■整合药物警戒和网络毒理学方法,以探索司美替尼的潜在不良药物事件(ADE)和毒性机制,为快速了解新上市药品的安全性和毒理机制提供参考。
■以司美替尼为例,本研究整合了基于现实数据的药物警戒方法和网络毒理学方法,分析了其ADE及其潜在的毒理学机制。首先,司美替尼的ADE报告摘自美国食品和药物管理局(FDA)不良事件报告系统(FAERS),ADE信号通过报告比值比(ROR)和英国药品和医疗保健产品监管机构(MHRA)方法检测。根据源自调节活动医学词典(MedDRA)的优选术语(PT)和系统器官类别(SOC)对ADE信号进行分类和描述。网络毒理学方法用于分析感兴趣的SOCs的毒理学机理。具体步骤包括使用TOXRIC预测司美替尼的潜在靶标,STITCH,ChEMBL,CTD,SwissTargetPreditcion,和Super-PRED数据库,使用GeneCards数据库收集SOC的目标,通过STRING数据库进行蛋白质-蛋白质相互作用(PPI)分析,通过DAVID数据库进行基因本体论(GO)和京都基因和基因组百科全书(KEGG)分析,并使用AutoDock软件测试分子亲和力。
■共提取了1388份与司美替尼相关的ADE报告,通过ROR和MHRA方法检测到53个阳性信号,其中20个信号未在包装说明书中提及,包括钉子在内,高磷酸盐血症,心脏瓣膜疾病,血尿,中性粒细胞减少症,等。对6种SOCs参与ADE阳性信号的毒理机制分析显示,关键靶点包括EGFR,STAT3、AKT1、IL6、BCL2等。,关键通路包括PI3K/Akt通路,凋亡,ErbB信号通路,和EGFR酪氨酸激酶抑制剂耐药,等。分子对接试验显示司美替尼与这些途径中的关键靶标自发结合。
■药物警戒分析确定了一些新的潜在的司美替尼的ADE,网络毒理学分析表明,司美替尼的毒性作用可能与PI3K/Akt通路有关,凋亡,ErbB信号通路,EGFR酪氨酸激酶抑制剂耐药等通路。
UNASSIGNED: To integrate pharmacovigilance and network toxicology methods to explore the potential adverse drug events (ADEs) and toxic mechanisms of
selumetinib, and to provide a reference for quickly understanding the safety and toxicological mechanisms of newly marketed drugs.
UNASSIGNED: Taking
selumetinib as an example, this study integrated pharmacovigilance methods based on real-world data and network toxicology methods to analyze its ADE and its potential toxicological mechanism. First, the ADE reports of selumetinib were extracted from the US Food and Drug Administration (FDA) adverse event reporting system (FAERS), and the ADE signals were detected by reporting odds ratio (ROR) and UK medicines and healthcare products regulatory agency (MHRA) methods. The ADE signals were classified and described according to the preferred terms (PTs) and system organ class (SOC) derived from the Medical Dictionary for Regulatory Activities (MedDRA). The network toxicology method was used to analyze the toxicological mechanism of the interested SOCs. The specific steps included predicting the potential targets of
selumetinib using TOXRIC, STITCH, ChEMBL, CTD, SwissTargetPreditcion, and Super-PRED databases, collecting the targets of SOC using GeneCards database, conducting protein-protein interaction (PPI) analysis through STRING database, conducting gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis through DAVID database, and testing the molecular affinity using AutoDock software.
UNASSIGNED: A total of 1388 ADE reports related to selumetinib were extracted, and 53 positive signals were detected by ROR and MHRA methods, of which 20 signals were not mentioned in the package insert, including ingrowing nail, hyperphosphatemia, cardiac valve disease, hematuria, neutropenia, etc. Analysis of the toxicological mechanism of six SOCs involved in positive ADE signals revealed that the key targets included EGFR, STAT3, AKT1, IL6, BCL2, etc., and the key pathways included PI3K/Akt pathway, apoptosis, ErbB signaling pathway, and EGFR tyrosine kinase inhibitor resistance, etc. Molecular docking assays showed spontaneous binding of
selumetinib to key targets in these pathways.
UNASSIGNED: The pharmacovigilance analysis identified some new potential ADEs of selumetinib, and the network toxicology analysis showed that the toxic effects of
selumetinib may be related to PI3K/Akt pathway, apoptosis, ErbB signaling pathway, EGFR tyrosine kinase inhibitor resistance and other pathways.