4.5 Article

Network pharmacology and molecular docking-based prediction of active compounds and mechanisms of action of Cnidii Fructus in treating atopic dermatitis

Journal

BMC COMPLEMENTARY MEDICINE AND THERAPIES
Volume 22, Issue 1, Pages -

Publisher

BMC
DOI: 10.1186/s12906-022-03734-7

Keywords

Cnidium monnieri; Network pharmacology; Atopic dermatitis; Gene Ontology; KEGG pathway

Funding

  1. NSFC [81874358, 82174029, 2021A1515010658, 2020A1515010579, JCYJ20200109150719846]

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This study evaluated the active compounds and molecular targets of Cnidii Fructus (CF) in treating atopic dermatitis (AD). It identified 19 active compounds in CF, predicted 532 potential targets, and collected 1540 genes related to AD. GO enrichment analysis showed that CF affects biological processes and molecular functions associated with inflammation and steroid hormone receptor activity. KEGG pathway analysis indicated that PI3K-Akt signaling, calcium signaling, Rap1 signaling, and cAMP signaling pathways are involved in the anti-AD effects of CF. Molecular docking analysis revealed the binding of key active compounds in CF with therapeutic targets.
Background Atopic dermatitis (AD) is a common inflammatory skin disease that compromises the skin's barrier function and capacity to retain moisture. Cnidii Fructus (CF), the dried fruits of Cnidium monnieri, has long been used to treat atopic dermatitis (AD) in China. However, the anti-AD compounds and mechanisms of CF are not fully understood. In this study, we evaluated the active compounds and molecular targets of CF in treating AD. Methods The Traditional Chinese Medicine Systems Pharmacology database was used to acquire information regarding the compounds that occur in the herb. Targets of these compounds were predicted using the SwissTargetPrediction website tool. AD-related genes were collected from the GeneCards database. Gene ontology (GO) enrichment analysis and KEGG pathway analysis of proteins that are targeted by active compounds of CF and encoded by AD-related genes were performed using Database for Annotation, Visualization, and Integrated Discovery Bioinformatics Resources. A compound-target network was constructed and analyzed using Cytoscape Software. Molecular docking was performed using BIOVIA Discovery Studio Visualizer and AutoDock Vina. Results We identified 19 active compounds in CF, 532 potential targets for these compounds, and 1540 genes related to AD. Results of GO enrichment indicated that CF affects biological processes and molecular functions, such as inflammatory response and steroid hormone receptor activity, which may be associated with its anti-AD effects. KEGG pathway analyses showed that PI3K-Akt signaling, calcium signaling, Rap1 signaling, and cAMP signaling pathways are the main pathways involved in the anti-AD effects of CF. Molecular docking analyses revealed that the key active compounds in CF, such as (E)-2,3-bis(2-keto-7-methoxy-chromen-8-yl)acrolein, ar-curcumene, and diosmetin, can bind the main therapeutic targets AKT1, SRC, MAPK3, EGFR, CASP3, and PTGS2. Conclusions Results of the present study establish a foundation for further investigation of the anti-AD compounds and mechanisms of CF and provide a basis for developing modern anti-AD agents based on compounds that occur in CF.

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