Integrating data mining and network pharmacology for traditional Chinese medicine for drug discovery of diabetic peripheral neuropathy.

IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS SLAS Technology Pub Date : 2024-12-01 Epub Date: 2024-12-03 DOI:10.1016/j.slast.2024.100228
Jing Ping, Hong-Zheng Hao, Zhen-Qi Wu, Yong-Ju Yang, He-Shan Yu
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引用次数: 0

Abstract

The purpose of this study was to examine the therapeutic potential of core traditional Chinese medicine (CTCM) in the treatment of diabetic peripheral neuropathy (DPN) through the use of a data-driven approach that combined network pharmacology and data mining. Important components of traditional Chinese medicine (TCM) and the targets that correspond with them were found through the examination of numerous databases and clinical prescriptions. The possible therapeutic pathways were investigated, with an emphasis on the AGE-RAGE pathway that was discovered via network pharmacology analysis. By evaluating histopathological alterations, inflammatory and apoptotic markers, microcirculation, and blood hypercoagulability in a rat model of DPN, the effectiveness of CTCM was confirmed.Through experimental validation in DPN rats, it was shown that CTCM improved histopathology, decreased inflammation and apoptosis, improved microcirculation, and corrected coagulation abnormalities in addition to alleviating neuropathic pain. These studies show the value of data-driven approaches in advancing traditional medicine research for drug development and offer a mechanistic basis for CTCM's therapeutic potential in DPN.

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来源期刊
SLAS Technology
SLAS Technology Computer Science-Computer Science Applications
CiteScore
6.30
自引率
7.40%
发文量
47
审稿时长
106 days
期刊介绍: SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.
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