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Objective: This study aimed to systematically investigate the potential antibacterial mechanisms of ethacridine in the treatment of diabetic foot ulcers (DFUs) by integrating network pharmacology, molecular docking, and molecular dynamics simulation approaches. Methods: The potential targets of ethacridine were predicted using the SwissTargetPrediction and PharmMapper databases and subsequently converted to gene symbols via the UniProt database. DFU-related and antibacterial-related targets were retrieved from the GeneCards and OMIM databases. The overlapping targets among ethacridine, DFU, and antibacterial-related genes were identified as candidate therapeutic targets. A “drug–disease–target” network was constructed using Cytoscape, while protein–protein interaction (PPI) networks were built through the STRING database. GO and KEGG enrichment analyses were performed using R software. Molecular docking was conducted to evaluate the binding affinities between core compounds and hub targets. Furthermore, molecular dynamics (MD) simulation was applied to assess the binding stability of the top-ranked compound–target complex. Finally, RT-qPCR was conducted on wound edge tissue samples from DFU patients treated with ethacridine to experimentally validate the mRNA expression of predicted hub genes. Results: A total of 302 potential ethacridine-related targets, 4264 DFU-related targets, and 1942 antibacterial-related targets were identified. Intersection analysis revealed 105 common targets potentially involved in the antibacterial effects of ethacridine against DFU. PPI network analysis highlighted 10 hub targets, including AKT1, EGFR, SRC, HSP90AA1, and MMP9. GO enrichment indicated significant involvement in responses to reactive oxygen species, regulation of inflammatory responses, responses to lipopolysaccharide, and bacterial molecular patterns. KEGG pathway analysis identified 157 relevant pathways, including the lipid and atherosclerosis, TNF signaling, IL-17 signaling, and the AGE–RAGE signaling pathways in diabetic complications. Molecular docking demonstrated favorable binding affinities (all < −5.0 kcal/mol) between ethacridine and the hub targets, with the strongest binding observed between MMP9 and ethacridine (−9.8 kcal/mol). These docking results suggest possible interaction tendencies that may contribute indirectly to Ethacridine’s network-level regulatory effects, rather than direct binding to all targets in vivo. Molecular dynamics simulation further confirmed the stable interaction between MMP9 and ethacridine. RT-qPCR validation in clinical DFU tissue samples demonstrated expression trends of key genes consistent with in silico predictions. These results reflect transcriptional regulation consistent with pathway modulation predicted by the network analysis, rather than direct protein–ligand binding across all targets. Conclusion: Ethacridine may exert antibacterial effects against bacterial biofilms in DFU through multi-target and multi-pathway mechanisms. These findings highlight ethacridine’s translational potential as a safe, readily available, and mechanistically validated topical agent for the clinical management of biofilm-associated diabetic foot infections.
Li et al. (Tue,) studied this question.