Exploring the Antidiabetic Potential of Salvia officinalis Using Network Pharmacology, Molecular Docking and ADME/Drug-Likeness Predictions
Year: 2024
Type: article
Abstract: A combination of network pharmacology, molecular docking and ADME/drug-likeness predictions was employed to explore the potential of Salvia officinalis compounds to interact with key targets involved in the pathogenesis of T2DM. These were predicted using the SwissTargetPrediction, Similarity Ensemble Approach and BindingDB databases. Networks were constructed using the STRING online tool and Cytoscape (v.3.9.1) software. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis and molecular docking were performed using DAVID, SHINEGO 0.77 and MOE suite, respectively. ADME/drug-likeness parameters were computed using SwissADME and Molsoft L.L.C. The top-ranking targets were CTNNB1, JUN, ESR1, RELA, NR3C1, CREB1, PPARG, PTGS2, CYP3A4, MMP9, UGT2B7, CYP2C19, SLCO1B1, AR, CYP19A1, PARP1, CYP1A2, CYP1B1, HSD17B1, and GSK3B. Apigenin, caffeic acid, oleanolic acid, rosmarinic acid, hispidulin, and salvianolic acid B showed the highest degree of connections in the compound-target network. Gene enrichment analysis identified pathways involved in insulin resistance, adherens junctions, metabolic processes, IL-17, TNF-α, cAMP, relaxin, and AGE-RAGE in diabetic complications. Rosmarinic acid, caffeic acid, and salvianolic acid B showed the most promising interactions with PTGS2, DPP4, AMY1A, PTB1B, PPARG, GSK3B and RELA. Overall, this study enhances understanding of the antidiabetic activity of S. officinalis and provides further insights for future drug discovery purposes.
Source: Plants
Authors: Chimaobi J. Ononamadu, Veronique Seidel
Institutions: University of Strathclyde, Nigeria Police Academy
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,069
Award: G2595169894
OpenAlex ID: W4403608560