Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
Chem Res Toxicol. 2021 Jan 18;34(1):91-102. doi: 10.1021/acs.chemrestox.0c00359. Epub 2020 Dec 17.
The traditional Chinese medicines (TCMs) have been used to treat diseases over a long history, but it is still a great challenge to uncover the underlying mechanisms for their therapeutic effects due to the complexity of their ingredients. Based on a novel network pharmacology-based approach, we explored in this study the potential therapeutic targets of Liuwei Dihuang (LWDH) decoction in its neuroendocrine immunomodulation (NIM) function. We not only collected the known targets of the compounds in LWDH but also predicted the targets for these compounds using the balanced substructure-drug-target network-based inference (bSDTNBI), which is a target prediction method based on network inferring developed by our laboratory. A "target-(pathway)-target" (TPT) network, in which targets of LWDH were connected by relevant pathways, was constructed and divided into several separate modules with strong internal connections. Then the target module that contributes the most to NIM function was determined through a contribution scoring algorithm. Finally, the targets with the highest contribution score to NIM-related diseases in this target module were recommended as potential therapeutic targets of LWDH.
中药在治疗疾病方面已有悠久的历史，但由于其成分复杂，要揭示其治疗效果的潜在机制仍然是一个巨大的挑战。本研究基于一种新颖的基于网络药理学的方法，探讨了六味地黄汤在神经内分泌免疫调节（NIM）功能中的潜在治疗靶点。我们不仅收集了六味地黄汤中化合物的已知靶点，还使用基于平衡子结构-药物-靶点网络推断（bSDTNBI）的方法预测了这些化合物的靶点，bSDTNBI 是我们实验室开发的一种基于网络推断的靶点预测方法。构建了一个“靶点-（通路）-靶点”（TPT）网络，其中六味地黄汤的靶点通过相关通路连接，并通过贡献评分算法确定对 NIM 功能贡献最大的靶点模块。最后，推荐此靶点模块中对 NIM 相关疾病贡献得分最高的靶点作为六味地黄汤的潜在治疗靶点。
