Network Pharmacology-Based Prediction of Catalpol and Mechanisms against Stroke

Jinghui Wang; Meifeng Zhang; Si Sun; Guoran Wan; Dong Wan; Shan Feng; Huifeng Zhu

doi:10.1155/2021/2541316

Network Pharmacology-Based Prediction of Catalpol and Mechanisms against Stroke

Evid Based Complement Alternat Med. 2021 Jan 7:2021:2541316. doi: 10.1155/2021/2541316. eCollection 2021.

Authors

Jinghui Wang¹, Meifeng Zhang¹, Si Sun¹, Guoran Wan², Dong Wan³, Shan Feng¹, Huifeng Zhu¹

Affiliations

¹ College of Pharmaceutical Sciences &; College of Chinese Medicine, Southwest University, Chongqing 400715, China.
² Department of Clinical Medicine, Chongqing Medical University, Chongqing 400016, China.
³ Department of Emergency and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.

Abstract

Aim: To apply the network pharmacology method to screen the target of catalpol prevention and treatment of stroke, and explore the pharmacological mechanism of Catalpol prevention and treatment of stroke.

Methods: PharmMapper, GeneCards, DAVID, and other databases were used to find key targets. We selected hub protein and catalpol which were screened for molecular docking verification. Based on the results of molecular docking, the ITC was used to determine the binding coefficient between the highest scoring protein and catalpol. The GEO database and ROC curve were used to evaluate the correlation between key targets.

Results: 27 key targets were obtained by mapping the predicted catalpol-related targets to the disease. Hub genes (ALB, CASP3, MAPK1 (14), MMP9, ACE, KDR, etc.) were obtained in the key target PPI network. The results of KEGG enrichment analysis showed that its signal pathway was involved in angiogenic remodeling such as VEGF, neurotrophic factors, and inflammation. The results of molecular docking showed that ACE had the highest docking score. Therefore, the ITC was used for the titration of ACE and catalpol. The results showed that catalpol had a strong binding force with ACE.

Conclusion: Network pharmacology combined with molecular docking predicts key genes, proteins, and signaling pathways for catalpol in treating stroke. The strong binding force between catalpol and ACE was obtained by using ITC, and the results of molecular docking were verified to lay the foundation for further research on the effect of catalpol on ACE. ROC results showed that the AUC values of the key targets are all >0.5. This article uses network pharmacology to provide a reference for a more in-depth study of catalpol's mechanism and experimental design.