Mathematical models can predict the success or failure of antibiotic therapy and help improve patient cure rates

A team of Israeli biophysicists and doctors said recently that they have developed a mathematical model that can predict the success or failure of certain antibiotic therapies, thereby helping doctors accurately choose antibiotics for patients’ conditions and improve the cure rate of patients And survival rate. The research was published in the most recent issue of Science.

Professor Natalie Baraban of the Hebrew University of Jerusalem and Dr. Maskid Bar-Meir of the Shah Al-Zadek Medical Center in Jerusalem have demonstrated that treatment can be considered to be resistant by the rational selection or combination of existing antibiotics Medicinal bacteria. Balaban said they can predict the effectiveness of antibiotic combinations against different bacteria and help doctors choose treatment options for patients.

Bacteria can create defenses to deal with harmful factors in their living environment. A common strategy is “tolerance”, which means that they are dormant during antibiotic treatment. Because antibiotics can only find and kill targets that are growing, bacteria pass Dormant survives and eventually becomes resistant.

Baraban and PhD student Erit Levin-Riesman have studied controlled bacteria in the laboratory and developed mathematical models that describe, measure, and predict when bacteria will become resistant to specific antibiotics . At the same time, they observed that when bacteria became resistant to an antibiotic, they were more likely to develop resistance.

Subsequently, Balaban cooperated with Ba-Meir to analyze the bacterial samples of patients infected with methicillin-resistant Staphylococcus aureus at the medical center, and found that the patient’s pattern was very similar to their laboratory findings, that is, the bacteria first Tolerance develops, then resistance develops, and eventually antibiotic treatment fails.

The intermediate stage of bacteria’s “antibiotic resistance” from resistance to resistance lasts only a few days and cannot be detected in standard medical laboratories, but Balaban’s mathematical model can detect them. Balaban said she hopes to repeat the experiment in other hospitals and enough patients to prove the validity of the mathematical model. She also encouraged more medical centers to adopt tests developed by her team to quickly and easily detect whether patients’ bacteria are resistant to planned antibiotic treatments before they are managed. Depending on the patient’s bacterial characteristics, doctors can choose antibiotics that have a greater chance of killing bacteria in the patient’s body.

In the long run, Balaban believes that studying the evolutionary process of antibiotic resistance and drug resistance in bacteria may play a role in cancer treatment and guide cancer treatment because tumor cells may be resistant to chemotherapy in the first place And resistance, then resistance to other anticancer drugs.

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