Evidence map›Paper›PMID 38550585›Full record

SynthesisFrontiers in immunology2024

Mathematical modeling in autoimmune diseases: from theory to clinical application.

Yaroslav Ugolkov, Antonina Nikitich, Cristina Leon, Gabriel Helmlinger, Kirill Peskov, Victor Sokolov, Alina Volkova

Erratum issuedOpen access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 5 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Yaroslav UgolkovResearch Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, Russia.
Antonina NikitichResearch Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, Russia.
Cristina LeonModeling and Simulation Decisions FZ - LLC, Dubai, United Arab Emirates.
Gabriel HelmlingerBiorchestra Co., Ltd., Cambridge, MA, United States.
Kirill PeskovResearch Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, Russia.
Victor SokolovMarchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, Russia.
Alina VolkovaMarchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, Russia.
Institute of Numerical Mathematics · RURussian Academy of Sciences · RUSirius University of Science and Technology · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Autoimmune DiseasesAnimalsAutoimmunityHumansModels, ImmunologicalModels, Theoreticalautoimmune diseasesimmune system modelingmathematical modelingmodel-informed drug developmentquantitative systems pharmacology

Identifiers

PMID38550585
PMCPMC10973044
OpenAlexW4392816304

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.