Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections

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 s...

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Bibliographic Details
Published in:Science (American Association for the Advancement of Science) 2022-02, Vol.375 (6583), p.889-894
Main Authors: Stracy, Mathew, Snitser, Olga, Yelin, Idan, Amer, Yara, Parizade, Miriam, Katz, Rachel, Rimler, Galit, Wolf, Tamar, Herzel, Esma, Koren, Gideon, Kuint, Jacob, Foxman, Betsy, Chodick, Gabriel, Shalev, Varda, Kishony, Roy
Format: Article
Language:English
Subjects:
Algorithms
Anti-Bacterial Agents - therapeutic use
Antibiotic resistance
Antibiotics
Bacteria - drug effects
Bacteria - genetics
Bacterial diseases
Bacterial Infections - drug therapy
Bacterial Infections - microbiology
Drug resistance
Drug Resistance, Bacterial
Escherichia coli Infections - drug therapy
Escherichia coli Infections - microbiology
Female
Humans
Infections
Machine Learning
Male
Microbial Sensitivity Tests
Microbiomes
Microbiota
Mutation
Patients
Reinfection - microbiology
Urinary tract
Urinary Tract Infections - drug therapy
Urinary Tract Infections - microbiology
Whole Genome Sequencing
Wound Infection - drug therapy
Wound Infection - microbiology
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Summary: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.
ISSN:0036-8075
1095-9203
DOI:10.1126/science.abg9868