Jintao Xue1, Yongli Shi1, Chunyan Li1,2, Huijie Song1. 1. Department of TCM, School of Pharmacy, Xinxiang Medical University, Xinxiang, China. 2. Experimental Education Center of Biology and Basic Medical Science, Sanquan College of Xinxiang Medical University, Xinxiang, China.
Abstract
AIMS: Compound Lian-Ge granules (CLGGs) is a traditional Chinese medicine preparation with good hypoglycemic effect and health function. This study was to predict its active ingredients, potential targets, signaling pathways, and investigate its mechanism of "ingredient-targets-pathways." METHODS: Pharmacodynamics studies on diabetic rats showed that CLGGs had an obvious hypoglycemic effect. On this basis, 27 hypoglycemic active ingredients were screened out. Their targets were confirmed by comparing with these hypoglycemic targets in PharmMapper and DrugBank databases via reversed pharmacophore matching approach. The relationships between ingredients and targets were revealed by comparing data in the String database. A network of "ingredient-target-passageway" was constructed. RESULTS: Studies showed that CLGGs had 24 active ingredients, ie, berberine, puerarin, danshinolic acid A, and sinigrin, etc. These ingredients involved nine targets, ie, insulin-like growth factor 1 receptor, insulin-degrading enzyme, ɑ-amylase, and so on, and 111 metabolic pathways, eg, hypoxia-inducible factor 1 signaling pathway, PI3K-Akt signaling pathway, mammalian target of rapamycin signaling pathway, and FoxO signaling pathway. CONCLUSION: Using network pharmacology methods, this study predicted the hypoglycemic active ingredients in CLGGs and revealed their targets, and provided a clue for further exploration of the hypoglycemic mechanism of CLGGs.
AIMS: Compound Lian-Ge granules (CLGGs) is a traditional Chinese medicine preparation with good hypoglycemic effect and health function. This study was to predict its active ingredients, potential targets, signaling pathways, and investigate its mechanism of "ingredient-targets-pathways." METHODS: Pharmacodynamics studies on diabeticrats showed that CLGGs had an obvious hypoglycemic effect. On this basis, 27 hypoglycemic active ingredients were screened out. Their targets were confirmed by comparing with these hypoglycemic targets in PharmMapper and DrugBank databases via reversed pharmacophore matching approach. The relationships between ingredients and targets were revealed by comparing data in the String database. A network of "ingredient-target-passageway" was constructed. RESULTS: Studies showed that CLGGs had 24 active ingredients, ie, berberine, puerarin, danshinolic acid A, and sinigrin, etc. These ingredients involved nine targets, ie, insulin-like growth factor 1 receptor, insulin-degrading enzyme, ɑ-amylase, and so on, and 111 metabolic pathways, eg, hypoxia-inducible factor 1 signaling pathway, PI3K-Akt signaling pathway, mammalian target of rapamycin signaling pathway, and FoxO signaling pathway. CONCLUSION: Using network pharmacology methods, this study predicted the hypoglycemic active ingredients in CLGGs and revealed their targets, and provided a clue for further exploration of the hypoglycemic mechanism of CLGGs.