Population pharmacokinetic model and Bayesian estimator for two tacrolimus formulations in adult liver transplant patients

Camille Riff, Jean Debord, Caroline Monchaud, Pierre Marquet, Jean-Baptiste Woillard
British Journal of Clinical Pharmacology 2019 April 11

AIMS: Tacrolimus is a narrow therapeutic range drug that requires fine dose adjustment, for which pharmacokinetic models have been amply proposed in renal, but not in liver, transplant recipients. This study aimed to build population pharmacokinetic (POPPK) models and Bayesian estimators (BE) in adult de novo liver transplant patients receiving either the immediate-release (Prograf®, TD) or prolonged-release (Advagraf®, OD) forms to help pharmacokinetics-guided dose individualization.

METHODS: One hundred sixty tacrolimus concentration-time profiles (1654 samples) were collected from 80 patients, at day 7 (D7) and week 6 (W6) post-transplant. Four POPPK models were developed using in parallel parametric (ITSIM) and non-parametric (Pmetrics) approaches for each formulation and period post-transplant. The best limited sampling strategies (LSS) for estimating the area-under-the-curve (AUC) were selected by comparing predicted values to an independent dataset. Finally, the doses required to reach AUC targets were estimated using each BE and compared to the doses obtained using the trapezoidal AUC.

RESULTS: Tacrolimus pharmacokinetics was best described using a one-compartmental model with first-order elimination and two gamma distributions to describe the absorption. In the validation datasets, Bayesian AUC estimates yielded mean bias/RMSE of -5.06%/13.43% (OD D7), 2.25%/8.51% (OD W6), -2.36%/7.27% (TD D7) and 0.87%/9.07% (TD W6) for ITSIM and 8.95%/17.84% (OD D7), -0.11%/10.13% (OD W6), 3.57%/18.40% (TD D7) and 4.48%/12.59% (TD W6) for Pmetrics.

CONCLUSION: The BEs and LSSs proposed here are able to predict accurately and precisely tacrolimus AUC in liver patients using only 3 plasma concentrations. The dosing methods are available on our ISBA website (

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