Klebsiella pneumoniae represents one of the most concerning ESKAPE pathogens, with multidrug-resistant strains driving urgent clinical interest in phage therapy as a viable alternative to antibiotics. However, the evolutionary arms race between phages and bacteria has equipped K. pneumoniae with sophisticated anti-phage immune defenses, posing a substantial barrier to durable therapeutic success. Through systematic analysis of K. pneumoniae-phage co-evolutionary dynamics, we identify predominant resistance mechanisms and discuss why these mechanisms primarily concentrate on adsorption blocking pathways. We then integrate clinical case studies with preclinical research to evaluate combination strategies against phage resistance, particularly highlighting synergistic approaches using antibiotics-phage or phage cocktails/phage serial therapy that increase selective pressure while reducing bacterial host adaptability and pathogenicity. Finally, we propose a computational roadmap leveraging machine learning for phage characterization, host-interaction prediction and de novo genome engineering, with particular emphasis on minimizing resistance emergence. This interdisciplinary review provides both immediate clinical guidance and a forward-looking vision for rational phage design, applicable beyond not only to K. pneumoniae but also to other high-priority pathogens. We also highlight that integrations of synthetic biology, computational science, and microbiology will be essential for transitioning phage therapy from experimental treatments to standardized interventions addressing antimicrobial resistance.
Keywords: Artificial intelligence; Biological foundation model; Klebsiella pneumoniae; Phage engineering; Phage resistance; Phage therapy.
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