842 ISSN 0006-3509, Biophysics, 2017, Vol. 62, No. 5, pp. 842–847. © Pleiades Publishing, Inc., 2017. Original Russian Text © A.N. Sveshnikova, M.A. Panteleev, A.V. Dreval, T.P. Shestakova, O.S. Medvedev, O.A. Dreval, 2017, published in Biofizika, 2017, Vol. 62, No. 5, pp. 1023– 1029. Theoretical Evaluation of the Parameters of Glucose Metabolism on the Basis of Continuous Glycemia Monitoring Data Using Mathematical Modeling A. N. Sveshnikova a, b, *, M. A. Panteleev a, b , A. V. Dreval c , T. P. Shestakova c , O. S. Medvedev d , and O. A. Dreval c a Department of Physics, Lomonosov Moscow State University, Moscow, 119991 Russia b Federal Research and Clinical Center of Pediatric Hematology, Oncology and Immunology named after D. Rogachev, Moscow, 117197 Russia c Moscow Regional Research and Clinical Institute named after M.F. Vladimirskii, Moscow, 129110 Russia d Faculty of Basic Medicine, Moscow State University, Moscow, 119192 Russia *e-mail: endocrinolog-cab@yandex.ru Received February 28, 2017 Abstract⎯The aim of this paper is to construct a mathematical model that takes the main physiological parameters of blood-glucose regulation into account, in order to identify these parameters for an individual patient according to continuous glucose-monitoring data. The constructed mathematical model consists of six ordinary differential equations that describe the dynamics of changes in glucose concentrations, as well as insulin and anti-insulin factors in the blood. Estimation of the parameters of the equations was performed using an evolutionary programming method. The model predictions were fitted to the continuous glucose- monitoring data. As a result of the identification of the model parameters for two patients with type 1 diabetes mellitus, the estimated insulin secretion was close to zero and the estimated glucose utilization and insulin clearance were increased in comparison with the data for healthy donors. Here, we present a personalized model of the regulation of blood glucose, which can be used to predict the results of continuous glucose mon- itoring depending on modification of the prescribed glucose-lowering therapy. This approach can signifi- cantly reduce the number of iterations of the selection of medical hypoglycemic therapy and therefore increase the effectiveness of treatment according to glucose-monitoring data. Keywords: mathematical modeling, pharmacological modeling, type 1 diabetes, evolutionary programming, continuous glucose monitoring DOI: 10.1134/S0006350917050220 The construction of mechanistic mathematical models that describe processes in a complex biological system using differential equations has long been proven as an approach to gaining new knowledge on system functioning and to identify the pathways in order to influence a system. One of the common applications of mathematical biophysics is the con- struction of so-called pharmacokinetic/pharmacody- namic (PKPD) models in which a change in the con- centration of a pharmacological agent (drug) in the body is described with a differential equation. The right-hand side of the equation is a function of drug absorption and elimination from the body. Despite their seeming simplicity from the viewpoint of mathe- matics, PKPD models enable useful predictions on the doses and therapy regimens specific to the patient [1]. The aim of this study was to construct a mathemat- ical PKPD model that describes glucose homeostasis in the human body. The purpose of constructing this model is the ability to quickly estimate the parameters of glucose metabolism in the body of an individual patient with diabetes mellitus and predict regimens for optimal glucose-lowering therapy. Diabetes mellitus is a common disease that affects approximately 300 million people worldwide. The main diagnostic feature of the disease is an elevated blood-glucose level, with improper glucose-lowering therapy leading eventually to the development of vas- cular and other diabetic complications, which are a major cause of the mortality of these patients [2]. Dia- betes mellitus type 1 (DM1) is caused by the reduc- Abbreviations: PKPD-models, pharmacokinetic/pharmacody- namics models; DM1, diabetes mellitus 1 type; CGM, continu- ous glucose monitoring; AIFs, anti-insulin factors. BIOPHYSICS OF COMPLEX SYSTEMS