Song Zhujin, Wu Yuhua, Luo Liping, Hu Qingqing, Wu Saiwei, Wu Miaolian, Zhang Guoqing
Department of Pharmacy, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
College of Basic Medical Science, Zhejiang Chinese Medical University, Hangzhou, China.
Front Nutr. 2025 Jul 16;12:1612746. doi: 10.3389/fnut.2025.1612746. eCollection 2025.
Accumulating clinical evidence demonstrates the therapeutic potential of Traditional Chinese Medicine (TCM) in mitigating depressive disorders. This research focuses on quercetin, a principal bioactive constituent shared among five classical TCM antidepressant formulations, to systematically decode its multi-target mechanisms via an integrative framework combining neuroinflammatory modulation and synaptic plasticity regulation.
A tripartite experimental design was implemented. Firstly, bibliometric analysis systematically screened antidepressant TCM prescriptions and their bioactive components. Secondly, network pharmacology delineated the therapeutic mechanisms of quercetin - a key phytochemical identified through prior analysis. Finally, we established a chronic unpredictable mild stress (CUMS)-induced depression-like behavior model in mice for validation.
Bibliometric analysis showed that the clinical efficacy of 5 TCM antidepressant prescriptions were identified by evidence-based medicine. In these prescriptions, , , and were the most commonly used herbs, while Quercetin was identified as the shared bioactive nexus across these prescriptions. Network pharmacology analysis revealed that quercetin may be closely related to PI3K/AKT pathway in depression. And results of animal experimentation showed that quercetin could improve depression-like behaviors and restore neurotransmitters levels. Concurrently, quercetin may inhibit neuroinflammation and ameliorate synaptic ultrastructural by PI3K/AKT pathway.
The present study elucidated the mechanism of quercetin, an active ingredient in TCM prescriptions, in the treatment of depression through data mining, network pharmacology prediction, and experimental validation. This integrated research method will provide a new perspective for the development of TCM.
越来越多的临床证据表明中药在缓解抑郁症方面具有治疗潜力。本研究聚焦于槲皮素，它是五种经典中药抗抑郁方剂共有的主要生物活性成分，通过结合神经炎症调节和突触可塑性调节的综合框架，系统地解码其多靶点机制。
实施了三方实验设计。首先，文献计量分析系统筛选抗抑郁中药方剂及其生物活性成分。其次，网络药理学描绘了槲皮素的治疗机制，槲皮素是通过先前分析确定的关键植物化学物质。最后，我们在小鼠中建立了慢性不可预测轻度应激（CUMS）诱导的抑郁样行为模型进行验证。
文献计量分析表明，5种中药抗抑郁方剂的临床疗效已通过循证医学得到确认。在这些方剂中，[此处原文缺失具体草药名称]、[此处原文缺失具体草药名称]和[此处原文缺失具体草药名称]是最常用的草药，而槲皮素被确定为这些方剂中共同的生物活性枢纽。网络药理学分析显示，槲皮素可能与抑郁症中的PI3K/AKT通路密切相关。动物实验结果表明，槲皮素可以改善抑郁样行为并恢复神经递质水平。同时，槲皮素可能通过PI3K/AKT通路抑制神经炎症并改善突触超微结构。
本研究通过数据挖掘、网络药理学预测和实验验证，阐明了中药方剂中的活性成分槲皮素在治疗抑郁症中的机制。这种综合研究方法将为中药的发展提供新的视角。
