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已发表论文
向中国患者精准使用艾司西酞普兰迈进一步：已发表的群体药代动力学模型的外部评估
Authors Yan K, Xie X, Shao Q, Wang N, Yang M
Received 13 June 2025
Accepted for publication 29 November 2025
Published 11 December 2025 Volume 2025:19 Pages 10937—10951
DOI https://doi.org/10.2147/DDDT.S546904
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Yan Zhu
Keyu Yan,1 Xi Xie,2 Qing Shao,1 Na Wang,1 Miaomiao Yang1
1Department of Pharmacy, Xi’an Mental Health Center, Xi’an, Shaanxi, People’s Republic of China; 2Department of Clinical Laboratory, Hunan Prevention and Treatment Institute for Occupational Diseases, Affiliated Prevention and Treatment Institute for Occupational Diseases of University of South China, Changsha, Hunan, People’s Republic of China
Correspondence: Miaomiao Yang, Department of Pharmacy, Xi’an Mental Health Center, No. 666, Kanghe Road, Chang’an District, Xi’an, Shaanxi, 710100, People’s Republic of China, Email ymm15829288298@sina.com
Objective: This study aims to evaluate the predictive performance of the published population pharmacokinetic (PopPK) model of escitalopram in Chinese patients using an external validation method.
Methods: PubMed, Embase, and Web of Science databases were searched to identify PopPK models. Clinical data collected from Chinese patients treated with escitalopram were used to evaluate these models. The predictive performance of the models was evaluated using both prediction-based diagnostic methods and simulation-based diagnostic methods.
Results: Ten published PopPK models were included in the external validation. A total of 241 serum concentration samples were collected from 193 Chinese patient. Among all evaluated models, the Poweleit 2023 model exhibited the optimal predictive performance, with the PE of − 2.14% and the RMSE of 22.27% at the individual level, and corresponding values of 14.13% and 104.19% at the population level, followed the model by Liu 2022. While the predictive performance of the other models was unsatisfactory.
Conclusion: Published PopPK models of escitalopram showed wide variations in predictive performance among our patient cohort. External models should be accurately evaluated before their application in clinical practice.
Keywords: population pharmacokinetics, external evaluation, therapeutic drug monitoring, nonlinear mixed effects modeling
1Department of Pharmacy, Xi’an Mental Health Center, Xi’an, Shaanxi, People’s Republic of China; 2Department of Clinical Laboratory, Hunan Prevention and Treatment Institute for Occupational Diseases, Affiliated Prevention and Treatment Institute for Occupational Diseases of University of South China, Changsha, Hunan, People’s Republic of China
Correspondence: Miaomiao Yang, Department of Pharmacy, Xi’an Mental Health Center, No. 666, Kanghe Road, Chang’an District, Xi’an, Shaanxi, 710100, People’s Republic of China, Email ymm15829288298@sina.com
Objective: This study aims to evaluate the predictive performance of the published population pharmacokinetic (PopPK) model of escitalopram in Chinese patients using an external validation method.
Methods: PubMed, Embase, and Web of Science databases were searched to identify PopPK models. Clinical data collected from Chinese patients treated with escitalopram were used to evaluate these models. The predictive performance of the models was evaluated using both prediction-based diagnostic methods and simulation-based diagnostic methods.
Results: Ten published PopPK models were included in the external validation. A total of 241 serum concentration samples were collected from 193 Chinese patient. Among all evaluated models, the Poweleit 2023 model exhibited the optimal predictive performance, with the PE of − 2.14% and the RMSE of 22.27% at the individual level, and corresponding values of 14.13% and 104.19% at the population level, followed the model by Liu 2022. While the predictive performance of the other models was unsatisfactory.
Conclusion: Published PopPK models of escitalopram showed wide variations in predictive performance among our patient cohort. External models should be accurately evaluated before their application in clinical practice.
Keywords: population pharmacokinetics, external evaluation, therapeutic drug monitoring, nonlinear mixed effects modeling