Phage therapy against Pseudomonas aeruginosa biofilm infections: challenges and advanced strategies
- 1School of Laboratory Medicine, Hubei University of Chinese Medicine
- 2Medical Science Research Center, Zhongnan Hospital of Wuhan University
Abstract
Bacteriophage therapy offers a potential solution for treating multidrug-resistant Pseudomonas aeruginosa infections, especially those involving biofilms that reduce antibiotic effectiveness. However, successful in vitro results often don not lead to consistent clinical success due to various challenges. These challenges are categorized into four areas: biofilm and bacterial defenses, phage ecological limitations, host-related pharmacokinetic and pharmacodynamic issues, and manufacturing and regulatory hurdles. In biofilms, phage activity is collectively shaped by extracellular matrix composition, receptor accessibility, and biofilm spatial structure. Dense matrix components including exopolysaccharides and extracellular DNA (eDNA) form a key barrier to phage penetration, while biofilm thickness further modulates phage access to target cells. Additionally, bacterial anti-phage mechanisms and prophage resistance decrease effectiveness, particularly in genetically diverse strains. Phage cocktail effectiveness is influenced more by replication dynamics, receptor compatibility, and dosing schedule than by the number of phages. Imbalanced replication and shared receptor targets can lead to inter-phage competition, reducing biofilm clearance compared to single-phage therapy. In vivo , phage activity is affected by bacterial receptor expression, mucus, immune response, and biofilm gradients that foster resistant subpopulations. Phage-antibiotic combinations can improve outcomes, but their interactions vary from synergistic to antagonistic based on dosage, timing, and bacterial strain, with resistance resensitization effects being highly variable. Clinical development is hindered by non-standardized production, simplistic preclinical models, and inconsistent regulations, though Artificial intelligence and machine learning (AI/ML) could aid in screening and optimizing phage cocktails, limited by data quality. This review discusses advanced strategies to address these barriers, with a primary focus on engineered phages, sequential administration regimens, AI-driven therapeutic design, and standardized manufacturing practices. Overall, this work aims to inform the optimization of phage therapy and support its progression toward a mainstream clinical option for chronic, drug-resistant P. aeruginosa biofilm infections.
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BibTeX
@article{xu2026phage,
title = {{Phage therapy against Pseudomonas aeruginosa biofilm infections: challenges and advanced strategies}},
author = {Qianhe Xu and Shaoping Liu and Juan Xi},
journal = {Frontiers in Microbiology},
year = {2026},
month = oct,
doi = {10.3389/fmicb.2026.1899503},
url = {https://www.frontiersin.org/articles/10.3389/fmicb.2026.1899503/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Phage therapy against Pseudomonas aeruginosa biofilm infections: challenges and advanced strategies
AU - Qianhe Xu
AU - Shaoping Liu
AU - Juan Xi
PY - 2026
DA - 2026/10/19
JO - Frontiers in Microbiology
DO - 10.3389/fmicb.2026.1899503
UR - https://www.frontiersin.org/articles/10.3389/fmicb.2026.1899503/full
AB - Bacteriophage therapy offers a potential solution for treating multidrug-resistant Pseudomonas aeruginosa infections, especially those involving biofilms that reduce antibiotic effectiveness. However, successful in vitro results often don not lead to consistent clinical success due to various challenges. These challenges are categorized into four areas: biofilm and bacterial defenses, phage ecological limitations, host-related pharmacokinetic and pharmacodynamic issues, and manufacturing and regulatory hurdles. In biofilms, phage activity is collectively shaped by extracellular matrix composition, receptor accessibility, and biofilm spatial structure. Dense matrix components including exopolysaccharides and extracellular DNA (eDNA) form a key barrier to phage penetration, while biofilm thickness further modulates phage access to target cells. Additionally, bacterial anti-phage mechanisms and prophage resistance decrease effectiveness, particularly in genetically diverse strains. Phage cocktail effectiveness is influenced more by replication dynamics, receptor compatibility, and dosing schedule than by the number of phages. Imbalanced replication and shared receptor targets can lead to inter-phage competition, reducing biofilm clearance compared to single-phage therapy. In vivo , phage activity is affected by bacterial receptor expression, mucus, immune response, and biofilm gradients that foster resistant subpopulations. Phage-antibiotic combinations can improve outcomes, but their interactions vary from synergistic to antagonistic based on dosage, timing, and bacterial strain, with resistance resensitization effects being highly variable. Clinical development is hindered by non-standardized production, simplistic preclinical models, and inconsistent regulations, though Artificial intelligence and machine learning (AI/ML) could aid in screening and optimizing phage cocktails, limited by data quality. This review discusses advanced strategies to address these barriers, with a primary focus on engineered phages, sequential administration regimens, AI-driven therapeutic design, and standardized manufacturing practices. Overall, this work aims to inform the optimization of phage therapy and support its progression toward a mainstream clinical option for chronic, drug-resistant P. aeruginosa biofilm infections.
SN - 1664-302X
ER -