Alamri Mubarak A, Tahir Ul Qamar Muhammad
Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
Integrative Omics and Molecular Modeling Laboratory, Department of Bioinformatics and Biotechnology, Government College University, Faisalabad 38000, Pakistan.
Saudi Pharm J. 2023 Nov;31(11):101802. doi: 10.1016/j.jsps.2023.101802. Epub 2023 Sep 28.
Inflammation is a nonspecific immune response against injury caused by a harmful agent that strives to restore tissue function and homeostasis. L.f. (Sapindaceae) is a medium-sized shrub used to treat a variety of diseases in traditional medicine. In the current study, integrated network-pharmacology and molecular docking approaches were used to identify the active constituents, their possible targets, signaling pathways, and anti-inflammatory effects of flavonoids from . active ingredients were acquired from the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT), and Traditional Chinese Medicine System Pharmacology (TCMSP) databases. The screening included the ten most prevalent components, and the SwissTargetPrediction database was utilized to anticipate the targets of these compounds. Anti-inflammatory genes were found using the GeneCards database. The 175 overlapping genes were discovered as prospective anti-inflammatory targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the overlapped targets were closely related to the major pathogenic processes linked to inflammation, such as response to organonitrogen compound, protein kinase activity, phosphotransferase activity, pI3k-Akt signaling pathway, metabolic pathways, and chemical carcinogenesis. Compound-target-pathway, and protein-protein interaction networks revealed 6-Methoxykaempferol and 5-Hydroxy-7,8 dimethoxyflavone as key compounds, and AKT1, VEGFA, and EGFR as key targets. Furthermore, molecular docking followed by molecular dynamic (MD) simulation of active ingredients with core proteins fully complemented the binding affinity of these compounds and indicated stable complexes at the docked site. These findings reveal 's multi-target, multi-compound, and multi-pathway strategies against inflammation. Our study paved the way for further research into the mechanism for developing -based natural products as alternative therapies for inflammation.
炎症是针对有害因子引起的损伤的非特异性免疫反应，旨在恢复组织功能和内环境稳态。无患子科植物倒地铃是一种中型灌木，在传统医学中用于治疗多种疾病。在本研究中，采用综合网络药理学和分子对接方法，以确定倒地铃中黄酮类化合物的活性成分、可能的靶点、信号通路和抗炎作用。活性成分从《印度药用植物、植物化学与治疗学》（IMPPAT）和中药系统药理学（TCMSP）数据库中获取。筛选包括十种最常见的成分，并利用瑞士靶点预测数据库预测这些化合物的靶点。使用基因卡片数据库查找抗炎基因。发现175个重叠基因作为潜在的抗炎靶点。基因本体论和京都基因与基因组百科全书（KEGG）富集分析表明，重叠靶点与炎症相关的主要致病过程密切相关，如对有机氮化合物的反应、蛋白激酶活性、磷酸转移酶活性、磷脂酰肌醇-3激酶-蛋白激酶B（PI3K-Akt）信号通路、代谢途径和化学致癌作用。化合物-靶点-通路和蛋白质-蛋白质相互作用网络显示，6-甲氧基山奈酚和5-羟基-7,8-二甲氧基黄酮为关键化合物，蛋白激酶B1（AKT1）、血管内皮生长因子A（VEGFA）和表皮生长因子受体（EGFR）为关键靶点。此外，活性成分与核心蛋白的分子对接及随后的分子动力学（MD）模拟充分补充了这些化合物的结合亲和力，并表明在对接位点形成稳定的复合物。这些发现揭示了倒地铃针对炎症的多靶点、多化合物和多途径策略。我们的研究为进一步研究基于倒地铃的天然产物作为炎症替代疗法的作用机制铺平了道路。
