title: A network pharmacology approach to determine active compounds and action mechanisms of ge-gen-qin-lian decoction for treatment of type 2 diabetes.
abstract: Traditional Chinese medicine (TCM) herbal formulae can be valuable therapeutic strategies and drug discovery resources. However, the active ingredients and action mechanisms of most TCM formulae remain unclear. Therefore, the identification of potent ingredients and their actions is a major challenge in TCM research. In this study, we used a network pharmacology approach we previously developed to help determine the potential antidiabetic ingredients from the traditional Ge-Gen-Qin-Lian decoction (GGQLD) formula. We predicted the target profiles of all available GGQLD ingredients to infer the active ingredients by clustering the target profile of ingredients with FDA-approved antidiabetic drugs. We also applied network target analysis to evaluate the links between herbal ingredients and pharmacological actions to help explain the action mechanisms of GGQLD. According to the predicted results, we confirmed that a novel antidiabetic ingredient from Puerariae Lobatae radix (Ge-Gen), 4-Hydroxymephenytoin, increased the insulin secretion in RIN-5F cells and improved insulin resistance in 3T3-L1 adipocytes. The network pharmacology strategy used here provided a powerful means for identifying bioactive ingredients and mechanisms of action for TCM herbal formulae, including Ge-Gen-Qin-Lian decoction.
authors: MOE Key Laboratory of Bioinformatics, Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China.
authors: Huiying
authors: H
authors: Li
authors: Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
authors: Linhua
authors: L
authors: Zhao
authors: MOE Key Laboratory of Bioinformatics, Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China ; Tianjin International Joint Academy of Biotechnology & Medicine, Tianjin 300457, China.
authors: Bo
authors: B
authors: Zhang
authors: Wuxi Medical School, Jiangnan University, Wuxi 214122, China.
authors: Yuyu
authors: Y
authors: Jiang
authors: MOE Key Laboratory of Bioinformatics, Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China.
authors: Xu
authors: X
authors: Wang
authors: Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
authors: Yun
authors: Y
authors: Guo
authors: Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
authors: Hongxing
authors: H
authors: Liu
authors: MOE Key Laboratory of Bioinformatics, Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China.
authors: Shao
authors: S
authors: Li
authors: Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
authors: Xiaolin
authors: X
authors: Tong