{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Stracy M"],"funding":["European Research Council","Wellcome Trust","NIGMS NIH HHS"],"pagination":["889-894"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7612469"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["375(6583)"],"pubmed_abstract":["Treatment of bacterial infections currently focuses on choosing an antibiotic that matches a 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. Combining whole-genome sequencing of 1113 pre- and posttreatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections and 7365 wound infections, we found that treatment-induced emergence of resistance could be predicted and minimized at the individual-patient level. Emergence of resistance was common and driven not by de novo resistance evolution but by rapid reinfection with a different strain resistant to the prescribed antibiotic. As most infections are seeded from a patient's own microbiota, these resistance-gaining recurrences can be predicted using the patient's past infection history and minimized by machine learning-personalized antibiotic recommendations, offering a means to reduce the emergence and spread of resistant pathogens."],"journal":["Science (New York, N.Y.)"],"pubmed_title":["Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections."],"pmcid":["PMC7612469"],"funding_grant_id":["204684","281891","R01 GM081617","204684/Z/16/Z"],"pubmed_authors":["Wolf T","Kishony R","Katz R","Amer Y","Herzel E","Yelin I","Koren G","Chodick G","Stracy M","Foxman B","Shalev V","Parizade M","Rimler G","Kuint J","Snitser O"],"additional_accession":[]},"is_claimable":false,"name":"Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections.","description":"Treatment of bacterial infections currently focuses on choosing an antibiotic that matches a 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. Combining whole-genome sequencing of 1113 pre- and posttreatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections and 7365 wound infections, we found that treatment-induced emergence of resistance could be predicted and minimized at the individual-patient level. Emergence of resistance was common and driven not by de novo resistance evolution but by rapid reinfection with a different strain resistant to the prescribed antibiotic. As most infections are seeded from a patient's own microbiota, these resistance-gaining recurrences can be predicted using the patient's past infection history and minimized by machine learning-personalized antibiotic recommendations, offering a means to reduce the emergence and spread of resistant pathogens.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Feb","modification":"2025-04-04T02:24:01.364Z","creation":"2025-04-04T02:24:01.364Z"},"accession":"S-EPMC7612469","cross_references":{"pubmed":["35201862"],"doi":["10.1126/science.abg9868"]}}