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

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.

Keywords

• Data mining
• Network pharmacology
• Diabetic peripheral neuropathy
• Traditional Chinese medicine