External Validation of Population Pharmacokinetic Models for Unbound Cefazolin in Patients Receiving Prophylactic Dosing.

IF 1.7 4区 医学 Q3 PHARMACOLOGY & PHARMACY
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
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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.

接受预防性给药的非结合头孢唑林人群药代动力学模型的外部验证。
本研究旨在评估已发表的非结合头孢唑林群体药代动力学模型,以评估其使用独立数据集的预测性能。在PubMed上进行了系统的文献检索,以确定评估非结合头孢唑林在患者体内的群体药代动力学的研究。随后,将选取的模型进行外部验证。通过绘制预测误差(PEs)和相对PEs来直观评估预测偏差。通过计算平均绝对误差(MAE)、均方根误差(RMSE)和平均相对误差(MRE)来评估预测精度。使用64名患者和218份非结合浓度样本的临床数据,对4种非结合人群药代动力学模型的预测性能进行了评估。在Komatsu模型中,未结合头孢唑啉浓度的pe呈正偏倚,而rpe沿y = 0线呈现相似的预测分布,无论预测值如何。相比之下,其他3个模型在未结合的头孢唑林浓度下对PE和RPE均显示负偏倚。在Komatsu等人的模型中,MAE、RMSE和MRE(%)的最佳值分别为4.71、9.02和30.2,在Chung等人的模型中,次之的最佳值分别为11.5、16.1和107.2。这两种模型在偏倚和准确性方面表现最好,也被用于研究非结合浓度和总浓度与蛋白质结合位点之间的相关性。本研究确定这些模型最适合预测手术患者的非结合头孢唑林浓度谱。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.50
自引率
5.00%
发文量
247
审稿时长
2 months
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.
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