Antibiotic resistance makes treating bacterial infections difficult. Therefore, recent years have seen a worrying increase in the levels of antibiotic resistance of many bacterial infections.
Clinical treatment of infections focuses on correctly matching an antibiotic to the resistance profile of the pathogen. Still, even such correctly matched treatments can fail as resistance can emergence during the treatment itself.
Scientists have developed an antibiotic prescribing algorithm using genomic sequencing techniques and machine learning analysis of patient records. The algorithm is expected to minimize the resistance spread.
This study mainly focused on two very common bacterial infections, urinary tract infections, and wound infections. They determined each patient’s past infection history to choose the best antibiotic to prescribe.
Professor Roy Kishony from the Technion — Israel Institute of Technology Faculty of Biology said, “We wanted to understand how antibiotic resistance emerges during treatment and find ways to better tailor antibiotic treatment for each patient to not only correctly match the patient’s current infection susceptibility, but also to minimize their risk of infection recurrence and gain of resistance to treatment.”
The key to the approach’s success was understanding the fact that the emergence of antibiotic resistance could be predicted. Predicting antibiotic resistance is difficult because bacteria can evolve by randomly acquiring mutations that make them resistant.
However, scientists found that random mutations did not acquire most patients’ infections resistance. Instead, it was driven by existing resistant bacteria from the patient’s microbiome.
Based on the outcomes, scientists proposed matching an antibiotic to the susceptibility of the bacteria causing the patient’s current infection and the bacteria in their microbiome that could replace it.
Dr. Mathew Stracy, the first author of the paper, said, “We found that the antibiotic susceptibility of the patient’s past infections could be used to predict their risk of returning with a resistant infection following antibiotic treatment.”
“Using this data, together with the patient’s demographics like age and gender, allowed us to develop the algorithm.”
Dr. Tal Patalon said, “I hope to see the algorithm applied at the point of care, providing doctors with better tools to personalize antibiotic treatments to improve treatment and minimize the spread of resistance.”
Mathew Stracy, Olga Snitser et al. Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections. Science, 2022; 375 (6583): 889 DOI: 10.1126/science.abg9868