Defensins as natural antimicrobial peptide scaffolds against antimicrobial-resistant pathogens: mechanisms,...

The growing threat of antimicrobial resistance has increased the need for anti-infective approaches beyond conventional single-target antibiotics. Defensins, a conserved family of cysteine-rich antimicrobial peptides, are promising candidates owing to their structural stability, membrane activity, target-specific mechanisms, immunomodulatory functions, and potential synergy with existing antibiotics. This critical narrative review discusses defensins as potential therapeutics against antimicrobial-resistant pathogens, with a focus on structure-activity relationships, bacterial envelope biology, resistance evolution and cross-resistance, antibiofilm activity, and translational feasibility. Data were synthesized from mammalian, plant, fungal, and insect defensins, together with defensin-derived peptides and defensin mimetics. Unrelated AMPs were included only as contextual comparators and were not treated as defensin-specific evidence. Antibacterial proof-of-concept and translational-readiness evidence were appraised separately, including activity under physiological ionic-strength and serum conditions, protease stability, cytotoxicity, hemolysis, resistance selection, and in vivo efficacy. However, clinical translation has been hampered by inconsistent testing standards, incomplete pharmacokinetic/pharmacodynamic characterization, safety concerns, manufacturing challenges, and inadequate resistance surveillance. Existing evidence does not support classifying defensins as resistance-proof or uniformly less resistance-prone than conventional antibiotics. Instead, they should be regarded as versatile scaffolds whose resistance risk requires candidate-specific evaluation. Future studies should combine standardized broth microdilution with testing in serum, protease-rich environments, and mature biofilms. Candidate progression should require infection-site PK/PD, route-appropriate safety, and efficacy in chronic-wound, device-biofilm, or mucosal-infection models. AI-guided design and delivery systems should advance only when they demonstrably improve stability, exposure, activity, or tolerability, with resistance monitored throughout development and use.