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Exploring the role of Yuxuebi tablet in neuropathic pain with the method of similarity research of drug pharmacological effects based on unsupervised machine learning.
Du, Xiao; Zhao, Chunhui; Xi, Yujie; Lin, Pengfei; Liu, Huihui; Wang, Shuling; Guo, Feifei.
Affiliation
  • Du X; State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
  • Zhao C; College of Pharmacy, School of Medicine, Hangzhou Normal University, Hangzhou, China.
  • Xi Y; State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
  • Lin P; State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
  • Liu H; China Resources Sanjiu Medical and Pharmaceutical Co., Ltd., Shenzhen, China.
  • Wang S; China Resources Sanjiu Medical and Pharmaceutical Co., Ltd., Shenzhen, China.
  • Guo F; College of Pharmacy, School of Medicine, Hangzhou Normal University, Hangzhou, China.
Front Pharmacol ; 15: 1440542, 2024.
Article in En | MEDLINE | ID: mdl-39355777
ABSTRACT

Introduction:

Having multiple pharmacological effects is a characteristic of Traditional Chinese Medicine (TCM). Currently, there is a lack of suitable methods to explore and discover modern diseases suitable for TCM treatment using this characteristic. Unsupervised machine learning technology is an efficient strategy to predict the pharmacological activity of drugs. This study takes Yuxuebi Tablet (YXB) as the research object. Using the unsupervised machine learning technology of drug cell functional fingerprint similarity research, the potential pharmacological effects of YXB were discovered and verified.

Methods:

LC-MS combined with the in vitro intestinal absorption method was used to identify components of YXB that could be absorbed by the intestinal tract of rats. Unsupervised learning hierarchical clustering was used to calculate the degree of similarity of cellular functional fingerprints between these components and 121 marketed Western drugs whose indications are diseases and symptoms that YXB is commonly used to treat. Then, based on the Library of Integrated Network-based Cellular Signatures database, pathway analysis was performed for selected Western drugs with high similarity in cellular functional fingerprints with the components of YXB to discover the potential pharmacological effects of YXB, which were validated by animal experiments.

Results:

We identified 40 intestinally absorbed components of YXB. Through predictive studies, we found that they have pharmacological effects very similar to non-steroidal anti-inflammatory drugs (NSAIDs) and corticosteroids. In addition, we found that they have very similar pharmacological effects to anti-neuropathic pain medications (such as gabapentin, duloxetine, and pethidine) and may inhibit the NF-κB signaling pathway and biological processes related to pain perception. Therefore, YXB may have an antinociceptive effect on neuropathic pain. Finally, we demonstrated that YXB significantly reduced neuropathic pain in a rat model of sciatic nerve chronic constriction injury (CCI). Transcriptome analysis further revealed that YXB regulates the expression of multiple genes involved in nerve injury repair, signal transduction, ion channels, and inflammatory response, with key regulatory targets including Sgk1, Sst, Isl1, and Shh.

Conclusion:

This study successfully identified and confirmed the previously unknown pharmacological activity of YXB against neuropathic pain through unsupervised learning prediction and experimental verification.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Pharmacol Year: 2024 Document type: Article Affiliation country: China Country of publication: Switzerland

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Pharmacol Year: 2024 Document type: Article Affiliation country: China Country of publication: Switzerland