PDF(Pubmed)
Abstract：
Neurodegenerative diseases, such as Alzheimer\'s disease (AD), Parkinson\'s disease (PD), and epilepsy, pose a growing global health challenge due to an aging population. These conditions share common processes, including protein accumulation, oxidative stress, and neuro-inflammation, making their treatment complex and costly. Network pharmacology, an innovative approach integrating systems biology and computational biology, offers insights into multi-target formulations and the repurposing of existing medications for neurodegenerative diseases. We shortlisted 730 bioactive compounds from 25 traditional Himalayan plants, assessed their drug-like properties using ADME criteria, and predicted their potential target proteins through reverse docking and pharmacophore mapping. Our study identified 287 compounds with high gastrointestinal absorption and good blood-brain barrier permeability. These compounds were subjected to target prediction, yielding a list of 171 potential target proteins. Functional annotation and pathway enrichment analysis highlighted their involvement in steroid hormone-related pathways, MAPK signaling, FOXO signaling, TNF signaling, VEGF signaling, and neurotrophin signaling. Importantly, one plant, Valeriana jatamansi, exhibited an association with beta-amyloid binding activity, a potential therapeutic approach for AD. From our study we could understand how these plants modulate our body to manage these diseases. However, further in vitro and in vivo validation is needed before commercial and public use of this data.
摘要：
神经退行性疾病，如阿尔茨海默病(AD)，帕金森病（PD），癫痫,由于人口老龄化，构成了日益严重的全球卫生挑战。这些条件共享共同的过程，包括蛋白质积累，氧化应激，和神经炎症，使他们的治疗复杂和昂贵。网络药理学,一种整合系统生物学和计算生物学的创新方法，提供了对多靶标制剂和现有药物对神经退行性疾病的再利用的见解。我们入围了来自25种传统喜马拉雅植物的730种生物活性化合物，使用ADME标准评估了它们的药物样特性，并通过反向对接和药效团作图预测其潜在的靶蛋白。我们的研究确定了287种具有高胃肠道吸收和良好血脑屏障通透性的化合物。对这些化合物进行目标预测,产生171个潜在靶蛋白的列表。功能注释和途径富集分析强调了它们参与类固醇激素相关途径，MAPK信号，FOXO信号,TNF信号,VEGF信号,和神经营养蛋白信号。重要的是,一株植物,Valerianajatamansi,表现出与β-淀粉样蛋白结合活性的关联，一种潜在的AD治疗方法。从我们的研究中，我们可以了解这些植物如何调节我们的身体来管理这些疾病。然而,在商业和公开使用该数据之前，还需要进一步的体外和体内验证。
