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Personalised medicine for combatting antibiotic resistance

Antibiotics are a double-edged sword: while they help clear an ongoing infection, they can also select for drug-resistance, making future infections harder to treat. Currently, treatment strategies focus on choosing an antibiotic that matches the present pathogen’s susceptibility, with less attention paid to the risk that even susceptibility-matched treatments can fail as a result of resistance emerging in response to treatment. Focusing on urinary tract infections and wound infections, we found that treatment-induced emergence of resistance was common and driven not by de novo evolution but by rapid reinfection with a resistant strain pre-existing in the patient’s own microbiota. Resistance-gaining recurrences could therefore be predicted using the patient’s past infection history and minimized by machine learning–personalized antibiotic recommendations, offering a promising route to reduce the emergence and spread of resistant pathogens.