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External evaluation of population pharmacokinetic models for ciclosporin in adult renal transplant recipients
Author(s) -
Mao JunJun,
Jiao Zheng,
Yun HwiYeol,
Zhao ChenYan,
Chen HanChao,
Qiu XiaoYan,
Zhong MingKang
Publication year - 2018
Publication title -
british journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.216
H-Index - 146
eISSN - 1365-2125
pISSN - 0306-5251
DOI - 10.1111/bcp.13431
Subject(s) - predictability , bayesian probability , population , medicine , observational study , covariate , econometrics , linear model , ciclosporin , statistics , computer science , transplantation , mathematics , environmental health
Aims Several population pharmacokinetic (popPK) models for ciclosporin (CsA) in adult renal transplant recipients have been constructed to optimize 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 predose and 500 2‐h postdose concentrations) from our hospital. Prediction‐based diagnostics and simulation‐based normalized prediction distribution error 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 normalized prediction distribution error 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.