Machine-learning antibiotic prescriptions can help minimize spread of bacterial resistance

You enticed me, Hashem, and I was enticed; You overpowered me and You prevailed. I have become a constant laughingstock. Everyone jeers at me. Jeremiah 20:7




(the israel bible)

February 25, 2022

3 min read

The ancient Egyptians used to apply a poultice of moldy bread to infected wounds – so it seems they discovered the potential of antibiotics thousands of years ago. It wasn’t until 1928 that penicillin – the first true antibiotic – was discovered by bacteriology Prof. Alexander Fleming, at St. Mary’s Hospital in London.

Antibiotics are vital to curing bacterial infections. On the other, due to overuse, bacteria resistant to antibiotics have appeared and proliferated, so many of these drugs are being useless as time passe. 

Now, using genomic sequencing techniques and machine-learning (artificial intelligence) analysis of patient records, Israeli researchers have developed an antibiotic-prescribing algorithm that reduces the risk of antibiotic resistance by half.   

Professor Roy Kishony-Photo credit to Nitzan Zohar for the Technion
Dr. Mathew Stracy-Photo credit to Nitzan Zohar for the Technion

The paper, just published today in the prestigious journal Science under the title “Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections,”  is a collaboration between research group of Prof. Roy Kishony at Haifa’s Technion-Israel Institute of Technology Faculty of Biology and the Henry and Marilyn Taub Faculty of Computer Science. Professors Varda Shalev, Gabriel Chodick and Jacob Kuint at Maccabi KSM Research and Innovation Center, which is headed by Dr. Tal Patalon, collaborated with them on the research. 

Focusing on two very common bacterial infections – urinary tract infections and wound infections – the paper describes how each patient’s past infection history can be used to choose the best antibiotic to prescribe them to reduce the chances of antibiotic resistance emerging. 

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. “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,” said Kishony.  

Combining whole-genome sequencing of 1,113 pre- and posttreatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections and 7,365 wound infections, they found that treatment-induced emergence of resistance could be predicted and minimized at the individual-patient level.

The key to the success of the approach was understanding that the emergence of antibiotic resistance could be predicted in individual patients’ infections. Bacteria can evolve by randomly acquiring mutations that makes them resistant, but the randomness of the process makes it hard to predict and to avoid, but the researchers discovered that in most patients’ infections resistance was not acquired by random mutations. 

As most infections are seeded from a patient’s own gut bacteria (microbiome), they noted, 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.

The researchers turned these findings into an advantage: they proposed matching an antibiotic not only to the susceptibility of the bacteria causing the patient’s current infection, but also to the bacteria in their microbiome that could replace it.

“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’ explained Dr. Mathew Stracy, the paper’s first author who is a Technion biochemist who is also at the University of Oxford in the UK.  Department of Biochemistry, University of Oxford, Oxford, UK.  “Using this data together with the patient’s demographics like age and gender allowed us to develop the algorithm.”

“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,” concluded Patalon. 

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