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Kutumova E, Kovaleva A, Sharipov R, Lifshits G, Kolpakov F. Mathematical modelling of the influence of ACE I/D polymorphism on blood pressure and antihypertensive therapy. Heliyon 2024; 10:e29988. [PMID: 38707445 PMCID: PMC11068647 DOI: 10.1016/j.heliyon.2024.e29988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/29/2024] [Accepted: 04/18/2024] [Indexed: 05/07/2024] Open
Abstract
The angiotensin-converting enzyme (ACE) gene (ACE) insertion/deletion (I/D) polymorphism raises the possibility of personalising ACE inhibitor therapy to optimise its efficiency and reduce side effects in genetically distinct subgroups. However, the extent of its influence among these subgroups is unknown. Therefore, we extended our computational model of blood pressure regulation to investigate the effect of the ACE I/D polymorphism on haemodynamic parameters in humans undergoing antihypertensive therapy. The model showed that the dependence of blood pressure on serum ACE activity is a function of saturation and therefore, the lack of association between ACE I/D and blood pressure levels may be due to high ACE activity in specific populations. Additionally, in an extended model simulating the effects of different classes of antihypertensive drugs, we explored the relationship between ACE I/D and the efficacy of inhibitors of the renin-angiotensin-aldosterone system. The model predicted that the response of cardiovascular and renal parameters to treatment directly depends on ACE activity. However, significant differences in parameter changes were observed only between groups with high and low ACE levels, while different ACE I/D genotypes within the same group had similar changes in absolute values. We conclude that a single genetic variant is responsible for only a small fraction of heredity in treatment success and its predictive value is limited.
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Affiliation(s)
- Elena Kutumova
- Department of Computational Biology, Sirius University of Science and Technology, Sirius, Krasnodar region, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
- Biosoft.Ru, Ltd., Novosibirsk, Russia
| | - Anna Kovaleva
- Laboratory for Personalized Medicine, Center of New Medical Technologies, Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia
| | - Ruslan Sharipov
- 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, Sirius, Krasnodar region, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
- Biosoft.Ru, Ltd., Novosibirsk, Russia
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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] [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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Abstract
Blood pressure is a way of describing the end result of changes in cardiac output, intravascular volume and peripheral resistance. It has certain advantages in that it is a reproducible and easily measured parameter, but in itself, it offers only a limited understanding of the underlying haemodynamics. In pregnancy, profound haemodynamic changes occur and in hypertensive diseases of pregnancy defining a condition by blood pressure alone risks missing the underlying cause. Partly, this has been a problem of ascribing the cause of hypertensive syndromes to the placenta which has inhibited rigorous research into other possible causes of haemodynamic dysfunction. It is becoming increasingly evident that hypertension in pregnancy may be associated with primarily high cardiac output or high peripheral resistance. A knowledge of the underlying type of hypertension may allow more rational treatment of these conditions in pregnancy rather than therapeutic attempts at controlling blood pressure by any means possible as an end in itself.
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Affiliation(s)
- Christoph Lees
- Imperial College London, London, UK.
- Centre for Fetal Care, Queen Charlotte's and Chelsea Hospital, Imperial Healthcare NHS Trust, Du Cane Road, London, W12 0HS, UK.
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy.
| | - Enrico Ferrazzi
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
- Department of Woman, Mother and Neonate, Buzzi Children Hospital, University of Milan, Milan, Italy
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