Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics and treatment regimens

Abstract

The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: i) a single mutation, which provides a large MIC increase, or ii) multiple mutations, each conferring a small increase, which combine to yield high-level resistance. Using stochastic modeling we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the most efficacious drug type depends on the pharmacokinetic profile. Further, we demonstrate that, for resistance evolution in multiple steps, adaptive treatment, which only suppresses the bacterial population, is favored over aggressive treatment, which aims at eradication.

RNA polymerases from Low G+C Gram Positive Bacteria

“…Multiple research programs involving high-throughput small molecule screens, structure-based drug design, and small peptide antagonists have yielded promising results (62)(63)(64)(65)(66)(67)(68)(69)(70)(71). PPIs are an attractive target for drug development as simultaneous complementary mutations are required at two unlinked genetic loci to confer resistance to a compound whilst retaining the interaction, which has the potential to substantially reduce the rate at which resistance develops (72).…”