{"title":"External Validation of Population Pharmacokinetic Models for Unbound Cefazolin in Patients Receiving Prophylactic Dosing.","authors":"Toshiaki Komatsu, Yuka Kawai, Yoko Takayama, Yuto Akamada, Mayuko Miyagawa, Masaomi Ikeda, Hideyasu Tsumura, Daisuke Ishii, Kazumasa Matsumoto, Masatsugu Iwamura, Hirotsugu Okamoto, Hideaki Hanaki, Katsuya Otori","doi":"10.1248/bpb.b25-00027","DOIUrl":null,"url":null,"abstract":"<p><p>This study aimed to evaluate published population pharmacokinetic models of unbound cefazolin to assess their predictive performance using an independent dataset. A systematic literature search was conducted on PubMed to identify studies evaluating the population pharmacokinetics of unbound cefazolin in patients. Subsequently, the selected models were used for external validation. Predictive bias was visually assessed by plotting the prediction errors (PEs) and relative PEs. Predictive precision was evaluated by calculating the mean absolute error (MAE), root mean square error (RMSE), and mean relative error (MRE). The predictive performance of the 4 unbound population pharmacokinetic models was evaluated using clinical data from 64 patients and 218 unbound concentration samples. The PEs for unbound cefazolin concentrations in the Komatsu model indicated a positive bias, while the RPEs demonstrated similar predictive distributions along the y = 0 line, regardless of the predicted values. In contrast, the other 3 models showed a negative bias for both PE and RPE at unbound cefazolin concentrations. The best MAE, RMSE, and MRE (%) values were 4.71, 9.02, and 30.2 in Komatsu et al.'s model, while the next best values were 11.5, 16.1, and 107.2 in Chung et al.'s model. Both models, which performed best regarding bias and accuracy, were also utilized in studies on unbound concentrations and the correlation between total concentrations and protein-binding sites. This study identified these models as the most suitable for predicting unbound cefazolin concentration profiles in surgical patients.</p>","PeriodicalId":8955,"journal":{"name":"Biological & pharmaceutical bulletin","volume":"48 5","pages":"650-656"},"PeriodicalIF":1.7000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biological & pharmaceutical bulletin","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1248/bpb.b25-00027","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
This study aimed to evaluate published population pharmacokinetic models of unbound cefazolin to assess their predictive performance using an independent dataset. A systematic literature search was conducted on PubMed to identify studies evaluating the population pharmacokinetics of unbound cefazolin in patients. Subsequently, the selected models were used for external validation. Predictive bias was visually assessed by plotting the prediction errors (PEs) and relative PEs. Predictive precision was evaluated by calculating the mean absolute error (MAE), root mean square error (RMSE), and mean relative error (MRE). The predictive performance of the 4 unbound population pharmacokinetic models was evaluated using clinical data from 64 patients and 218 unbound concentration samples. The PEs for unbound cefazolin concentrations in the Komatsu model indicated a positive bias, while the RPEs demonstrated similar predictive distributions along the y = 0 line, regardless of the predicted values. In contrast, the other 3 models showed a negative bias for both PE and RPE at unbound cefazolin concentrations. The best MAE, RMSE, and MRE (%) values were 4.71, 9.02, and 30.2 in Komatsu et al.'s model, while the next best values were 11.5, 16.1, and 107.2 in Chung et al.'s model. Both models, which performed best regarding bias and accuracy, were also utilized in studies on unbound concentrations and the correlation between total concentrations and protein-binding sites. This study identified these models as the most suitable for predicting unbound cefazolin concentration profiles in surgical patients.
期刊介绍:
Biological and Pharmaceutical Bulletin (Biol. Pharm. Bull.) began publication in 1978 as the Journal of Pharmacobio-Dynamics. It covers various biological topics in the pharmaceutical and health sciences. A fourth Society journal, the Journal of Health Science, was merged with Biol. Pharm. Bull. in 2012.
The main aim of the Society’s journals is to advance the pharmaceutical sciences with research reports, information exchange, and high-quality discussion. The average review time for articles submitted to the journals is around one month for first decision. The complete texts of all of the Society’s journals can be freely accessed through J-STAGE. The Society’s editorial committee hopes that the content of its journals will be useful to your research, and also invites you to submit your own work to the journals.