Antimicrobial peptides in the era of resistance: integrating AI-driven design, host-microbe interactions, and nanotechnological delivery

The escalating crisis of antimicrobial resistance (AMR) requires a fundamental transition from traditional antibiotic development to innovative therapeutic strategies rooted in microbial physiology and host defense mechanisms. Antimicrobial peptides (AMPs), evolutionarily conserved effectors of innate immunity, offer a potent alternative due to their rapid, multifaceted modes of action that reduce the likelihood of rapid resistance emergence compared to single-target antibiotics, although bacterial adaptation via membrane remodeling and efflux mechanisms remains a documented challenge. This review synthesizes recent advances in AMP research, focusing on their structural diversity, mechanistic interactions with microbial membranes, and immunomodulatory roles in shaping host-microbe dynamics. We critically examine the transition from empirical isolation to rational design, highlighting the integration of artificial intelligence (AI) and machine learning (ML) algorithms in predicting peptide efficacy and optimizing sequence-activity relationships. Furthermore, we explore the application of CRISPR-Cas technologies in engineering producer organisms and modulating pathogen susceptibility. A significant portion of this review addresses the translational challenges of AMPs, including proteolytic instability and toxicity, by evaluating next-generation nanotechnological delivery systems such as liposomal encapsulation and stimuli-responsive hydrogels. By bridging fundamental microbiological insights with cutting-edge computational and material science innovations, this article establishes a framework for overcoming current limitations. We conclude by outlining future perspectives for integrating AMPs into clinical practice, emphasizing their potential as synergistic agents in combination therapies and their role in extending the clinical utility of existing antimicrobial agents within a One Health context.