Drug Design, Development and Therapy (Sep 2021)

A Network Pharmacology Study on the Molecular Mechanism of Protocatechualdehyde in the Treatment of Diabetic Cataract

  • Cheng X,
  • Song Z,
  • Wang X,
  • Xu S,
  • Dong L,
  • Bai J,
  • Li G,
  • Zhang C

Journal volume & issue
Vol. Volume 15
pp. 4011 – 4023

Abstract

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Xiao Cheng, Zhihui Song, Xin Wang, Shanshan Xu, Liming Dong, Jie Bai, Guangyao Li, Chao Zhang Department of Pharmacy, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Chao ZhangDepartment of Pharmacy, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of ChinaTel/Fax +86-10-58267167Email [email protected]: Protocatechualdehyde (PCA) is a phenolic compound found in the roots of Salvia miltiorrhiza with anti-proliferative and antioxidant activities. At present, there are few studies on protocatechualdehyde against diabetic cataract (DC), and there is also lack of systematic research on the mechanism of protocatechualdehyde. Therefore, this study tried to comprehensively clarify the targets and complex mechanisms of PCA against DC from the perspective of network pharmacology.Materials and Methods: Through collecting relevant targets from the databases, GO and KEGG enrichment analysis were performed on the potential targets. Moreover, core genes were identified by topological analysis of protein–protein interaction (PPI) network and gene–phenotype correlation analysis.Results: The results indicated that protocatechualdehyde may be closely related to targets such as AKT1, MAPK3 and HDAC3, as well as signal pathways such as MAPK signaling pathway, PI3K-Akt signaling pathway and AGE-RAGE signaling pathway in diabetic complications.Conclusion: Together, the present study systematically clarified the possible mechanisms of protocatechualdehyde in the treatment of diabetic cataract and provided new ideas for the drug research of this disease.Keywords: protocatechualdehyde, diabetic cataract, network pharmacology, topological analysis, gene–phenotype correlation analysis

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