JOURNAL ARTICLE
A network pharmacology-based approach to explore the active ingredients and molecular mechanism of Shen-Kui-Tong-Mai granules on a rat model with chronic heart failure.
Published In: Journal of Pharmacy & Pharmacology, 2023, v. 75, n. 6. P. 764 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Huang, Hong; Xu, Junyao; Zhang, Siqi; Zhao, Jing; Liu, Shun; Tian, Lei; Wang, Haidan; Geng, Zhirong; Yan, Shihai 3 of 3
Abstract
The article focuses on investigating the active ingredients and molecular mechanisms of Shen-Kui-Tong-Mai granules (SKTMG), a traditional Chinese medicine formulation, in treating chronic heart failure (CHF) using a rat model. Employing a combination of network pharmacology, ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC–MS/MS), molecular docking, and in vivo validation, the study identified 192 active compounds and 307 potential targets of SKTMG, highlighting 10 core genes related to the MAPK signaling pathway, including AKT1, STAT3, MAPK1, P53, SRC, JUN, TNF, APP, MAPK8, and IL6. Key compounds such as luteolin, quercetin, astragaloside IV, and kaempferol demonstrated strong binding affinity to these targets, and SKTMG treatment in CHF rats improved cardiac function, reduced myocardial injury and fibrosis, and inhibited phosphorylation of AKT, P38, P53, c-JUN, as well as TNF-α expression. These findings suggest that SKTMG exerts cardioprotective effects in CHF primarily through modulation of the MAPK signaling pathway, providing a scientific basis for its clinical application and further drug development.
Additional Information
- Source:Journal of Pharmacy & Pharmacology. 2023/06, Vol. 75, Issue 6, p764
- Document Type:Article
- Subject Area:Complementary and Alternative Medicine
- Publication Date:2023
- ISSN:0022-3573
- DOI:10.1093/jpp/rgad009
- Accession Number:171966726
- Copyright Statement:Copyright of Journal of Pharmacy & Pharmacology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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