  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.
