Physiologically based pharmacokinetic modeling (PBPK) to predict drug-drug interactions for encorafenib. Part II. Prospective predictions in hepatic and renal impaired populations with clinical inhibitors and inducers

基于生理学的药代动力学模型 药理学 CYP3A4型 药代动力学 人口 药品 药物代谢 药店 医学 化学 内科学 新陈代谢 细胞色素P450 环境卫生 家庭医学
作者
Sivacharan Kollipara,Tausif Ahmed,Praveen Sivadasu
出处
期刊:Xenobiotica [Informa]
卷期号:53 (5): 339-356 被引量:6
标识
DOI:10.1080/00498254.2023.2246153
摘要

AbstractEncorafenib, a potent BRAF kinase inhibitor gets significantly metabolised by CYP3A4 (83%) and CYP2C19 (16%) and is a substrate for P-glycoprotein (P-gp). Due to significant metabolism by CYP3A4, encorafenib exposure can increase in hepatic and renal impairment and may lead to altered magnitude of drug-drug interactions (DDI). Hence, it is necessary to assess the exposures & DDI's in impaired population.Physiologically based pharmacokinetic modelling (PBPK) was utilised to determine the exposures of encorafenib in hepatic and renal impairment along with altered DDI's. Prospective DDI's were predicted with USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors and CYP3A4 inducers.PBPK models for encorafenib, perpetrators simulated PK parameters within 2-folds error. Encorafenib exposures significantly increased in hepatic as compared to renal impairment because of reduced CYP3A4 levels.Hepatic impairment caused changes in inhibition and induction DDI's, when compared to healthy population. Renal impairment did not cause significant changes in DDIs except for itraconazole. P-gp, CYP2C19 inhibitors did not result in altered DDI's. The DDI's were found to have insignificant correlation with relative exposure increase of perpetrators in case of impairment. Overall, this work signifies use of PBPK modelling for DDI's evaluations in hepatic and renal impairment populations.Keywords: Encorafenibdrug-drug interactionPBPK modellinghepatic impairmentrenal impairmentinhibitioninhibition AcknowledgmentsThe authors would like to thank Dr. Reddy's laboratories for providing access to Gastroplus software to perform this work. The authors would also like to thank Gautam Vijaywargi and Rajkumar Boddu for their support.Disclosure statementThe authors report that there are no competing interests to declare.Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.
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