Quantitative Models of Phage-Antibiotic Combination Therapy

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.

Kinetic Fingerprinting Links Bacteria-Phage Interactions with Emergent Dynamics: Rapid Depletion of Klebsiella pneumoniae Indicates Phage Synergy

“…Additional environmental interactions such as anti-bacterial immunity in bacterial hosts [ 17 , 18 ] or anti-microbial interventions [ 25 , 39 , 40 ] may further influence the kinetics of phages and their bacterial host. This shows the importance to study bacteria phage systems in well defined settings as exemplified in our study to distinguish the influence of many potential contributions to the kinetic profile of more complex bacteria phage mixtures and their ecology.…”

“…This phage displays a high sequence homology of about 95% to members of the K. pneumonia phage K32 family [ 21 ]. To decipher the kinetic fingerprint of the K. pneumoniae vB_KpnP_Lessing pair we built on modeling studies [ 22 , 23 , 24 ] which have implemented knowledge on phage–host interactions and thereby advanced the understanding of specific phage–bacteria interactions and ecological dynamics at large [ 17 , 18 , 25 ]. In particular, we focused on a model for a single bacterial strain and phage species [ 14 ] and extend it to allow for more flexibility in infection dynamics.…”

The Impact of Pf Bacteriophages on the Fitness ofPseudomonas aeruginosa; A Mathematical Modeling Approach

“…Understanding the forces that influence the progression of these infections is critical for developing effective clinical treatments. Mathematical models can yield important insights into these dynamics (10)(11)(12)(13)(14)(15).…”

BACPHLIP: Predicting bacteriophage lifestyle from conserved protein domains

“…1 Introduction 1 Bacteriophages play important ecological roles (1)(2)(3)(4) and influence both the physiology and 2 evolution of host species (5-7). Additionally, phages possess a number of unique traits (8,9) 3 that are relevant to ongoing biotechnological and medical applications (10)(11)(12). 4 The availability of phage genome sequences has expanded greatly in recent years-owing 5 largely to the development of meta-genomics (13)(14)(15)(16)(17)(18).…”

“…7 Phage lifestyle ultimately needs to be determined experimentally, but it is infeasible to per-8 form the required experiments exhaustively on the large number of newly discovered phage 9 genomes. 10 McNair et al [23] developed a machine learning classifier (PHACTS) to predict phage 11 lifestyle from the protein sequences encoded within the genome. However, PHACTS was 12 trained and tested on a set of phages that were available in 2012-a total of 227.…”