Steps to personalized therapy of multiple sclerosis: predicting safety of treatment using mathematical modeling

AIM: To construct a mathematical model capable of predicting the drug safety of a patient receiving multiple sclerosis disease modifying drugs (DMD), on a model of flu-like syndrome (FLS). MATERIAL AND METHODS: The study included 457 patients with multiple sclerosis (MS), aged from 18 to 68 years, mean 38.79 years, the mean duration of disease 122.58 months. All patients received first-line injections drug (interferon-beta). The sample included data from a three-year prospective dynamic observation with a frequency of observation of 1 every 6 months, with only the data of those examinations for which the presence or absence of FLS was known for the next 6 months (1203 cases). At the first step, the frequency of factors in the compared groups using the W Wald-Wolkovitz test, then the prognostic coefficients (PC) and the Kulbak informativity coefficient (CI) were calculated for each factor gradation. To determine the predictive ability of signs, the Spearman's R criterion was used. At the second step, a model of a two-layer neural network was constructed based on the data obtained. RESULTS: A simple static model and algorithm were developed to assess the risks of the onset and persistence of FLS during the next 6 months of interferon beta therapy. An attempt was also made to create an active model using neural network technology. Both models showed good sensitivity and specificity - 81.2% and 80.6% for the neural network, and 73.4 and 71.6% for the static model. CONCLUSION: Using of these algorithms allows to significantly increase the possibility of predicting the occurrence of AE at the time of drug prescribing. From the mathematical point of view, for the first time the mechanism and possibilities of using a neural network in conditions of incomplete initial information were determined.