Why the study?
This study was conducted to simulate the cardiovascular and renal response to azilsartan medoxomil alone and in dual combinations with hydrochlorothiazide, bisoprolol, and amlodipine using mathematical modeling.
Can an agent-based mathematical model simulate the response of the cardiovascular and renal systems to azilsartan medoxomil and its combinations in patients with hypertension?
Can an agent-based mathematical model simulate the response of the cardiovascular and renal systems to azilsartan medoxomil and its combinations in patients with hypertension?
An agent-based mathematical model can simulate the physiological response to azilsartan medoxomil and its combinations, allowing adaptation to individual patient clinical parameters.
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Patient-specific azilsartan simulations are now feasible in agent-based models; leaves open validation against clinical outcomes.
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.
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Borodulina et al. (2023) studied this question.
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