LEAPS-MPS: Optimal Design of Therapeutic Phage Cocktails: a Data-Driven Mathematical Approach

Antimicrobial resistance has been described as one of the biggest threats to human health in the twenty-first century. Antibiotic-resistant bacteria, found in people, animals, plants, and the environment, have rapidly emerged and spread throughout the world. Bacteriophages, or phages for short, are viruses that have evolved to infect and kill bacteria. Before the widespread use of antibiotics, phage therapy was successfully applied for the treatment of a variety of infections in the 1920s and 1930s. As a century-old infection remedy, phage therapy is currently viewed as a potential antibiotic alternative and is being widely redeveloped to treat multidrug-resistance infections. There are millions of phages in existence, each with different properties, it is therefore impossible to experimentally test them for efficacy against individual clinical pathogens. Mathematical models can help identify characteristics that would suggest that a phage is a promising therapeutic candidate. This project will lead to the development of an optimal combination phage cocktail and antibiotic therapy and further reduce the health risks at the human-animal-plant-ecosystem interface caused by antimicrobial resistance. The project will engage undergraduate students with different research backgrounds and interests and support underrepresented students in STEM at The College of Wooster. Students will benefit from receiving year-round interdisciplinary training in formulating genuine life science questions into standard mathematical problems. The project will additionally advance curricular and program development, which will enhance the institution's research environment and further establish a sustained, student-focused, and interdisciplinary research program in mathematical biology at Wooster. The project will explore the treatment efficacies of single, double simultaneous, and double sequential administration strategies with three Pseudomonas phages by using nonlinear ordinary differential equations to model the density-dependent interactions between multiple-phage and bacteria. Unlike previous studies that focused on the phage-killing aspect of phage therapy, this project will incorporate both phage-killing and the evolution of bacterial phage resistance. Understanding both is important for the future development of a combination phage cocktail and antibiotic therapy. The investigator will consider a more biologically realistic mixing than the commonly used linear interaction, as adsorption rate is linked to bacterial growth, investigate the stochastic modeling approach to capture the emergence of phage-resistant bacteria, and analyze the structure of the model using bifurcation and sensitivity analyses to inform further modeling modifications. The modeling framework has many inferential and exploratory uses for clinical investigation such as identifying the most sensitive model parameters, corresponding to phage characteristics, for phage selection as well as exploring different treatment regimens. The species-specific and phage-specific modeling studies can be used as predictive analytics tools to assist in the design of future clinical studies and to improve the understanding of experimental data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.