Tacrolimus population pharmacokinetics in adult heart transplant patients

IntroductionTacrolimus is an immunosuppressant largely used in heart transplantation. However, the calculation of its exposure based on the area under the curve (AUC) requires the use of a population pharmacokinetic (PK) model. The aims of this work were (i) to develop a population PK model for tacrolimus in heart transplant patients, (ii) to derive a maximum a posteriori Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) and (iii) to estimate probabilities of target attainment (PTAs) for AUC and trough concentration (C0). Material and MethodsForty-seven PK profiles (546 concentrations) of 18 heart transplant patients of the Pharmacocinetique des Immunosuppresseurs chez les patients GREffes Cardiaques study receiving tacrolimus (Prograf & REG;) were included. The database was split into a development (80%) and a validation (20%) set. PK parameters were estimated in MONOLIX & REG; and based on this model a Bayesian estimator using an LSS was built. Simulations were performed to calculate the PTA for AUC and C0. ResultsThe best model to describe the tacrolimus PK was a two-compartment model with a transit absorption and a linear elimination. Only the CYP3A5 covariate was kept in the final model. The derived MAP-BE based on the LSS (0-1-2 h postdose) yielded an AUC bias & PLUSMN; SD = 2.7 & PLUSMN; 10.2% and an imprecision of 9.9% in comparison to the reference AUC calculated using the trapezoidal rule. PTAs based on AUC or C0 allowed new recommendations to be proposed for starting doses (0.11 mg & BULL;kg(-1)& BULL;12 h(-1) for the CYP3A5 nonexpressor and 0.22 mg & BULL;kg(1)& BULL;12 h(-1) for the CYP3A5 expressor). ConclusionThe MAP-BE developed should facilitate estimation of tacrolimus AUC in heart transplant patients.

Automated summary

This study developed a population pharmacokinetic (PK) model for tacrolimus in heart transplant patients and built a maximum a posteriori Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) to estimate the area under the curve (AUC) and trough concentration (C0). The results showed that the MAP-BE based on LSS had a small bias and imprecision compared to the reference AUC calculated using the trapezoidal rule. The probabilities of target attainment (PTAs) based on AUC or C0 allowed for new recommendations for starting doses in CYP3A5 nonexpressors and expressors.