Abstract
The spread of multidrug-resistant (MDR) bacteria is a global public health crisis. Bacteriophage therapy (or "phage therapy") constitutes a potential alternative approach to treat MDR infections. However, the effective use of phage therapy may be limited when phage-resistant bacterial mutants evolve and proliferate during treatment. Here, we develop a nonlinear population dynamics model of combination therapy that accounts for the system-level interactions between bacteria, phage, and antibiotics for in vivo application given an immune response against bacteria. We simulate the combination therapy model for two strains of Pseudomonas aeruginosa, one which is phage sensitive (and antibiotic resistant) and one which is antibiotic sensitive (and phage resistant). We find that combination therapy outperforms either phage or antibiotic alone and that therapeutic effectiveness is enhanced given interaction with innate immune responses. Notably, therapeutic success can be achieved even at subinhibitory concentrations of antibiotics, e.g., ciprofloxacin. These in silico findings provide further support to the nascent application of combination therapy to treat MDR bacterial infections, while highlighting the role of innate immunity in shaping therapeutic outcomes.IMPORTANCE This work develops and analyzes a novel model of phage-antibiotic combination therapy, specifically adapted to an in vivo context. The objective is to explore the underlying basis for clinical application of combination therapy utilizing bacteriophage that target antibiotic efflux pumps in Pseudomonas aeruginosa In doing so, the paper addresses three key questions. How robust is combination therapy to variation in the resistance profiles of pathogens? What is the role of immune responses in shaping therapeutic outcomes? What levels of phage and antibiotics are necessary for curative success? As we show, combination therapy outperforms either phage or antibiotic alone, and therapeutic effectiveness is enhanced given interaction with innate immune responses. Notably, therapeutic success can be achieved even at subinhibitory concentrations of antibiotic. These in silico findings provide further support to the nascent application of combination therapy to treat MDR bacterial infections, while highlighting the role of system-level feedbacks in shaping therapeutic outcomes.
Highlights
The spread of multidrug-resistant (MDR) bacteria is a global public health crisis
The results suggest that a curative outcome is possible when phage are combined with antibiotics in an immunocompetent host— even when the phage is initially mistargeted to the dominant bacterial strain
The infection clearance shows robustness to variations in the concentration of antibiotic, delays in the administration of the combined therapy, the bacterial composition of the inoculum, and model assumptions
Summary
The spread of multidrug-resistant (MDR) bacteria is a global public health crisis. Bacteriophage therapy (or “phage therapy”) constitutes a potential alternative approach to treat MDR infections. We develop a nonlinear population dynamics model of combination therapy that accounts for the system-level interactions between bacteria, phage, and antibiotics for in vivo application given an immune response against bacteria. Therapeutic success can be achieved even at subinhibitory concentrations of antibiotics, e.g., ciprofloxacin These in silico findings provide further support to the nascent application of combination therapy to treat MDR bacterial infections, while highlighting the role of innate immunity in shaping therapeutic outcomes. Therapeutic success can be achieved even at subinhibitory concentrations of antibiotic These in silico findings provide further support to the nascent application of combination therapy to treat MDR bacterial infections, while highlighting the role of system-level feedbacks in shaping therapeutic outcomes. The realized outcomes of combination strategies are varied, ranging from successes in vitro [9] and in vivo [4, 11] to failure given in vitro settings [6]
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