<p>This study evaluates the potential of repurposed non-antibiotic drugs as possible treatments for methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) skin infection, through a comprehensive approach combining in silico, in vitro, and in vivo methodologies. From 198 protein targets previously identified through multi-omics analysis, 10 essential, druggable, broad-spectrum targets were selected. Three promising non-antibiotic ligands (rupatadine, orlistat, and citric acid) were evaluated through minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), molecular docking, cytotoxicity testing, and a murine model of MRSA skin infection. Rupatadine, orlistat, and citric acid showed MICs of 0.031&#xa0;mg/mL, 1.5&#xa0;mg/mL, and 2.5&#xa0;mg/mL, respectively. Rupatadine demonstrated the most potent anti-MRSA activity, with molecular docking revealing favorable binding energy (− 3.45&#xa0;kcal/mol) to Aminoacyl transferase (FemA). Serial passage experiments over eight passages showed no resistance development against rupatadine in one MRSA strain and only twofold increase in another, compared to 8–13 fold increases observed with conventional antibiotics. Checkerboard assays revealed synergistic effects between rupatadine and β-lactam antibiotics (cefazolin and cefotaxime, FICI = 0.3). FemA is essential in cell wall biosynthesis and represents an essential target for anti-MRSA agents. Functional enrichment and pathway network analyses revealed significant disruption of biological processes governing peptidoglycan biosynthesis and cell wall biogenesis, which indicates the possibility of rupatadine compromising <i>S. aureus</i> cell wall integrity through interference with peptidoglycan assembly pathways, supporting its potential as a repurposed anti-MRSA agent. Rupatadine showed favorable tolerance on human skin fibroblast cells (IC<sub>50</sub>: 1150&#xa0;µg/mL), and significantly reduced bacterial count in a murine model.</p>

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Repurposing rupatadine as topical treatment against methicillin-resistant Staphylococcus aureus

  • Asmaa Farag,
  • Dalia El-Damasy,
  • Dalia Soliman,
  • Galal Yahya,
  • Kareem Ibrahim

摘要

This study evaluates the potential of repurposed non-antibiotic drugs as possible treatments for methicillin-resistant Staphylococcus aureus (MRSA) skin infection, through a comprehensive approach combining in silico, in vitro, and in vivo methodologies. From 198 protein targets previously identified through multi-omics analysis, 10 essential, druggable, broad-spectrum targets were selected. Three promising non-antibiotic ligands (rupatadine, orlistat, and citric acid) were evaluated through minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), molecular docking, cytotoxicity testing, and a murine model of MRSA skin infection. Rupatadine, orlistat, and citric acid showed MICs of 0.031 mg/mL, 1.5 mg/mL, and 2.5 mg/mL, respectively. Rupatadine demonstrated the most potent anti-MRSA activity, with molecular docking revealing favorable binding energy (− 3.45 kcal/mol) to Aminoacyl transferase (FemA). Serial passage experiments over eight passages showed no resistance development against rupatadine in one MRSA strain and only twofold increase in another, compared to 8–13 fold increases observed with conventional antibiotics. Checkerboard assays revealed synergistic effects between rupatadine and β-lactam antibiotics (cefazolin and cefotaxime, FICI = 0.3). FemA is essential in cell wall biosynthesis and represents an essential target for anti-MRSA agents. Functional enrichment and pathway network analyses revealed significant disruption of biological processes governing peptidoglycan biosynthesis and cell wall biogenesis, which indicates the possibility of rupatadine compromising S. aureus cell wall integrity through interference with peptidoglycan assembly pathways, supporting its potential as a repurposed anti-MRSA agent. Rupatadine showed favorable tolerance on human skin fibroblast cells (IC50: 1150 µg/mL), and significantly reduced bacterial count in a murine model.