5 years ago

External Evaluation of Population Pharmacokinetic Models for Cyclosporine in Adult Renal Transplant Recipients

Han-Chao Chen, Chen-Yan Zhao, Hwi-Yeol Yun, Jun-Jun Mao, Ming-Kang Zhong, Xiao-Yan Qiu, Zheng Jiao
Aim Several population pharmacokinetic (popPK) models for cyclosporine (CsA) in adult renal transplant recipients have been constructed to optimise the therapeutic regimen of CsA. However, little is known about their predictabilities when extrapolated to different clinical centres. Therefore, this study aimed to externally evaluate the predictive ability of CsA popPK models and determine the potential influencing factors. Methods A literature search was conducted and the predictive performance was determined for each selected model using an independent data set of 62 patients (471 pre-dose and 500 2-hour post-dose concentrations) from our hospital. Prediction-based diagnostics and simulation-based normalised prediction distribution error (NPDE) were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting. Additionally, potential factors influencing model predictability were investigated. Results Seventeen models extracted from 17 published popPK studies were assessed. Prediction-based diagnostics showed that ethnicity potentially influenced model transferability. Simulation-based NPDE analyses indicated misspecification in most of the models, especially regarding variance. Bayesian forecasting demonstrated that the predictive performance of the models substantially improved with 2 - 3 prior observations. The predictability of nonlinear Michaelis-Menten models was superior to that of linear compartmental models when evaluating the impact of structural models, indicating the underlying nonlinear kinetics of CsA. Structural model, ethnicity, covariates, and prior observations potentially affected model predictability. Conclusions Structural model is the predominant factor influencing model predictability. Incorporation of nonlinear kinetics in CsA popPK modelling should be considered. Moreover, Bayesian forecasting substantially improved model predictability.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1111/bcp.13431

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