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Updated: Jun 29, 2025

Induction and Diverse Assessment Indicators of Experimental Autoimmune Encephalomyelitis
Published on: September 9, 2022
Mathematical modeling in autoimmune diseases: from theory to clinical application
Yaroslav Ugolkov1,2, Antonina Nikitich1,2, Cristina Leon3
1Research Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, Russia.
Abstract:
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.
Insights
Mathematical modeling aids autoimmune disease drug development by quantitatively describing immune responses. This review consolidates knowledge from 38 models, identifying common approaches and future research directions for novel therapeutics.
Area of Science:
- Systems Biology and Immunology
- Computational Medicine
- Pharmacometrics
Background:
- Autoimmune diseases present complex R&D challenges due to intricate pathogenesis and rising global prevalence.
- Limited targeted therapies necessitate innovative approaches to improve drug development success rates.
- Mathematical modeling offers a crucial tool for informed decision-making in R&D programs for autoimmune conditions.
Purpose of the Study:
- To systematically review and analyze mathematical models of autoimmune diseases, focusing on mechanistic descriptions of the immune system.
- To consolidate existing quantitative knowledge on autoimmune processes through model analysis.
- To identify future directions for model-based research in autoimmune disease therapeutics.
Main Methods:
- Conducted a systematic literature review to identify relevant mathematical models of autoimmune diseases.
- Analyzed 38 identified models based on quantitative data, mathematical methods, and mechanistic granularity.
- Categorized models by disease type, mathematical approach (e.g., ODEs, PDEs, Boolean networks), and described immune components.
Main Results:
- Identified 38 mathematical models for 13 autoimmune conditions, with notable focus on inflammatory bowel disease, multiple sclerosis, and lupus.
- The majority (≥70%) of models employed nonlinear systems of ordinary differential equations.
- Commonly modeled immune components included T-cell responses, cytokine signaling, and macrophage involvement.
Conclusions:
- Mathematical models provide valuable quantitative insights into autoimmune disease mechanisms and progression.
- Despite disease diversity, models often focus on similar core immune system components.
- Further model-based analyses hold significant potential for advancing the development of novel autoimmune therapeutics.

