Speaker
Description
Combination therapy can be an effective strategy to limit the evolution and spread of antibiotic resistance, a major challenge for patient treatment worldwide. The administration of multiple drugs during treatment increases the genetic barrier to resistance, as (in the absence of cross-resistance) the bacteria would need to evolve resistance against each drug separately. Yet the success of this strategy ultimately depends on how well the combination controls the dynamics within the bacterial population. How should we thus combine antibiotics to limit the evolution and spread of resistance? We developed a stochastic pharmacodynamic model to determine the probability of population extinction for a given treatment. We compared the success of combination treatments differing in the number of drugs (including monotherapy), drug ratios, or types of drugs (e.g. mode of action or pharmacodynamic characteristics). Our results show that combination therapy is almost always better in limiting the evolution of resistance than monotherapy, but exceptions exist for drugs with steep dose–response curves. We further find combinations of drugs with specific modes of action that are particularly beneficial, and that the mode of action, as well as the pharmacodynamic properties, affect the choice of drug ratio. Our mathematical analysis helps to understand where the benefits of a specific strategy arise from and how to optimize treatment settings for a potential clinical use.
