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.
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.
Authors: Wei Zhao; Florentia Kaguelidou; Valérie Biran; Daolun Zhang; Karel Allegaert; Edmund V Capparelli; Nick Holford; Toshimi Kimura; Yoke-Lin Lo; José-Esteban Peris; Alison Thomson; John N van den Anker; May Fakhoury; Evelyne Jacqz-Aigrain Journal: Br J Clin Pharmacol Date: 2013-04 Impact factor: 4.335
Authors: J C Lukas; A M Suárez; M P Valverde; M V Calvo; J M Lanao; R Calvo; E Suarez; A D Gil Journal: J Clin Pharm Ther Date: 2005-12 Impact factor: 2.512
Authors: Jing Zhu; Olivia Campagne; Chad D Torrice; Gabrielle Flynn; Jordan A Miller; Tejendra Patel; Oscar Suzuki; Jonathan R Ptachcinski; Paul M Armistead; Tim Wiltshire; Donald E Mager; Daniel L Weiner; Daniel J Crona Journal: Clin Transl Sci Date: 2021-01-27 Impact factor: 4.689