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.

PubMed

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.