利托那韦生理药代动力学模型的建立及其介导的药物相互作用预测
Establishment of physiological based pharmacokinetic model of ritonavir and prediction of its drug-drug interactions
孙泽旭 1赵楠 2解染 2刘昭前1
作者信息
- 1. 中南大学湘雅药学院,湖南长沙 410013
- 2. 北京大学第一医院药学部,北京 100034
- 折叠
摘要
目的 建立利托那韦生理药代动力学(PBPK)模型,模拟利托那韦介导的药物相互作用,对利托那韦的药代动力学(PK)特征及药物相互作用(DDI)进行前瞻性预测.方法 通过检索相关数据库,收集利托那韦的理化性质、PK、DDI相关参数和临床研究数据,用PK-Sim软件构建利托那韦的PBPK模型,并对模型进行参数优化和验证,评价构建的PBPK模型在描述利托那韦的PK特征和预测DDI的性能.结果 利托那韦PBPK模型展现出了良好的性能,利托那韦AUC和Cmax的模拟值与实际观测值几何平均折叠误差(GMFE)分别为1.11和1.16,与利托那韦联用后受变药的AUC和Cmax的GMFE分别为1.24和1.26.结论 本研究成功构建了利托那韦的PBPK模型,利托那韦对代谢酶细胞色素P450 3A4影响显著,利托那韦与相关受变药联用时,可根据PBPK模型指导个体化用药.
Abstract
Objective To develop a physiological pharmacokinetic(PBPK)model of ritonavir,simulate ritonavir-mediated drug interactions,and provide future predictions for ritonavir pharmacokinetics(PK)and drug-drug interaction(DDI).Methods The PBPK model of ritonavir was constructed by searching relevant databases,collecting data on the physicochemical properties,PK,DDI related parameters and clinical studies of ritonavir,and using PK-Sim software to optimize and validate the parameters of the model to evaluate the performance of the constructed PBPK model in describing the PK characteristics and predicting the DDI of ritonavir.Results The ritonavir PBPK model demonstrated good performance,and by calculating the geometric mean folded error(GMFE)between the Sim and actual Obs values,the GMFEs of ritonavir AUC and Cmax were 1.11 and 1.16,respectively,and the GMFEs of ritonavir AUC and Cmax after combination with ritonavir were 1.24 and 1.26,respectively.Conclusion The PBPK model of ritonavir has been successfully constructed,and the effect of ritonavir on the metabolic enzyme cytochrome P450 3A4 is significant.Therefore,when ritonavir is combined with the victim drugs,individualized dosing can be guided according to the PBPK model.
关键词
利托那韦/生理药代动力学模型/药代动力学/药物相互作用Key words
ritonavir/physiologically based pharmacokinetic/pharmacokinetics/drug-drug interaction引用本文复制引用
基金项目
国家自然科学基金(82173901)
湖南省科技人才托举工程基金(2023TJ-G03)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据