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Kutumova E, Kiselev I, Sharipov R, Lifshits G, Kolpakov F. Mathematical modeling of antihypertensive therapy. Front Physiol 2022; 13:1070115. [PMID: 36589434 PMCID: PMC9795234 DOI: 10.3389/fphys.2022.1070115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022] Open
Abstract
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.
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Affiliation(s)
- Elena Kutumova
- Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia,Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia,Biosoft.Ru, Ltd., Novosibirsk, Russia,*Correspondence: Elena Kutumova,
| | - Ilya Kiselev
- Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia,Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia,Biosoft.Ru, Ltd., Novosibirsk, Russia
| | - Ruslan Sharipov
- Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia,Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia,Biosoft.Ru, Ltd., Novosibirsk, Russia,Specialized Educational Scientific Center, Novosibirsk State University, Novosibirsk, Russia
| | - Galina Lifshits
- Laboratory for Personalized Medicine, Center of New Medical Technologies, Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia
| | - Fedor Kolpakov
- Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia,Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia,Biosoft.Ru, Ltd., Novosibirsk, Russia
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