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Feb 04, 2020;5 (1):

Quantitative Models of Phage-Antibiotic Combination Therapy.

Rogelio A Rodriguez-Gonzalez, Chung Yin Leung, Benjamin K Chan, Paul E Turner, Joshua S Weitz
Author Information
  1. Rogelio A Rodriguez-Gonzalez: Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, Georgia, USA.
  2. Chung Yin Leung: School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.
  3. Benjamin K Chan: Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, USA.
  4. Paul E Turner: Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, USA.
  5. Joshua S Weitz: School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA jsweitz@gatech.edu. ORCID
PMID: 32019835 DOI: 10.1128/mSystems.00756-19

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 application given an immune response against bacteria. We simulate the combination therapy model for two strains of , 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 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. This work develops and analyzes a novel model of phage-antibiotic combination therapy, specifically adapted to an context. The objective is to explore the underlying basis for clinical application of combination therapy utilizing bacteriophage that target antibiotic efflux pumps in 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 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.

Keywords

antimicrobial agents bacteriophage therapy bacteriophages evolutionary biology mathematical modeling microbial ecology

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Grants

  1. R01 AI146592/NIAID NIH HHS
  2. UL1 TR001863/NCATS NIH HHS
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