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Volume 18   Issue 1   Year 2023
Mathematical Modeling of Antihypertensive Therapy with Azilsartan Medoxomil on the Example of Clinical Data of a Real Patient

Borodulina A.D.1,2, Kutumova E.O.1,2,3,4, Lifshits G.I.5, Kolpakov F.A.2,3,4

1Novosibirsk State University, Novosibirsk, Russia
2BIOSOFT.RU Ltd., Novosibirsk, Russia
3Sirius University of Science and Technology, Sirius, Russia
4Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
5Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia

Abstract. Hypertension is a pathology caused by increased systolic and/or diastolic blood pressure. The disease can be controlled by various antihypertensive drugs. This study simulates the response of the human cardiovascular and renal systems to the action of the angiotensin II receptor blocker azilsartan medoxomil, taking into account dual combinations of this drug with the thiazide diuretic hydrochlorothiazide, the $upbeta$-blocker bisoprolol and the calcium channel blocker amlodipine. For this purpose, we consider an agent-based mathematical model of blood pressure regulation, previously developed in the BioUML software and including pharmacodynamic functions for hydrochlorothiazide, bisoprolol, and amlodipine. To simulate the effect of azilsartan, we extended the model with a dose-dependent constant that reduces the rate of binding of angiotensin II to AT1 receptors in accordance with the pharmacological action of the drug. The identification of this constant was carried out on the basis of known clinical trials of azilsartan. The model was tested on a population of virtual patients (equilibrium parametrizations of the model within the specified physiological constraints) with uncomplicated hypertension and uniformly distributed values of systolic/diastolic blood pressure and heart rate. Then, a methodological issue of adapting the model to the clinical parameters of a real patient was considered.

Key words: arterial hypertension, antihypertensive drugs, cardiovascular system, renal system, virtual patients, BioUML.

Table of Contents Original Article
Math. Biol. Bioinf.
doi: 10.17537/2023.18.228
published in Russian

Abstract (rus.)
Abstract (eng.)
Full text (rus., pdf)


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