg dose for expressers (C)