Biofilm-driven antimicrobial resistance: A review of molecular mechanisms, clinical implications, and therapeutic innovation.

Abstract

Biofilms are structured microbial communities embedded within a self-produced extracellular polymeric substance matrix that promotes persistence under adverse environmental and host-associated conditions. Their clinical importance is primarily associated with increased antimicrobial tolerance, evasion of host immune responses, and persistence in chronic and medical device-associated infections. This review provides an integrated overview of the molecular and genetic determinants governing biofilm development, including surface attachment, matrix biosynthesis, quorum-sensing networks, cyclic-di-GMP signaling, maturation, and dispersal. The contribution of key matrix components, including polysaccharides, extracellular proteins, extracellular DNA, lipids, and water, is considered in relation to biofilm architecture, stability, and cellular adaptation. Clinically relevant biofilm-forming microorganisms, including Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, and Candida albicans, are major contributors to chronic infections, healthcare-associated infections, and infections associated with medical devices. The mechanisms contributing to reduced antimicrobial susceptibility in biofilms include restricted drug penetration, metabolic heterogeneity, persister-cell formation, efflux activity, stress-response pathways, and horizontal gene transfer. Conventional in vitro and in vivo biofilm models often fail to fully mimic the complex conditions present within the human host, thereby limiting the clinical translation and therapeutic relevance of experimental findings. In addition, emerging antibiofilm interventions, including quorum-sensing inhibitors, matrix-degrading enzymes, nanoparticle-based delivery systems, antimicrobial peptides, and bacteriophage therapy, are considered alongside the growing application of multi-omics and artificial intelligence for biomarker discovery and therapeutic target identification.