Rushendran Rapuru, Singh S Ankul, Begum Rukaiah Fatma, Chitra Vellapandian, Ali Nemat, Prajapati Bhupendra G
Department of Pharmacology, SRM Institute of Science and Technology, SRM College of Pharmacy, Chennai, India.
Department of Pharmacology, Dr. M.G.R Educational and Research Institute, Faculty of Pharmacy, Chennai, India.
Drug Dev Res. 2025 Feb;86(1):e70038. doi: 10.1002/ddr.70038.
The central nervous system is affected by multiple sclerosis (MS), a chronic autoimmune illness characterized by axonal destruction, demyelination, and inflammation. This article summarizes the state of the field, highlighting its complexity and significant influence on people's quality of life. The research employs a network pharmacological approach, integrating systems biology, bioinformatics, and pharmacology to identify biomarkers associated with MS. Utilizing Nelumbo Nucifera (Lotus) seeds, the study involves toxicity assessments, biomolecule screening, and target prediction. Advanced computational methodologies are employed, including molecular docking and dynamic simulations, to assess potential therapeutic interactions. Biomolecule screening identifies eight active compounds from Lotus seeds, including Anonaine and Liriodenine. Target prediction reveals 264 common targets with MS-related genes. Protein-protein interaction analysis establishes a complex network, identifying central targets like SRC and AKT1. Bioinformatics enrichment analysis uncovers potential therapeutic candidates and pathways. A Biomolecule-Target-Pathway network diagram visualizes interactions, with Anonaine and Liriodenine exhibiting strong binding affinities in molecular docking studies. Molecular dynamics simulations provide insights into dynamic interactions. In conclusion, through advanced computational techniques, it unveils molecular interactions, potential therapies, and pathways, bridging predictions with practical applications. Anonaine and Liriodenine show promise in curbing MS biomarkers.
中枢神经系统会受到多发性硬化症(MS)的影响,这是一种慢性自身免疫性疾病,其特征为轴突破坏、脱髓鞘和炎症。本文总结了该领域的现状,突出了其复杂性以及对人们生活质量的重大影响。该研究采用网络药理学方法,整合系统生物学、生物信息学和药理学来识别与MS相关的生物标志物。利用莲子进行研究,包括毒性评估、生物分子筛选和靶点预测。采用了先进的计算方法,包括分子对接和动态模拟,以评估潜在的治疗相互作用。生物分子筛选从莲子中鉴定出八种活性化合物,包括去甲乌药碱和鹅掌楸碱。靶点预测揭示了与MS相关基因的264个共同靶点。蛋白质-蛋白质相互作用分析建立了一个复杂的网络,确定了SRC和AKT1等核心靶点。生物信息学富集分析揭示了潜在的治疗候选物和途径。生物分子-靶点-途径网络图直观展示了相互作用,去甲乌药碱和鹅掌楸碱在分子对接研究中表现出很强的结合亲和力。分子动力学模拟提供了对动态相互作用的见解。总之,通过先进的计算技术,它揭示了分子相互作用、潜在疗法和途径,将预测与实际应用联系起来。去甲乌药碱和鹅掌楸碱在抑制MS生物标志物方面显示出前景。