Researchers from the Technion and Maccabi Health Services have developed an algorithm that enables more personalized and effective antibiotic treatment for patients. The algorithm is the first use of personalized medicine in the field of antibiotics.
According to the study, the algorithm may reduce by 40 percent the likelihood that a patient will receive antibiotics that are not suitable for him. As a result, antibiotics lose their effectiveness, as bacterial infections will become resistant to antibiotics. Infections that are now considered moderate and not dangerous will become treatment resistant and deadly. One of the methods that speed up the development of antibiotic resistance is the common use of broad-range antibiotics. Meaning, drugs directed to kill a wide range of bacteria.
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The study analyzed more than five million cases of antibiotics use over 10 years and assessment of antibiotic resistance in more than 700,000 urine cultures.
The new technique saving unnecessary exposure to antibiotics that contribute to the development of resistant bacteria that the World Health Organization (WHO) has defined as a “global health emergency.”
A Technion researcher, Prof. Roy Kishony, one of the leading experts in the field of antibiotic resistance, developed methods for genetic mapping of bacterial resistance to antibiotics. His technology predicts the tolerance, of each bacterium to various antibiotics in the present and, even to the level of resistance that bacteria may develop in the future.
These kinds of infections involve various bacteria, including Klebsiella pneumoniae, E. coli, and Proteus mirabilis. The system that was developed may help the doctor choose the optimal antibiotic for treating urinary tract infections.
In their study, the researchers focused on infection in the urinary tract, which affects more than half of women at some time during their lives.
The team found that antibiotic resistance levels were different for each woman. They also found that certain antibiotics work in one case but not in another.
“It is now possible to computationally predict the level of bacterial resistance for infection-causing bacteria,” said the Technion researcher Dr. Idan Yelin. “This is done by weighting of demographic data, including age, gender, pregnancy or retirement home residence, together with levels of resistance measured in the patient’s previous urine cultures as well as their drug purchase history.”
A sophisticated algorithm was able to find a clear link among the various data and thus predict the level of antibiotic resistance for each infection and provide a recommendation for the best type of antibiotics. The researchers found that the use of the technology could reduce the likelihood of choosing the wrong medication by about 40%. Therefore, they estimate that this system will contribute greatly to the global effort to delay the “resistance epidemic.”
The technology, which was presented in a study published in Nature Medicine, was made possible by a unique collaboration between the KSM Institute of Maccabi, headed by Professor Varda Shalev, and Technion researchers Professor Roy Kishony and Dr. Idan Yelin.