Article date: April 2019
By: Huan‐Xi Zhang, Chang‐Cheng Sheng, Long‐Shan Liu, Bi Luo, Qian Fu, Qun Zhao, Jun Li, Yan‐Feng Liu, Rong‐Hai Deng, Zheng Jiao, Chang‐Xi Wang in Volume 85, Issue 4, pages 746-761
Aims
Various mycophenolate mofetil (MMF) population pharmacokinetic (popPK) models have been developed to describe its PK characteristics and facilitate its optimal dosing in adult kidney transplant recipients co‐administered with tacrolimus. However, the external predictive performance has been unclear. Thus, this study aimed to comprehensively evaluate the external predictability of published MMF popPK models in such populations and investigate the potential influencing factors.
Methods
The external predictability of qualified popPK models was evaluated using an independent dataset. The evaluation included prediction‐ and simulation‐based diagnostics, and Bayesian forecasting. In addition, factors influencing model predictability, especially the impact of structural models, were investigated.
Results
Fifty full PK profiles from 45 patients were included in the evaluation dataset and 11 published popPK models were identified and evaluated. In prediction‐based diagnostics, the prediction error within ±30% was less than 50% in most published models. The prediction‐ and variability‐corrected visual predictive check and posterior predictive check showed large discrepancies between the observations and simulations in most models. Moreover, the normalized prediction distribution errors of all models did not follow a normal distribution. Bayesian forecasting demonstrated an improvement in the model predictability. Furthermore, the predictive performance of two‐compartment (2CMT) models incorporating the enterohepatic circulation (EHC) process was not superior to that of conventional 2CMT models.
Conclusions
The published models showed large variability and unsatisfactory predictive performance, which indicated that therapeutic drug monitoring was necessary for MMF clinical application. Further studies incorporating potential covariates need to be conducted to investigate the key factors influencing model predictability of MMF.
DOI: 10.1111/bcp.13850
View this article