Exploration of Pharmacological Mechanisms of Dapagliflozin against Type 2 Diabetes Mellitus through PI3K-Akt Signaling Pathway based on Network Pharmacology Analysis and Deep Learning Technology.

Abstract

Background: Dapagliflozin is commonly used to treat type 2 diabetes mellitus (T2DM). However, research into the specific anti-T2DM mechanisms of dapagliflozin remains scarce.

Objective: This study aimed to explore the underlying mechanisms of dapagliflozin against T2DM.

Methods: Dapagliflozin-associated targets were acquired from CTD, SwissTargetPrediction, and SuperPred. T2DM-associated targets were obtained from GeneCards and DigSee. VennDiagram was used to obtain the overlapping targets of dapagliflozin and T2DM. GO and KEGG analyses were performed using clusterProfiler. A PPI network was built by STRING database and Cytoscape, and the top 30 targets were screened using the degree, maximal clique centrality (MCC), and edge percolated component (EPC) algorithms of CytoHubba. The top 30 targets screened by the three algorithms were intersected with the core pathway-related targets to obtain the key targets. DeepPurpose was used to evaluate the binding affinity of dapagliflozin with the key targets.

Results: In total, 155 overlapping targets of dapagliflozin and T2DM were obtained. GO and KEGG analyses revealed that the targets were primarily enriched in response to peptide, membrane microdomain, protein serine/threonine/tyrosine kinase activity, PI3K-Akt signaling pathway, MAPK signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. AKT1, PIK3CA, NOS3, EGFR, MAPK1, MAPK3, HSP90AA1, MTOR, RELA, NFKB1, IKBKB, ITGB1, and TP53 were the key targets, mainly related to oxidative stress, endothelial function, and autophagy. Through the DeepPurpose algorithm, AKT1, HSP90AA1, RELA, ITGB1, and TP53 were identified as the top 5 anti-targets of dapagliflozin.

Conclusion: Dapagliflozin might treat T2DM mainly by targeting AKT1, HSP90AA1, RELA, ITGB1, and TP53 through PI3K-Akt signaling.