Abstract
The traditional activity-guided approach has the shortcoming of low accuracy and efficiency in discovering active compounds from TCM. In this work, an approach was developed by integrating activity index (AI), liquid chromatography - mass spectrometry (LC-MS), and nuclear magnetic resonance (NMR) to rapidly predict and identify the potential active constituents from TCM. This approach was used to discover and identify the anti-inflammatory constituents from a TCM formula, Gui-Zhi-Jia-Shao-Yao-Tang (GZJSYT). The AI results indicated that, among the 903 constituents detected in GZJSYT by LC-MS, 61 constituents with higher AI values were very likely to have anti-inflammatory activities. And eight potential active constituents of them were isolated and validated to have significant inhibitory effects against NO production on LPS-induced RAW 264.7 cell model. Among them, glycyrrhisoflavone (836), glisoflavanone (893) and isoangustone A (902) were reported to have anti-inflammatory effects for the first time. The proposed approach could be generally applicable for rapid and high efficient discovery of anti-inflammatory constituents from other TCM formulae or natural products.