EvoComb · Evolution under sequential antibiotic combinations: integrating clinical data into models and experiments to tackle resistance

The looming crisis of antibiotic resistance makes it imperative to better understand how treatments influence the dynamics of resistance evolution. Clinical treatments often combine multiple antibiotics aiming to increase treatment efficacy and minimise resistance evolution. In addition, chronic infections typically require long-term treatments that combine multiple antibiotics and antibiotic-free periods sequentially. Although treatment selection pressure is a fundamental determinant of resistance evolution, quantitative, systematic studies investigating the impact of treatment schemes on evolutionary dynamics in clinical treatments are lacking.EvoComb proposes a multidisciplinary, hybrid approach that combines theoretical models, evolution experiments, and clinical data from patient treatments to elucidate the effects of treatment schemes that combine antibiotics on the evolution of antibiotic resistance. I will develop population genetic models and integrate them with clinical data to design, perform and interpret controlled evolution experiments with clinically relevant bacterial populations. This kind of approach has proven to be an effective tool for predicting and disentangling the effects of the different factors driving resistance evolution. I will also phenotypically characterise bacterial populations at different stages of evolution both in the lab and in the clinic, by quantitatively determining their drug responses. Using a dedicated robotic platform, I will address the reproducibility of results within evolutionary stochasticity via high-throughput experiments. Taken together, the outcomes of this project will substantially advance our understanding of the dynamics of resistance evolution during real-world treatments, with broad biomedical applications.