Leveraging mathematical modeling framework to guide regimen strategy for phage therapy

Bacteriophage (phage) cocktail therapy has been relied upon more and more to treat antibiotic-resistant infections. Understanding of the complex kinetics between phages, target bacteria, and the emergence of phage resistance remain hurdles to successful clinical outcomes. Building upon previous mathematical concepts, we develop biologically-motivated nonlinear ordinary differential equation models to explore single, cocktail, and sequential phage treatment modalities. While the optimal pairwise phage treatment strategy was the double simultaneous administration of two highly potent and asymmetrically binding phage strains, it appears unable to prevent the evolution of resistance. This treatment regimen did have a greater lysis efficiency, promoted higher phage population sizes, reduced bacterial density the most, and suppressed the evolution of resistance the longest compared to all other treatments strategies tested. Conversely, the combination of phages with polar potencies allows the more efficiently replicating phages to monopolize susceptible host cells, thereby quickly negating the intended compounding effect of cocktails. Together, we demonstrate that a biologically-motivated modeling-based framework can be leveraged to quantify the effects of each phage’s properties to more precisely predict treatment responses.Author summary: Antimicrobials are one of the most significant medical advancements and are largely responsible for the reduction in morbidity and mortality associated with infectious diseases and routine medical procedures. However, the emergence and spread of antimicrobial resistance (AMR) has outpaced the development and approval of new antimicrobials, which has risen AMR as one of the leading global public health threats of the 21st century. Bacteriophages (phages for short) have long been considered a new class of antibacterials that can selectively target and kill AMR bacteria with great efficiency. However, optimizing the efficacy of phage therapy has been challenging due to the complex nature of employing a virus-based therapeutic. In this study, we synthesized in vitro and in silico models to determine optimal two-phage combination cocktails that lead to the eradication of the pathogen Pseudomonas aeruginosa. In addition, we uncover the pharmacokinetics and pharmacodynamics of phages in cocktails that may lead to treatment failure. Our findings could lead to a reduction in the risk of complications and failure of phage candidates and speed up the development of more effective phage therapies against AMR disease.