Liu Lihua, Zhu Yingying, Fu Peng, Yang Jundong
Laizhou City People's Hospital, Laizhou, Yantai, China.
Department of Pharmacy, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China.
Front Aging Neurosci. 2022 Apr 8;14:822480. doi: 10.3389/fnagi.2022.822480. eCollection 2022.
In order to explore and further understand the efficacy of donepezil (DNP) in the treatment of Alzheimer's disease (AD), this research was conducted based on network pharmacology and molecular docking.
Compounds of DNP and its effective targets were collected using the TCMSP Chinese medicine system pharmacology database. Disease targets were screened and selected utilizing GeneCards, TTD, DrugBank, CTD, and other online databases. Then, Venn diagrams were generated to identify the intersections. A diseases-drug-active ingredient-key target protein interaction (PPI) network was constructed using the STING database. GO and KEGG enrichment analyses were conducted to predict the function and mechanism of DNP, which were visualized by graphs and bubble charts. After the screening, the top five interacting targets in the PPI network and the compound containing the most active target were selected for molecular docking.
The study received 110 potential targeting genes and 155 signaling pathways. A strong association between DNP and modulation of chemical synaptic transmission and the regulation of trans-synaptic signaling is noted. Signaling pathways related to the proliferation, differentiation, and survival of cells are also found positively relative. The results revealed that the mechanism of its therapeutic effect is multi-component, multi-target, and multi-pathway, laying a foundation for the follow-up in-depth study of the mechanism of DNP in the treatment of AD.
This research provides a superior prediction that AD could be treated using DNP which targets the key proteins and essential pathways associated with the recovery of AD.
为了探索并进一步了解多奈哌齐(DNP)治疗阿尔茨海默病(AD)的疗效,本研究基于网络药理学和分子对接展开。
利用中药系统药理学数据库(TCMSP)收集DNP的化合物及其有效靶点。通过GeneCards、TTD、DrugBank、CTD等在线数据库筛选并选取疾病靶点。然后,绘制维恩图以确定交集。使用STING数据库构建疾病-药物-活性成分-关键靶蛋白相互作用(PPI)网络。进行基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析,以预测DNP的功能和作用机制,并通过图形和气泡图进行可视化展示。筛选后,选择PPI网络中相互作用最强的前五个靶点以及含有最活跃靶点的化合物进行分子对接。
该研究获得了110个潜在的靶向基因和155条信号通路。注意到DNP与化学突触传递的调节以及跨突触信号的调控之间存在密切关联。还发现与细胞增殖、分化和存活相关的信号通路呈正相关。结果表明其治疗作用机制是多成分、多靶点、多途径的,为后续深入研究DNP治疗AD的机制奠定了基础。
本研究提供了一个有力的预测,即可以使用DNP治疗AD,其靶向与AD恢复相关的关键蛋白和重要途径。