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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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Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools. Int J Mol Sci 2022; 23:ijms232012560. [PMID: 36293410 PMCID: PMC9604366 DOI: 10.3390/ijms232012560] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/07/2022] [Accepted: 10/14/2022] [Indexed: 11/30/2022] Open
Abstract
Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.
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Kolpakov F, Akberdin I, Kiselev I, Kolmykov S, Kondrakhin Y, Kulyashov M, Kutumova E, Pintus S, Ryabova A, Sharipov R, Yevshin I, Zhatchenko S, Kel A. BioUML-towards a universal research platform. Nucleic Acids Res 2022; 50:W124-W131. [PMID: 35536253 PMCID: PMC9252820 DOI: 10.1093/nar/gkac286] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 04/04/2022] [Accepted: 04/13/2022] [Indexed: 12/12/2022] Open
Abstract
BioUML (https://www.biouml.org)—is a web-based integrated platform for systems biology and data analysis. It supports visual modelling and construction of hierarchical biological models that allow us to construct the most complex modular models of blood pressure regulation, skeletal muscle metabolism, COVID-19 epidemiology. BioUML has been integrated with git repositories where users can store their models and other data. We have also expanded the capabilities of BioUML for data analysis and visualization of biomedical data: (i) any programs and Jupyter kernels can be plugged into the BioUML platform using Docker technology; (ii) BioUML is integrated with the Galaxy and Galaxy Tool Shed; (iii) BioUML provides two-way integration with R and Python (Jupyter notebooks): scripts can be executed on the BioUML web pages, and BioUML functions can be called from scripts; (iv) using plug-in architecture, specialized viewers and editors can be added. For example, powerful genome browsers as well as viewers for molecular 3D structure are integrated in this way; (v) BioUML supports data analyses using workflows (own format, Galaxy, CWL, BPMN, nextFlow). Using these capabilities, we have initiated a new branch of the BioUML development—u-science—a universal scientific platform that can be configured for specific research requirements.
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Affiliation(s)
- Fedor Kolpakov
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation.,Budker Institute of Nuclear Physics SB RAS, Novosibirsk 630090, Russian Federation
| | - Ilya Akberdin
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation.,Novosibirsk State University, Novosibirsk 630090, Russian Federation
| | - Ilya Kiselev
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation.,Budker Institute of Nuclear Physics SB RAS, Novosibirsk 630090, Russian Federation
| | - Semyon Kolmykov
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation
| | - Yury Kondrakhin
- Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation.,Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation
| | | | - Elena Kutumova
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation
| | - Sergey Pintus
- Sirius University of Science and Technology, Sochi 354340, Russian Federation
| | - Anna Ryabova
- Sirius University of Science and Technology, Sochi 354340, Russian Federation
| | - Ruslan Sharipov
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation.,Novosibirsk State University, Novosibirsk 630090, Russian Federation
| | - Ivan Yevshin
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation
| | - Sergey Zhatchenko
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation
| | - Alexander Kel
- Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation.,geneXplain GmbH, Wolfenbüttel 38302, Germany
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