Scientists have developed a new technique for customizing or adapting antibiotics to the individual needs of patients. It reduces the problems associated with treatment compromised by an ineffective strain and develops antibiotic-resistant bacterial strains.
The new technology was developed by Varda Shalev, director of the Kahn-Sagol-Maccabi Research and Innovation Institute, in collaboration with researchers Roy Kishony and Idan Yelin of the Technion – Israel Institute of Technology.
One of the biggest challenges of modern medicine is antibiotic-resistant bacteria. The World Health Organization (WHO) has described it as a major threat to global health. This can affect people of any sex, age group and region.
The International Health Authority has also stated that antibiotic resistance can lead to increased mortality, higher medical costs and longer stays in the hospital.
It is estimated that more than two million people worldwide develop antibiotic-resistant infections each year. The agency also reported that nearly 23,000 people die from this type of infection.
Bacteria develop resistance to antibiotics for several reasons. One of them is the excessive use of antibiotics, which can lead to a loss of effectiveness. It should also be noted that some resistant bacteria develop due to infections caused by excessive use of antibiotics. This type of bacteria will likely be "treatment-resistant and lethal," the researchers said.
The development of resistant bacteria can be prevented by reducing the repeated use of antibiotics in medical treatments. Depending on the needs of patients, scientists could use artificial intelligence and patient data to develop specific antibiotics.
"It is now possible to predict the level of bacterial resistance of the bacteria causing infections.The demographics, such as age, sex, pregnancy, etc., are weighted with the degree of resistance. [which are]measured by the patient's past urine cultures as well as by his antecedents in the purchase of drugs, "said Israel Hayom Yelin.
For research, scientists analyzed more than 700,000 urine cultures. They then looked at urinary tract infections involving several types of bacteria, including E. coli, Klebsiella pneumonia and Proteus mirabilis.
The researchers then developed an algorithm based on antibiotic purchases over the past 10 years for more than five million cases. The algorithm provided treatment recommendations based on the antibiotic resistance of the infection.
The study "Personal Medical History Predicts Antibiotic Resistance in Urinary Tract Infections" was published last week in the journal Nature Medicine.