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

TL;DR

This study systematically evaluated the external predictive performance of 16 published population pharmacokinetic models for tacrolimus in adult liver transplant recipients, revealing limitations and potential improvements through Bayesian forecasting and nonlinear modeling.

Contribution

It provides a comprehensive external validation of existing tacrolimus popPK models and highlights the benefits of Bayesian forecasting and nonlinear kinetics incorporation.

Findings

All models had prediction errors within 30% in less than half of cases.

Simulation diagnostics showed large discrepancies in most models.

Bayesian forecasting improved model predictability with prior observations.

Abstract

Background:Diverse tacrolimus population pharmacokinetic 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. Methods:The 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.Results:Sixteen 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 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 compartment models,indicating the underlying nonlinear kinetics of tacrolimus in liver transplant recipients.Conclusions:The 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.