ArticleCurrent computer-aided drug design2025
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.
Article in Current computer-aided drug design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Antidiabetic Effect of Coronopus Didymus, Azadirachta Indica and Their Active Compound Quercetin Against STZ-NA Induced Diabetes Mice Models.Applied biochemistry and biotechnology · 2026Article
- Insulin signaling pathway related mNutrition & metabolism · 2026Article
- Comprehensive Studies on the Regulation of Type 2 Diabetes by Cucurbitane-Type Triterpenoids inPharmaceuticals (Basel, Switzerland) · 2025Article
- An analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcers.Frontiers in genetics · 2025Article
- Immunomodulatory roles of quercetin in diabetic nephropathy: targeting inflammation, oxidative stress, and ferroptosis.Frontiers in pharmacology · 2025Review
- Targeting Autophagy: A Promising Therapeutic Strategy for Diabetes Mellitus and Diabetic Nephropathy.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundDapagliflozin is commonly used to treat type 2 diabetes mellitus (T2DM). However, research into the specific anti-T2DM mechanisms of dapagliflozin remains scarce.
objectiveThis study aimed to explore the underlying mechanisms of dapagliflozin against T2DM.
methodsDapagliflozin-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.
resultsIn 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.
conclusionDapagliflozin might treat T2DM mainly by targeting AKT1, HSP90AA1, RELA, ITGB1, and TP53 through PI3K-Akt signaling.
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