Determination of the parameters of the mathematical model of the immune response to HIV
- Authors: Surnin P.S.1, Shishlenin M.A.1, Bocharov G.A.2
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Affiliations:
- Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences
- Issue: Vol 71, No 1 (2025): Nonlocal and nonlinear problems
- Pages: 159-175
- Section: Articles
- URL: https://journals.rcsi.science/2413-3639/article/view/327846
- DOI: https://doi.org/10.22363/2413-3639-2025-71-1-159-175
- EDN: https://elibrary.ru/VHWIQO
- ID: 327846
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Abstract
Human immunodeficiency virus of type 1 (HIV) attacks the immune system and thereby weakens the defense against other infections and some types of cancer that the immune system of a healthy person can cope with. Despite the use of highly active antiretroviral therapy (HAART), there are no methods yet to completely eliminate HIV from the body of an infected person. However, due to the expansion of access to HIV prevention, diagnosis and treatment with HAART, HIV infection has moved into the category of controllable chronic diseases. Mathematical modeling methods are actively used to study the kinetic mechanisms of HIV pathogenesis and the development of personalized approaches to treatment based on combined immunotherapy. One of the central tasks of HIV infection modeling is to determine the individual parameters of the immune system response during the acute phase of HIV infection by solving inverse problems. To study the kinetics of the pathogenesis of HIV infection, a mathematical model of eight ordinary differential equations formulated by Bank et al. [5] was used. The system of equations of the model describes the change in the number of four subpopulations of CD4+ T cells and two types of CD8+ T cells. A feature of this model is the consideration of latently infected CD4+ T cells, which serve as the main reservoir of the viral population. The viral load on the human body is determined by the combination of populations of infectious and noninfectious viral particles. The inverse problem of parameter identification based on the data of the acute phase of HIV infection was studied. In particular, the identifiability of the parameters was studied and sensitivity analysis from the input data was performed. The inverse problem was reduced to a minimization problem using the evolutionary centers method.
About the authors
P. S. Surnin
Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences
Email: p.surnin@internet.ru
Novosibirsk, Russia
M. A. Shishlenin
Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences
Email: m.a.shishlenin@mail.ru
Novosibirsk, Russia
G. A. Bocharov
Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences
Author for correspondence.
Email: g.bocharov@inm.ras.ru
Moscow, Russia
References
- Кабанихин С.И., Шишленин М.А. Об использовании априорной информации в коэффициентных обратных задачах для гиперболических уравнений// Тр. ИММ УрО РАН. - 2012.- 18, № 1.-С. 147-164.
- Соболь И.М. Глобальные показатели чувствительности для изучения нелинейных математических моделей// Матем. модел. -2005.- 17, № 9.- С. 43-52.
- ВИЧ-инфекция в Российской Федерации на 31 декабря 2021 г.// Референс-Центр по мониторингу за ВИЧ и ВИЧ-ассоциированными инфекциями [электронный ресурс].- Режим доступа: http://www.hivrussia.info/wp-content/uploads/2022/03/Spravka-VICH-v-Rossii-na-31.12.2021 -g.pdf (дата обращения: 11.11.2024).
- Banks H., Banks J., Link K., Rosenheim J., Ross C., Tillman K. Model comparison tests to determine data information content// Appl. Math. Lett. -2015.-43.-С. 10-18.-doi: 10.1016/j.aml.2014.11.002.
- Banks H.T., Davidian M., Hu S., Kepler G.M., Rosenberg E.S. Modelling HIV immune response and validation with clinical data// J. Biol. Dyn. - 2008.- 2, № 4.- С. 357-385.-DOI: 10.1080/ 17513750701813184.
- Banks H., Flores K.B., Hu S., Rosenberg E., Buzon M., Yu X., Lichterfeld M. Immuno-modulatory strategies for reduction of HIV reservoir cells// J. Theor. Biol.-2015.-372.-С. 146-158.-doi: 10.1016/j.jtbi.2015.02.006.
