Published Paper Details:

The multi-target effects of bioactive compounds and their therapeutic potential are well explained by a network pharmacology framework. Computational network pharmacology methods were applied in this research to evaluate Scaevola taccada leaf extract's wound-healing activity. The GC-MS analysis identified a total of 20 active phytoconstituents, and Lipinski's rule was applied to filter for drug-likeness. 192 target-identified relevant genes were contrasted with 5,482 wound healing-related genes retrieved from the GeneCards database. 86 overlapping targets were determined through Venn analysis and are considered to be candidate therapeutic targets for wound healing. Essential hub genes were obtained through protein-protein interaction (PPI) analysis and included PPARG, ESR1, HIF1A, and MAPK1 as being essential regulators. GO enrichment analysis identified their involvement in inflammation, proliferation, and tissue remodelling through significant roles in nuclear receptor activation, transcription factor binding, and cell migration. Participation of putative targets in significant pathways like estrogen signaling, HIF-1 signaling, MAPK signaling, and regulation of lipid/atherosclerosis was again confirmed by KEGG pathway enrichment. The complex interaction between phytochemicals and molecular targets was demonstrated by the compound-target-pathway network constructed with Cytoscape. All of these findings indicate the prospective use of Scaevola taccada as a natural medicinal compound by showing it to modulate multi-gene and multi-pathway interactions to achieve wound-healing effects. This article emphasizes the employment of network pharmacology in validating traditional medicinal herbs for modern drug discovery, aside from providing molecular evidence for the pharmacological basis of Scaevola taccada