药理学
化学
对接(动物)
肺癌
可药性
药代动力学
分子模型
天然产物
计算生物学
儿茶素
体内
药物数据库
分子动力学
公共化学
生物化学
数量结构-活动关系
医学
表皮生长因子受体
自动停靠
虚拟筛选
作者
Zafer Saad Al Shehri,Abdur Rehman
标识
DOI:10.2174/0109298673415885251023013748
摘要
introduction: Lung cancer remains a major global health burden with high mortality rates and limited therapeutic options. Natural flavonoids, particularly those derived from Cassia species, have shown immunomodulatory and anticancer potential. This study investigates the therapeutic promise of selected Cassia-derived flavonoids targeting key lung cancer-associated proteins: Prostaglandin-endoperoxide synthase 2 (PTGS2), Mast/stem cell growth factor receptor (KIT), and Xanthine dehydrogenase (XDH). Methodology:: Eight flavonoids were selected based on literature and database-reported bioactivity. Target prediction was performed using SwissTargetPrediction and STITCH, followed by pathway enrichment via STRING and KEGG databases. Molecular docking was conducted using AutoDock Vina against PTGS2 (PDB: 5IKQ), KIT (1N5X), and XDH (4U0I). Top-ranked complexes underwent 100 ns molecular dynamics (MD) simulations with GROMACS to assess binding stability, RMSD, and conformational behavior. Drug-likeness, ADME, and toxicity profiles were evaluated using SwissADME and ProTox-II. Standard drugs (Trametinib, Nivolumab, Erlotinib) were used for comparison. results: Epicatechin and Hispidulin showed the strongest binding affinities with PTGS2 (−9.04 kcal/mol) and XDH (−8.22 kcal/mol), respectively, with stable RMSD profiles. Chrysoeriol demonstrated the highest binding to KIT (−8.68 kcal/mol), outperforming Nivolumab (−6.03 kcal/mol). All selected flavonoids displayed acceptable pharmacokinetic profiles and low predicted toxicity. MD simulations confirmed the dynamic stability of key complexes. conclusion: Cassia-derived flavonoids represent promising multi-target candidates for lung cancer therapy, particularly through modulation of PTGS2, KIT, and XDH. Their favorable interaction profiles and safety predictions warrant further experimental and in vivo validation.
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