Work

Exploring the Mechanism of "Radix Bupleuri-Paeoniae Radix Alba" in the Treatment of Post-Stroke Depression Based on Network Pharmacology and Molecular Docking

Year: 2024

Type: article

Abstract: Purpose: To explore the mechanism of "Radix Bupleuri-Paeoniae Radix Alba" in the treatment of post-stroke depression through network pharmacology and molecular docking. Methods: The active ingredients and targets of "Radix Bupleuri-Paeoniae Radix Alba" were predicted by Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. The related targets of post-stroke depression were searched in GeneCards, NCBI and DisGeNET databases. Cystoscape software was used to construct the "Chinese Medicine-Active Ingredient-Disease-Intersection Target" network, followed by Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The active ingredients corresponding to the core targets were verified by molecular docking techniques. Results: A total of 145 potential core targets of "Radix Bupleuri-Paeoniae Radix Alba" in the treatment of post-stroke depression were identified. The core ingredients such as quercetin, kaempferol and β-sitosterol, and the key protein targets such as RAC-alpha serine/threonine-protein kinase 1 (AKT1), Interleukin-6 (IL-6) and Interleukin-1β (IL-1β) were screened out. KEGG pathway enrichment analysis mainly involved "lipids and atherosclerosis", "chemical carcinogenesis-receptor activation", and "hepatitis B". Molecular docking results showed that quercetin and kaempferol, the main active ingredients of "Radix Bupleuri-Paeoniae Radix Alba", had good affinity with the key potential targets AKT1, IL-6 and IL-1β. Conclusion: The pair of "Radix Bupleuri-Paeoniae Radix Alba" may act on the core targets such as AKT1, IL-6 and IL-1β through the main active ingredients including quercetin and kaempferol, and may effectively treat post-stroke depression by regulating multiple signaling pathways such as "lipids and atherosclerosis" and "hepatitis B".

Source: Journal of Experimental and Clinical Application of Chinese Medicine

DOI: 10.62767/jecacm504.9815

Authors: Xia Zhou, Zhidong Zeng, Yanli Fu, Lei Chen

Institution: Guangzhou University of Chinese Medicine

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: Traditional Chinese Medicine Bureau of Guangdong Province