Systematic external evaluation of published population pharmacokinetic models for tacrolimus in adult liver transplant recipients

Abstract

BackgroundDiverse tacrolimus population pharmacokinetic (popPK) models in adult liver transplant recipients have been established to describe the PK characteristics of tacrolimus in the last two decades. However, their extrapolated predictive performance remains unclear. Therefore, in this study, we aimed to evaluate their external predictability and identify their potential influencing factors. MethodsThe external predictability of each selected popPK model was evaluated using an independent dataset of 84 patients with 572 trough concentrations prospectively collected from Huashan Hospital. Prediction- and simulation-based diagnostics and Bayesian forecasting were conducted to evaluate model predictability. Furthermore, the effect of model structure on the predictive performance was investigated. ResultsSixteen published popPK models were assessed. In prediction-based diagnostics, the prediction error within ± 30% was below 50% in all the published models. The simulation-based normalised prediction distribution error test and prediction- and variability-corrected visual predictive check indicated large discrepancies between the observations and simulations in most of the models. Bayesian forecasting showed improvement in model predictability with two to three prior observations. Additionally, the predictive performance of the nonlinear Michaelis–Menten model was superior to that of linear one- and two-compartment models with first-order elimination, indicating the underlying nonlinear kinetics of tacrolimus in liver transplant recipients, which was consistent with the findings in adult kidney transplant recipients. ConclusionsThe published models performed inadequately in prediction- and simulation-based diagnostics. Bayesian forecasting may improve the predictive performance of the models. Furthermore, nonlinear kinetics of tacrolimus may be mainly caused by the properties of the drug itself, and incorporating nonlinear kinetics may be considered to improve model predictability.