Immunologists from the Institute of science Gulbenkian de Portugal developed a mathematical model that allows to use the treatment of bacterial infections, which would reduce the risk of formation of bacteria resistance to antibiotics and the emergence of superinfection. In the model also takes into account the role of the immune system of the patient. The study is published in the journal PLOS.
Antibiotics are necessary to treat severe bacterial infections, however, their improper and excessive use leads to the formation of resistance of microorganisms to the medicine. In the presence of resistance to standard methods of treatment become ineffective or not working at all. Since the new antibiotics appear quite slowly, scientists are trying to devise methods of efficient use of available funds.
Researchers have created a mathematical framework for the use of various methods: aggressive treatment with the maximum dose of the antibiotic, moderate therapies that combine low doses of the drug or a shorter duration of treatment, as well as short-term therapy. Modelling and computer analysis allowed us to compare the effectiveness of treatments with a fixed dose, but with different duration of treatment and procedures, where both the dose and duration of medication is determined by the patient’s condition. According to scientists, it also allows you to figure out how to minimize the risk of development of resistance.
A strong immune response may be grounds to reduce the intensity of drug therapy, and the model allows us to predict how. Taking into account such factors as the strength of immunity, a dose of antibiotics, duration of treatment and timing of the disease, it is possible to determine whether the appropriate treatment is successful, or bacteria can develop resistance. Also, the model helps to find out whether a possible relapse are sensitive to the antibiotic microorganisms, but not of resistant, as is usually assumed.
Scientists claim that such a personalized approach will become the basis of medicine. Mathematical models will play an important role in obtaining clinically important information and the development of effective methods of treatment.
