Search Results (2)

Traditional Chinese medicine (TCM) possesses unique advantages in the management of blood glucose and lipids. However, there is still a significant gap in the exploration of its pharmacologically active components. Integrated strategies encompassing deep-learning prediction models and active validation based on absorbable ingredients can greatly improve the identification rate and screening efficiency in TCM. In this study, the affinity prediction of 11,549 compounds from the traditional Chinese medicine system’s pharmacology database (TCMSP) with dipeptidyl peptidase-IV (DPP-IV) based on a deep-learning model was firstly conducted. With the results, Gardenia jasminoides Ellis (GJE), a food medicine with homologous properties, was selected as a model drug. The absorbed components of GJE were subsequently identified through in vivo intestinal perfusion and oral administration. As a result, a total of 38 prototypical absorbed components of GJE were identified. These components were analyzed to determine their absorption patterns after intestinal, hepatic, and systemic metabolism. Virtual docking and DPP-IV enzyme activity experiments were further conducted to validate the inhibitory effects and potential binding sites of the common constituents of deep learning and sequential metabolism. The results showed a significant DPP-IV inhibitory activity (IC₅₀ 53 ± 0.63 μg/mL) of the iridoid glycosides’ potent fractions, which is a novel finding. Genipin 1-gentiobioside was screened as a promising new DPP-IV inhibitor in GJE. These findings highlight the potential of this innovative approach for the rapid screening of active ingredients in TCM and provide insights into the molecular mechanisms underlying the anti-diabetic activity of GJE. Full article

Fructus Gardeniae (FG) is a common Chinese medicine and food. However, the toxicity of FG has drawn increasing concern, especially its hepatotoxicity. The purpose of this study was to screen the hepatotoxic components of FG and evaluate their effects on rat liver BRL-3A cells. The chemical composition of FG was determined by HPLC-ESI-MS. CCK-8 assay was used to evaluate the cytotoxicity of ten chemical components from FG, and then the toxic components with significant inhibitory activity were selected for further study. The results showed that geniposide, genipin, genipin-1-gentiobioside, gardenoside, and shanzhiside all suppress cells viability. Apoptosis assays further indicated that geniposide and its metabolite genipin are the main hepatotoxic components of FG. Pretreatment of cells with geniposide or genipin increased the levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP). The activities of superoxide dismutase (SOD) and glutathione (GSH) were decreased, while the malondialdehyde (MDA) level was increased. The cell contents of tumor necrosis factor (TNF-α), interleukin-6 (IL-6), and nitric oxide (NO) were also increased. Molecular docking simulations were used to investigate the mechanism of FG-induced hepatotoxicity, revealing that geniposide and genipin bind strongly to the pro-inflammatory factor TNFR1 receptor of the NF-κB and MAPK signaling pathways. The obtained results strongly indicate that the hepatotoxicity of FG is caused by iridoids compounds. Genipin had the most significant hepatotoxic effect. These toxic substances destroy the cell antioxidant defense system, increasing inflammatory injury to the liver cells and leading to apoptosis and even necrosis. Thus, this study lays a foundation for toxicology research into FG and its rational application. Full article