Predicting evolutionary dynamics of multi-drug resistance

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There is an urgent need to develop novel approaches to halt the evolution and spread of antimicrobial resistance. Combination therapies (multiple drugs given as a single prescription) are promising, both for preventing resistance and for optimising treatments for specific infections. However, recent experimental work has shown that combination therapies can select for multi-drug resistance in conditions experienced by natural microbial populations (e.g. temporally-varying drug concentrations and elevated mutation rates).To establish whether combination therapies are a viable strategy, we need predictive models for how well drug combinations prevent resistance under real-world conditions where antibiotics are present (including infection and agriculture). A combined modelling and experimental approach is required because testing more than a handful of drugs is a considerable logistical challenge-5 antibiotics screened at 10 doses requires nearly 10 million growth assays, beyond the limits of high-throughput technologies. However, models need to account for the basic biology of microbial growth under temporally-varying antibiotic levels, which requires experimental measurement. Model predictions will be validated by experimentally exposing bacterial populations to the best and worst identified combinations to see if multi-drug resistance evolves. This work is crucial for establishing combination therapies as a viable solution to the antibiotic resistance crisis.