Talha Zahid Muhammad, Zamir Ammara, Majeed Abdul, Imran Imran, Alsanea Sary, Ahmad Tanveer, Alqahtani Faleh, Fawad Rasool Muhammad
Department of Pharmacy Practice, Faculty of Pharmacy, Bahauddin Zakariya University, 60800, Multan, Pakistan.
Department of Pharmaceutics, Faculty of Pharmacy, Bahauddin Zakariya University, 60800, Multan, Pakistan.
Saudi Pharm J. 2023 Aug;31(8):101675. doi: 10.1016/j.jsps.2023.06.008. Epub 2023 Jun 19.
The physiologically based pharmacokinetic modeling (PBPK) approach can predict drug pharmacokinetics (PK) by combining changes in blood flow and pathophysiological alterations for developing drug-disease models. Cefepime hydrochloride is a parenteral cephalosporin that is used to treat pneumonia, sepsis, and febrile neutropenia, among other things. The current study sought to identify the factors that impact cefepime pharmacokinetics (PK) following dosing in healthy, diseased (CKD and obese), and pediatric populations. For model construction and simulation, the modeling tool PK-SIM was utilized. Estimating cefepime PK following intravenous (IV) application in healthy subjects served as the primary step in the model-building procedure. The prediction of cefepime PK in chronic kidney disease (CKD) and obese populations were performed after the integration of the relevant pathophysiological changes. Visual predictive checks and a comparison of the observed and predicted values of the PK parameters were used to verify the developed model. The results of the PK parameters were consistent with the reported clinical data in healthy subjects. The developed PBPK model successfully predicted cefepime PK as observed from the ratio of the observed and predicted PK parameters as they were within a two-fold error range.
基于生理的药代动力学建模(PBPK)方法可以通过结合血流变化和病理生理改变来预测药物药代动力学(PK),从而建立药物-疾病模型。盐酸头孢吡肟是一种胃肠外使用的头孢菌素,用于治疗肺炎、败血症和发热性中性粒细胞减少症等疾病。本研究旨在确定在健康、患病(慢性肾脏病和肥胖)及儿科人群中给药后影响头孢吡肟药代动力学(PK)的因素。对于模型构建和模拟,使用了建模工具PK-SIM。估计健康受试者静脉注射(IV)后头孢吡肟的PK是模型构建过程的第一步。在整合相关病理生理变化后,对慢性肾脏病(CKD)和肥胖人群中头孢吡肟的PK进行了预测。使用可视化预测检查以及PK参数的观察值与预测值的比较来验证所建立的模型。PK参数的结果与健康受试者中报告的临床数据一致。所建立的PBPK模型成功地预测了头孢吡肟的PK,观察到的PK参数与预测的PK参数之比在两倍误差范围内。