Towards modeling phage therapy

Abstract

Patients infected with life-threatening multi-drug resistant (MDR) bacteria have been treated with cocktails of bacteriophages. This is a complicated form of personalized medicine as the phages given to a patient have to be selected beforehand on the basis of their lytic capacity of the infecting bacteria. Because bacteria rapidly become resistant, the evolution of resistance to a diverse cocktail of phages is a complicated dynamical process, during which competing bacterial strains replace one another by accumulating several resistance mechanisms, each of which may involve a fitness cost. As a consequence, it is typically not known why a particular phage therapy succeeded or failed, and how one can optimize the composition of the cocktails to maximize the rate of success. To improve upon this, we extend an existing in vivo-calibrated mouse model into a novel mathematical model for the human situation, and include multiple phages infecting multiple bacterial strains, differing in their resistance to each of the phages. We adjust several parameter estimates of the bacterial model to the human situation, and use the model to describe a successful case of phage therapy involving several cocktails, each containing several phages. In the model, treatment success crucially depended on pretreatment resistance levels, and on the diversity and the timing of the cocktails. Once an appropriate cocktail is found, it is less important to further optimize the infection rates of the phages. Resistant bacterial strains expand rapidly when sensitive strains decline, and the higher the infectivity of the phages, the faster resistant strains expand. Because resistance evolves rapidly, it is best to provide a diverse set of phages right from the start of therapy, i.e., to hit hard and early, and create a high genetic barrier to bacterial resistance. Author summary: Patients with dangerous antibiotic-resistant bacteria have been treated with mixtures of bacteriophages — viruses that infect bacteria. This treatment is highly personalized because the right phages must be selected for each patient’s infection. Since bacteria quickly evolve resistance to phages, it is often unclear why a treatment works or fails, and how to design the best phage combinations. To better understand this process, we developed a mathematical model of phage therapy in humans based on earlier mouse studies. The model includes multiple bacterial strains and multiple phages, each with different resistance patterns. We used the model to study a successful real-world phage therapy case involving several phage cocktails. The results show that treatment success depends strongly on the bacteria’s resistance before treatment, as well as on the diversity and timing of the phage cocktails. Since resistant bacteria rapidly take over once sensitive bacteria decline, the best strategy is to start treatment early with a broad and diverse phage cocktail to make it harder for bacteria to evolve resistance

Original publication: PLOS Computational Biology (2026-06-22). Source. Source DOI: 10.1371/journal.pcbi.1014408.