Isorhamnetin 3-O-glucoside

Bairui Granules (BRKL), a standardized botanical drug derived from Thesium chinense Turcz., has been clinically used for respiratory infections. However, its potential in ulcerative colitis (UC) and underlying mechanisms remain unexplored. This study elucidated the therapeutic effects and mechanisms of BRKL against UC through integrated phytochemistry, multi-omics, and pharmacological approaches.

We investigated the protective effects of BRKL on DSS-induced colitis in mice. Pathological characteristics and oxidative stress of the distal colon were assessed. The impact of BRKL on gut microbiota was analyzed using 16S rRNA sequencing, while metabolomics explored changes in small molecular metabolites. UPLC-QE-MS validated BRKL's prototype compounds in the bloodstream, and network pharmacology predicted its targets and pathways. Molecular docking/dynamics validated target engaagement, followed by experimental validation in vitro and in vivo.

BRKL significantly alleviated colitis symptoms, restored gut barrier integrity, and reduced oxidative stress. It reshaped gut microbiota composition (Akkermansia, Lachnospiraceae; Escherichia-Shigella) and modulated 22 colon metabolites linked to histidine and JAK-STAT pathways. Network pharmacology identified JAK2 as a core target, corroborated by molecular dynamics showing stable binding of BRKL-derived Withalongolide L to JAK2. BRKL and its active component Isorhamnetin-3-O-glucoside (0.1 mg/g crude drug) suppressed IL-6/JAK2/STAT3 phosphorylation, reduced apoptosis, and inhibited pro-inflammatory cytokines . Structural analysis confirmed Isorhamnetin-3-O-glucoside as a novel JAK2 inhibitor scaffold (Tanimoto index <0.3 vs. clinical inhibitors).

BRKL ameliorates UC by synchronously modulating gut microbiota-metabolite interactions and blocking the JAK2/STAT3 pathway. Isorhamnetin-3-O-glucoside are identified as key bioactive compounds, positioning BRKL as a promising phytotherapeutic for inflammatory bowel disease.

Employing the technique of liquid chromatography-mass spectrometry (LCMS) in conjunction with artificial intelligence (AI) technology to predict and screen for antirheumatoid arthritis (RA) active compounds in Xanthocerais lignum.

Natural products have become an important source of new drug discovery. RA is a chronic autoimmune disease characterized by joint inflammation and systemic inflammation. Although there are many drugs available for the treatment of RA, they still have many side effects and limitations. Therefore, finding more effective and safer natural products for the treatment of RA has become an important issue.

In this study, a collection of inhibitors targeting RA-related specific targets was gathered. Machine learning models and deep learning models were constructed using these inhibitors. The performance of the models was evaluated using a test set and ten-fold cross-validation, and the most optimal model was selected for integration. A total of five commonly used machine learning algorithms (logistic regression, k-nearest neighbors, support vector machines, random forest, XGBoost) and one deep learning algorithm (GCN) were employed in this research. Subsequently, a Xanthocerais lignum compound library was established through HPLC-Q-Exactive- MS analysis and relevant literature. The integrated model was utilized to predict and screen for anti-RA active compounds in Xanthocerais lignum.

The integrated model exhibited an AUC greater than 0.94 for all target datasets, demonstrating improved stability and accuracy compared to individual models. This enhancement enables better activity prediction for unknown compounds. By employing the integrated model, the activity of 69 identified compounds in Xanthocerais lignum was predicted. The results indicated that isorhamnetin-3-O-glucoside, myricetin, rutinum, cinnamtannin B1, and dihydromyricetin exhibited inhibitory effects on multiple targets. Furthermore, myricetin and dihydromyricetin were found to have relatively higher relative abundances in Xanthocerais lignum, suggesting that they may serve as the primary active components contributing to its anti-RA effects.

In this study, we utilized AI technology to learn from a large number of compounds and predict the activity of natural products from Xanthocerais lignum on specific targets. By combining AI technology and the LC-MS approach, rapid screening and prediction of the activity of natural products based on specific targets can be achieved, significantly enhancing the efficiency of discovering new bioactive molecules from medicinal plants.