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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 resistance benefit, or (ii) multiple mutations, each conferring a small benefit, 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 extent of this limitation depends on the combination of drug type and pharmacokinetic profile. Further, if multiple mutations are necessary, adaptive treatment, which only suppresses the bacterial population, delays treatment failure due to resistance for a longer time than aggressive treatment, which aims at eradication.<\/jats:p>","DOI":"10.7554\/elife.64116","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T20:00:14Z","timestamp":1621281614000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.64116","source":"Crossref","is-referenced-by-count":62,"title":["Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics, and treatment regimens"],"prefix":"10.7554","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7777-546X","authenticated-orcid":true,"given":"Claudia","family":"Igler","sequence":"first","affiliation":[{"name":"Institute of Integrative Biology, ETH Zurich, Zurich, 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