文献类型：期刊文献

英文题名：TCnet: A Novel Strategy to Predict Target Combination of Alzheimer's Disease via Network-Based Methods

作者：Yue, Chengyuan[1];Chen, Baiyu[1];Pan, Fei[1];Wang, Ze[1];Yu, Hongbo[1];Liu, Guixia[1];Li, Weihua[1];Wang, Rui[1];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China

年份：2025

卷号：65

期号：7

起止页码：3866

外文期刊名：JOURNAL OF CHEMICAL INFORMATION AND MODELING

收录：;EI(收录号：20251518198703);WOS:【SCI-EXPANDED(收录号:WOS:001457706300001)】；

基金：This work was supported by the National Natural Science Foundation of China (grants 82173746 and U23A20530) and the Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism (Shanghai Municipal Education Commission).

语种：英文

外文关键词：Aluminum compounds - Disease control - Gene therapy

摘要：Alzheimer's disease (AD) is a complex neurodegenerative disorder with an unclear pathogenesis; the traditional '' single gene-single target-single drug '' strategy is insufficient for effective treatment. This study explores a novel strategy for the multitarget therapy of AD by integrating multiomics data and employing network analysis. Different from conventional single-target methods, TCnet adopts a mechanism-driven strategy, utilizing multiomics data to decompose disease mechanisms, construct potential target combinations, and prioritize the optimal combinations using a scoring function. TCnet not only advances our understanding of disease mechanisms but also facilitates large-scale drug screening. This approach was further employed to screen active compounds from Huang-Lian-Jie-Du-Tang (HLJDT), identifying quercetin as a candidate targeting GSK3 beta and ADAM17. Subsequent in vitro experiments confirmed the neuroprotective and anti-inflammatory effects of quercetin. Overall, TCnet offers a promising approach for predicting target combinations and provides new insights and directions for drug discovery in AD.