Vancomycin resistance in gram-positive infections: evolutionary strategies of survival

Vancomycin is a critical glycopeptide antibiotic for treating severe infections caused by Gram-positive bacteria, particularly MRSA and Clostridioides difficile, by inhibiting cell wall synthesis through binding to D-Ala-D-Ala termini of peptidoglycan precursors. Resistance has emerged in Enterococcus spp (VRE) and Staphylococcus spp, (VISA/VRSA) through acquisition of van operons, precursor modification (D-Ala-D-Lac/D-Ser), cell wall thickening, biofilm formation, and regulatory mutations, leading to treatment failures and increased morbidity. Global genomic surveillance reveals ongoing clonal expansion and horizontal spread of resistance determinants. This review comprehensively examines vancomycin's mechanism of action, the evolutionary emergence and genetic basis of resistance, adaptive survival strategies of pathogens, clinical/epidemiological consequences, current alternative therapies, and precision stewardship approaches including area under the concentration-time curve/minimum inhibitory concentration (AUC/MIC)-guided therapeutic drug monitoring (TDM). Most importantly, it highlights the transformative and still under-appreciated role of artificial intelligence in overcoming vancomycin resistance: machine learning accelerates discovery of novel antimicrobial peptides and repurposed drugs, AI-driven surveillance enables real-time resistance detection and outbreak forecasting, and hybrid AI-molecular modeling rationally designs superior vancomycin derivatives with enhanced activity against VRE and VRSA. These rapidly evolving AI-integrated strategies, when combined with strengthened infection control and stewardship, offer the most promising path forward to preserve and extend the clinical utility of vancomycin and related antibiotics.