swertiamarin and Idiopathic-Pulmonary-Fibrosis

Idiopathic pulmonary fibrosis is a chronic progressive disorder and is diagnosed as post-COVID fibrosis. Idiopathic pulmonary fibrosis has no effective treatment because of the low therapeutic effects and side effects of currently available drugs.. The aim is to screen new inhibitors against idiopathic pulmonary fibrosis from traditional Chinese medicines.. Few-shot-based machine learning and molecule docking were used to predict the potential activities of candidates and calculate the ligand-receptor interactions. In vitro A549 cell model was taken to verify the effects of the selected leads on idiopathic pulmonary fibrosis.. A logistic regression classifier model with an accuracy of 0.82 was built and, combined with molecule docking, used to predict the activities of candidates. 6 leads were finally screened out and 5 of them were in vitro experimentally verified as effective inhibitors against idiopathic pulmonary fibrosis.. Herbacetin, morusin, swertiamarin, vicenin-2, and vitexin were active inhibitors against idiopathic pulmonary fibrosis. Swertiamarin exhibited the highest anti-idiopathic pulmonary fibrosis effect and should be further in vivo investigated for its activity.

Topics: COVID-19; Drug Repositioning; Humans; Idiopathic Pulmonary Fibrosis; Machine Learning; Molecular Docking Simulation

Swertiamarin is the main hepatoprotective component of Swertiapatens and has anti-inflammatory and antioxidation effects. Our previous study showed that it was a potent inhibitor of idiopathic pulmonary fibrosis (IPF) and can regulate the expressions of α-smooth muscle actin (α-SMA) and epithelial cadherin (E-cadherin), two markers of the TGF-β/Smad (transforming growth factor beta/suppressor of mothers against decapentaplegic family) signaling pathway. But its targets still need to be investigated. The main purpose of this study is to identify the targets of swertiamarin.. GEO2R was used to analyze the differentially expressed genes (DEGs) of GSE10667, GSE110147, and GSE71351 datasets from the Gene Expression Omnibus (GEO) database. The DEGs were then enriched with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis for their biological functions and annotated terms. The protein-protein interaction (PPI) network was constructed to identify hub genes. The identified hub genes were predicted for their bindings to swertiamarin by molecular docking (MD) and validated by experiments.. 76 upregulated and 27 downregulated DEGs were screened out. The DEGs were enriched in the biological function of cellular component (CC) and 7 cancer-related signaling pathways. Three hub genes, i.e., LOX (lysyl oxidase), COL5A2 (collagen type V alpha 2 chain), and CTGF (connective tissue growth factor) were selected, virtually tested for the interactions with swertiamarin by MD, and validated by in vitro experiments.. LOX, COL5A2, and CTGF were identified as the targets of swertiamarin on IPF.

Topics: Computational Biology; Gene Expression Profiling; Humans; Idiopathic Pulmonary Fibrosis; Molecular Docking Simulation