- Banks H., Hu S., Rosenberg E. A dynamical modeling approach for analysis of longitudinal clinical trials in the presence of missing endpoints// Appl. Math. Lett. -2017.-63.-С. 109-117.-doi: 10.1016/j.aml.2016.07.002.
- Bocharov G., Chereshnev V., Gainova I., Bazhan S., Bachmetyev B., Argilaguet J., Martinez J., Meyerhans A. Human immunodeficiency virus infection: from biological observations to mechanistic mathematical modelling// Math. Model. Nat. Phenom. -2012.- 7, № 5.-С. 78-104.-DOI: 10.1051/ mmnp/20127507.
- Gandhi R.T., Bedimo R., Hoy J.F., Landovitz R.J., Smith D.M. и др. Antiretroviral drugs for treatment and prevention of HIV infection in adults: 2022 recommendations of the international antiviral society- USA panel// JAMA. -2023.- 329, № 1.-С. 63-84.- doi: 10.1001/jama.2022.22246.
- Jenner A.L., Aogo R.A., Davis C.L., Smith A.M., Craig M. Leveraging computational modeling to understand infectious diseases// Curr. Pathobiol. Rep.- 2020.-8, № 4.-С. 149-161.- DOI: 10.1007/ s40139-020-00213-x.
- Kabanikhin S., Shishlenin M. Quasi-solution in inverse coefficient problems// J. Inverse Ill-Posed Probl.- 2008.-16, № 7.- С. 317-357.-doi: 10.1515/jiip.2008.043.
- Kabanikhin S., Shishlenin M. Theory and numerical methods for solving inverse and illposed problems// J. Inverse Ill-Posed Probl. -2019.- 27, № 3.- С. 453-456.-doi: 10.1515/jiip-2019-5001.
- Kazer S.W., Aicher T.P., Muema D.M., Carroll S.L., Ordovas-Montanes J. и др. Integrated single-cell analysis of multicellular immune dynamics during hyperacute HIV-1 infection// Nat. Med. -2020.- 26, № 4. -С. 511-518.-doi: 10.1038/s41591-020-0799-2.
- Landovitz R.J., Scott H., Deeks S.G. Prevention, treatment and cure of HIV infection// Nat. Rev. Microbiol.- 2023.-21.-С. 657-670.-doi: 10.1038/s41579-023-00914-1.
- Mej´ıa-de Dios J.-A., Mezura-Montes E. A new evolutionary optimization method based on center of mass: performance and safety management// В сб.: «Decision Science in Action Theory and Applications of Modern Decision Analytic Optimisation».-Singapore: Springer, 2019.- С. 65-74.-doi: 10.1007/978981-13-0860-4_6.
- Perelson A.S., Ribeiro R.M. Introduction to modeling viral infections and immunity// Immunol. Rev.- 2018.-285.- С. 5-8.-doi: 10.1111/imr.12700.
- Tsitouras C. Runge-Kutta pairs of order 5(4) satisfying only the first column simplifying assumption// Comput. Math. Appl. -2011.- 62, № 2. -С. 770-775.-doi: 10.1016/j.camwa.2011.06.002.
- Vemparala B., Chowdhury S., Guedj J., Dixit N.M. Modelling HIV-1 control and remission// npj Syst. Biol. Appl. - 2024.- 10, № 1.- 84.- doi: 10.1038/s41540-024-00407-8.
- Wendelsdorf K., Dean G., Hu S., Nordone S., Banks H. Host immune responses that promote initial HIV spread// J. Theor. Biol.-2011.-289.- С. 17-35.-doi: 10.1016/j.jtbi.2011.08.012.
- Zheltkova V., Argilaguet J., Peligero C., Bocharov G., Meyerhans A. Prediction of PD-L1 inhibition effects for HIV-infected individuals// PLOS Computational Biology.- 2019.- 15, № 11.-e1007401.- doi: 10.1371/journal.pcbi.1007401.
- HIV and AIDS// World Health Organization [электронный ресурс]. -Режим доступа: https://www. who.int/news-room/fact-sheets/detail/hiv-aids (дата обращения: 11.11.2024).
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