Mathematical modeling in autoimmune diseases: from theory to clinical application
The research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-03-01
|
| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1371620/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832581901543538688 |
|---|---|
| author | Yaroslav Ugolkov Yaroslav Ugolkov Yaroslav Ugolkov Antonina Nikitich Antonina Nikitich Cristina Leon Gabriel Helmlinger Kirill Peskov Kirill Peskov Kirill Peskov Kirill Peskov Victor Sokolov Victor Sokolov Alina Volkova Alina Volkova |
| author_facet | Yaroslav Ugolkov Yaroslav Ugolkov Yaroslav Ugolkov Antonina Nikitich Antonina Nikitich Cristina Leon Gabriel Helmlinger Kirill Peskov Kirill Peskov Kirill Peskov Kirill Peskov Victor Sokolov Victor Sokolov Alina Volkova Alina Volkova |
| author_sort | Yaroslav Ugolkov |
| collection | DOAJ |
| description | The research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence of autoimmune conditions in the global population continues to rise. Mathematical modeling of biological systems is an essential tool which may be applied in support of decision-making across R&D drug programs to improve the probability of success in the development of novel medicines. Over the past decades, multiple models of autoimmune diseases have been developed. Models differ in the spectra of quantitative data used in their development and mathematical methods, as well as in the level of “mechanistic granularity” chosen to describe the underlying biology. Yet, all models strive towards the same goal: to quantitatively describe various aspects of the immune response. The aim of this review was to conduct a systematic review and analysis of mathematical models of autoimmune diseases focused on the mechanistic description of the immune system, to consolidate existing quantitative knowledge on autoimmune processes, and to outline potential directions of interest for future model-based analyses. Following a systematic literature review, 38 models describing the onset, progression, and/or the effect of treatment in 13 systemic and organ-specific autoimmune conditions were identified, most models developed for inflammatory bowel disease, multiple sclerosis, and lupus (5 models each). ≥70% of the models were developed as nonlinear systems of ordinary differential equations, others – as partial differential equations, integro-differential equations, Boolean networks, or probabilistic models. Despite covering a relatively wide range of diseases, most models described the same components of the immune system, such as T-cell response, cytokine influence, or the involvement of macrophages in autoimmune processes. All models were thoroughly analyzed with an emphasis on assumptions, limitations, and their potential applications in the development of novel medicines. |
| format | Article |
| id | doaj-art-73c9785dc2df4d8898dd4013a1d53edf |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-73c9785dc2df4d8898dd4013a1d53edf2025-01-30T08:47:36ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-03-011510.3389/fimmu.2024.13716201371620Mathematical modeling in autoimmune diseases: from theory to clinical applicationYaroslav Ugolkov0Yaroslav Ugolkov1Yaroslav Ugolkov2Antonina Nikitich3Antonina Nikitich4Cristina Leon5Gabriel Helmlinger6Kirill Peskov7Kirill Peskov8Kirill Peskov9Kirill Peskov10Victor Sokolov11Victor Sokolov12Alina Volkova13Alina Volkova14Research Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, RussiaMarchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, RussiaFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, RussiaResearch Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, RussiaMarchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, RussiaModeling and Simulation Decisions FZ - LLC, Dubai, United Arab EmiratesBiorchestra Co., Ltd., Cambridge, MA, United StatesResearch Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, RussiaMarchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, RussiaModeling and Simulation Decisions FZ - LLC, Dubai, United Arab EmiratesSirius University of Science and Technology, Sirius, RussiaMarchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, RussiaModeling and Simulation Decisions FZ - LLC, Dubai, United Arab EmiratesMarchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, RussiaModeling and Simulation Decisions FZ - LLC, Dubai, United Arab EmiratesThe research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence of autoimmune conditions in the global population continues to rise. Mathematical modeling of biological systems is an essential tool which may be applied in support of decision-making across R&D drug programs to improve the probability of success in the development of novel medicines. Over the past decades, multiple models of autoimmune diseases have been developed. Models differ in the spectra of quantitative data used in their development and mathematical methods, as well as in the level of “mechanistic granularity” chosen to describe the underlying biology. Yet, all models strive towards the same goal: to quantitatively describe various aspects of the immune response. The aim of this review was to conduct a systematic review and analysis of mathematical models of autoimmune diseases focused on the mechanistic description of the immune system, to consolidate existing quantitative knowledge on autoimmune processes, and to outline potential directions of interest for future model-based analyses. Following a systematic literature review, 38 models describing the onset, progression, and/or the effect of treatment in 13 systemic and organ-specific autoimmune conditions were identified, most models developed for inflammatory bowel disease, multiple sclerosis, and lupus (5 models each). ≥70% of the models were developed as nonlinear systems of ordinary differential equations, others – as partial differential equations, integro-differential equations, Boolean networks, or probabilistic models. Despite covering a relatively wide range of diseases, most models described the same components of the immune system, such as T-cell response, cytokine influence, or the involvement of macrophages in autoimmune processes. All models were thoroughly analyzed with an emphasis on assumptions, limitations, and their potential applications in the development of novel medicines.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1371620/fullautoimmune diseasesmathematical modelingquantitative systems pharmacologymodel-informed drug developmentimmune system modeling |
| spellingShingle | Yaroslav Ugolkov Yaroslav Ugolkov Yaroslav Ugolkov Antonina Nikitich Antonina Nikitich Cristina Leon Gabriel Helmlinger Kirill Peskov Kirill Peskov Kirill Peskov Kirill Peskov Victor Sokolov Victor Sokolov Alina Volkova Alina Volkova Mathematical modeling in autoimmune diseases: from theory to clinical application Frontiers in Immunology autoimmune diseases mathematical modeling quantitative systems pharmacology model-informed drug development immune system modeling |
| title | Mathematical modeling in autoimmune diseases: from theory to clinical application |
| title_full | Mathematical modeling in autoimmune diseases: from theory to clinical application |
| title_fullStr | Mathematical modeling in autoimmune diseases: from theory to clinical application |
| title_full_unstemmed | Mathematical modeling in autoimmune diseases: from theory to clinical application |
| title_short | Mathematical modeling in autoimmune diseases: from theory to clinical application |
| title_sort | mathematical modeling in autoimmune diseases from theory to clinical application |
| topic | autoimmune diseases mathematical modeling quantitative systems pharmacology model-informed drug development immune system modeling |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1371620/full |
| work_keys_str_mv | AT yaroslavugolkov mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT yaroslavugolkov mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT yaroslavugolkov mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT antoninanikitich mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT antoninanikitich mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT cristinaleon mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT gabrielhelmlinger mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT kirillpeskov mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT kirillpeskov mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT kirillpeskov mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT kirillpeskov mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT victorsokolov mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT victorsokolov mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT alinavolkova mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication AT alinavolkova mathematicalmodelinginautoimmunediseasesfromtheorytoclinicalapplication |