Xiong Rui, Lei Jing, Wang Lu, Zhang Shipeng, Liu Hengxu, Wang Hongping, Liu Tao, Lai Xiaodan
Department of Pharmacy, Jiangbei Campus of The First Affiliated Hospital of Army Medical University (The 958th Hospital of Chinese People's Liberation Army), Chongqing, China.
Department of Pharmacy, Daping Hospital, Army Medical University, Chongqing, China.
Front Pharmacol. 2024 Aug 13;15:1432759. doi: 10.3389/fphar.2024.1432759. eCollection 2024.
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.
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.
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.
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.
整合药物警戒与网络毒理学方法，探索司美替尼的潜在药物不良事件（ADEs）及其毒性机制，为快速了解新上市药物的安全性和毒理学机制提供参考。
以司美替尼为例，本研究整合基于真实世界数据的药物警戒方法和网络毒理学方法，分析其ADEs及其潜在毒理学机制。首先，从美国食品药品监督管理局（FDA）不良事件报告系统（FAERS）中提取司美替尼的ADE报告，并采用报告比值比（ROR）和英国药品和医疗产品监管局（MHRA）方法检测ADE信号。根据源自《监管活动医学词典》（MedDRA）的首选术语（PTs）和系统器官分类（SOC）对ADE信号进行分类和描述。采用网络毒理学方法分析感兴趣的SOCs的毒理学机制。具体步骤包括使用TOXRIC、STITCH、ChEMBL、CTD、SwissTargetPreditcion和Super-PRED数据库预测司美替尼的潜在靶点，使用GeneCards数据库收集SOC的靶点，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（GO）和京都基因与基因组百科全书（KEGG）分析，并使用AutoDock软件测试分子亲和力。
共提取到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个信号在药品说明书中未提及，包括嵌甲、高磷血症、心脏瓣膜病、血尿、中性粒细胞减少等。对阳性ADE信号所涉及的6个SOCs的毒理学机制分析显示，关键靶点包括表皮生长因子受体（EGFR）、信号转导和转录激活因子3（STAT3）、蛋白激酶B（AKT1）、白细胞介素6（IL6）、B淋巴细胞瘤-2（BCL2）等，关键通路包括磷脂酰肌醇-3激酶/蛋白激酶B（PI3K/Akt）通路、细胞凋亡、表皮生长因子受体（ErbB）信号通路以及EGFR酪氨酸激酶抑制剂耐药等。分子对接实验表明司美替尼与这些通路中的关键靶点存在自发结合。
药物警戒分析确定了司美替尼一些新的潜在ADEs，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、细胞凋亡、ErbB信号通路、EGFR酪氨酸激酶抑制剂耐药等通路有关。
