Mathematical modelling of autoimmune myocarditis and the effects of immune checkpoint inhibitors

Vegt, Solveig A. van der; Polonchuk, Liudmila; Wang, Ken; Waters, Sarah L.; Baker, Ruth E.

doi:10.1016/j.jtbi.2021.111002

Cited by 8 publications

(5 citation statements)

References 81 publications

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“…While rare, autoimmune myocarditis can be caused as a side effect incurred by treatment using immune checkpoint inhibitors – a large and effective class of anti-cancer therapies ( 113 , 114 ). Building up on the historical research of the immune system, Van der Vegt and colleagues pioneered the modeling of checkpoint-induced autoimmune reaction, in an effort to provide an instrument that helps in balancing the benefits and risks of this type of treatment ( 62 ). The core of the developed system consists of 4 ODEs, complemented with the pharmacodynamic effects of PD-1 and CTLA-4 inhibitors as explicit functions.…”

Section: Resultsmentioning

confidence: 99%

Mathematical modeling in autoimmune diseases: from theory to clinical application

Ugolkov,

Nikitich,

Leon

et al. 2024

Front. Immunol.

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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.

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Section: Resultsmentioning

confidence: 99%

Mathematical modeling in autoimmune diseases: from theory to clinical application

Ugolkov,

Nikitich,

Leon

et al. 2024

Front. Immunol.

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“…With the current information of interactions between each cellular player and the kinetics of the immune response, some mathematical models have emerged to predict disease progress [ 88 ]. Modelling will be further improved as a consequence of deeper characterization of pathogenesis mechanisms.…”

Section: Recent Insights Into Human Ici-associated Myocarditis Develo...mentioning

confidence: 99%

Pathophysiology of Immune Checkpoint Inhibitor-Induced Myocarditis

Jiménez-Alejandre

Ruiz-Fernández

Martín

2022

Cancers

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Immune checkpoint inhibitors (ICIs) have recently emerged as strong therapies for a broad spectrum of cancers being the first-line treatment for many of them, even improving the prognosis of malignancies that were considered untreatable. This therapy is based on the administration of monoclonal antibodies targeting inhibitory T-cell receptors, which boost the immune system and prevent immune evasion. However, non-specific T-cell de-repression can result in a wide variety of immune-related adverse events (irAEs), including gastrointestinal, endocrine, and dermatologic, with a smaller proportion of these having the potential for fatal outcomes such as neurotoxicity, pulmonary toxicity, and cardiotoxicity. In recent years, alarm has been raised about cardiotoxicity as it has the highest mortality rate when myocarditis develops. However, due to the difficulty in diagnosing this cardiac condition and the lack of clinical guidelines for the management of cardiovascular disease in patients on therapy with ICIs, early detection of myocarditis has become a challenge in these patients. In this review we outline the mechanisms of tolerance by which this fatal cardiomyopathy may develop in selected cancer patients treated with ICIs, summarize preclinical models of the disease that will allow the development of more accurate strategies for its detection and treatment, and discuss the challenges in the future to decrease the risks of its development with better decision making in susceptible patients.

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“…We recently published the first mathematical model of autoimmune myocarditis based on a highly simplified version of the graph in Figure 3 ( van der Vegt et al, 2022 ). This model consists of four ordinary differential equations (ODEs) describing the time evolution of dead/damaged cardiomyocytes, innate immune cells, pathogenic CD4 + T cells and regulatory T cells, and exhibits steady state behaviour that is consistent with what is observed in patients.…”

Section: Mathematical Modelingmentioning

confidence: 99%

A model-informed approach to assess the risk of immune checkpoint inhibitor-induced autoimmune myocarditis

et al. 2022

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Immune checkpoint inhibitors (ICIs), as a novel immunotherapy, are designed to modulate the immune system to attack malignancies. Despite their promising benefits, immune-related adverse events (IRAEs) may occur, and incidences are bound to increase with surging demand of this class of drugs in treating cancer. Myocarditis, although rare compared to other IRAEs, has a significantly higher fatal frequency. Due to the overwhelming complexity of the immune system, this condition is not well understood, despite the significant research efforts devoted to it. To better understand the development and progression of autoimmune myocarditis and the roles of ICIs therein, we suggest a new approach: mathematical modelling. Mathematical modelling of myocarditis has enormous potential to determine which parts of the immune system are critical to the development and progression of the disease, and therefore warrant further investigation. We provide the immunological background needed to develop a mathematical model of this disease and review relevant existing models of immunology that serve as the mathematical inspiration needed to develop this field.

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Mathematical modelling of autoimmune myocarditis and the effects of immune checkpoint inhibitors

Cited by 8 publications

References 81 publications

Mathematical modeling in autoimmune diseases: from theory to clinical application

Mathematical modeling in autoimmune diseases: from theory to clinical application

Pathophysiology of Immune Checkpoint Inhibitor-Induced Myocarditis

A model-informed approach to assess the risk of immune checkpoint inhibitor-induced autoimmune myocarditis

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