Engineering smart liposomes to combat antimicrobial resistance: from mechanistic design to clinical translation

INTRODUCTION: Antimicrobial resistance (AMR) represents a critical global health threat, surpassing the pace of discovery and clinical development of new antimicrobial agents. Therefore, innovative therapeutic strategies are urgently needed to enhance antibiotic efficacy, overcome multidrug-resistant infections, and limit resistance development. Smart liposomal nanoplatforms have emerged as promising tools capable of improving antimicrobial therapy through targeted delivery, enhanced bacterial uptake, biofilm penetration, protection against enzymatic degradation, and controlled drug release. AREAS COVERED: This review examines recent advances in the rational design of evolved liposomes, including stimuli-responsive and programmable systems, for combating resistant bacterial infections. It discusses their ability to improve pharmacokinetic and pharmacodynamic profiles, overcome biological barriers, and enable precision antimicrobial delivery. Current translational challenges, including large-scale manufacturing, physicochemical characterization, regulatory requirements, and clinically relevant preclinical models, are critically evaluated. Emerging strategies, such as artificial intelligence-driven formulation optimization, continuous microfluidic manufacturing, multifunctional therapeutic platforms incorporating gene editing, and next-generation antibiotic-adjuvant combination strategies, are also explored. EXPERT OPINION: Liposomal drug-delivery systems may improve the exposure, distribution, and intracellular or local delivery of antimicrobial agents, but improved delivery should not be equated with reversal of bacterial resistance. Successful clinical translation will depend on formulation- and infection-specific evidence, clinically relevant preclinical models, controlled and scalable manufacturing, and appropriate regulatory frameworks, while artificial intelligence, digital-twin, and precision-medicine approaches are best viewed as decision-support tools that complement experimental and clinical validation.