TY - JOUR

T1 - Mathematical modeling of antihypertensive therapy

AU - Kutumova, Elena

AU - Kiselev, Ilya

AU - Sharipov, Ruslan

AU - Lifshits, Galina

AU - Kolpakov, Fedor

N1 - Funding: This study was supported by the Sirius University, project CMB-RND-2123. Copyright © 2022 Kutumova, Kiselev, Sharipov, Lifshits and Kolpakov.

PY - 2022/12

Y1 - 2022/12

N2 - Hypertension is a multifactorial disease arising from complex pathophysiological pathways. Individual characteristics of patients result in different responses to various classes of antihypertensive medications. Therefore, evaluating the efficacy of therapy based on in silico predictions is an important task. This study is a continuation of research on the modular agent-based model of the cardiovascular and renal systems (presented in the previously published article). In the current work, we included in the model equations simulating the response to antihypertensive therapies with different mechanisms of action. For this, we used the pharmacodynamic effects of the angiotensin II receptor blocker losartan, the calcium channel blocker amlodipine, the angiotensin-converting enzyme inhibitor enalapril, the direct renin inhibitor aliskiren, the thiazide diuretic hydrochlorothiazide, and the β-blocker bisoprolol. We fitted therapy parameters based on known clinical trials for all considered medications, and then tested the model's ability to show reasonable dynamics (expected by clinical observations) after treatment with individual drugs and their dual combinations in a group of virtual patients with hypertension. The extended model paves the way for the next step in personalized medicine that is adapting the model parameters to a real patient and predicting his response to antihypertensive therapy. The model is implemented in the BioUML software and is available at https://gitlab.sirius-web.org/virtual-patient/antihypertensive-treatment-modeling.

AB - Hypertension is a multifactorial disease arising from complex pathophysiological pathways. Individual characteristics of patients result in different responses to various classes of antihypertensive medications. Therefore, evaluating the efficacy of therapy based on in silico predictions is an important task. This study is a continuation of research on the modular agent-based model of the cardiovascular and renal systems (presented in the previously published article). In the current work, we included in the model equations simulating the response to antihypertensive therapies with different mechanisms of action. For this, we used the pharmacodynamic effects of the angiotensin II receptor blocker losartan, the calcium channel blocker amlodipine, the angiotensin-converting enzyme inhibitor enalapril, the direct renin inhibitor aliskiren, the thiazide diuretic hydrochlorothiazide, and the β-blocker bisoprolol. We fitted therapy parameters based on known clinical trials for all considered medications, and then tested the model's ability to show reasonable dynamics (expected by clinical observations) after treatment with individual drugs and their dual combinations in a group of virtual patients with hypertension. The extended model paves the way for the next step in personalized medicine that is adapting the model parameters to a real patient and predicting his response to antihypertensive therapy. The model is implemented in the BioUML software and is available at https://gitlab.sirius-web.org/virtual-patient/antihypertensive-treatment-modeling.

KW - agent-based modular model

KW - antihypertensive therapy

KW - blood pressure regulation

KW - cardiovascular system

KW - mathematical modeling

KW - renal system

UR - https://www.mendeley.com/catalogue/ee31695a-b5ae-3626-b396-c201692a1e7f/

U2 - 10.3389/fphys.2022.1070115

DO - 10.3389/fphys.2022.1070115

M3 - Article

C2 - 36589434

VL - 13

SP - 1070115

JO - Frontiers in Physiology

JF - Frontiers in Physiology

SN - 1664-042X

ER -