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Ugolkov Y, Nikitich A, Leon C, Helmlinger G, Peskov K, Sokolov V, Volkova A. Mathematical modeling in autoimmune diseases: from theory to clinical application. Front Immunol 2024; 15:1371620. [PMID: 38550585 PMCID: PMC10973044 DOI: 10.3389/fimmu.2024.1371620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 02/29/2024] [Indexed: 04/02/2024] Open
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.
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Affiliation(s)
- Yaroslav Ugolkov
- Research Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, Russia
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, Russia
| | - Antonina Nikitich
- Research Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, Russia
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, Russia
| | - Cristina Leon
- Modeling and Simulation Decisions FZ - LLC, Dubai, United Arab Emirates
| | | | - Kirill Peskov
- Research Center of Model-Informed Drug Development, Ivan Mikhaylovich (I.M.) Sechenov First Moscow State Medical University, Moscow, Russia
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, Russia
- Modeling and Simulation Decisions FZ - LLC, Dubai, United Arab Emirates
- Sirius University of Science and Technology, Sirius, Russia
| | - Victor Sokolov
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, Russia
- Modeling and Simulation Decisions FZ - LLC, Dubai, United Arab Emirates
| | - Alina Volkova
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (RAS), Moscow, Russia
- Modeling and Simulation Decisions FZ - LLC, Dubai, United Arab Emirates
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Casson CL, John SA, Ferrall-Fairbanks MC. Mathematical modeling of cardio-oncology: Modeling the systemic effects of cancer therapeutics on the cardiovascular system. Semin Cancer Biol 2023; 97:30-41. [PMID: 37979714 DOI: 10.1016/j.semcancer.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 08/25/2023] [Accepted: 11/12/2023] [Indexed: 11/20/2023]
Abstract
Cardiotoxicity is a common side-effect of many cancer therapeutics; however, to-date there has been very little push to understand the mechanisms underlying this group of pathologies. This has led to the emergence of cardio-oncology, a field of medicine focused on understanding the effects of cancer and its treatment on the human heart. Here, we describe how mechanistic modeling approaches have been applied to study open questions in the cardiovascular system and how these approaches are being increasingly applied to advance knowledge of the underlying effects of cancer treatments on the human heart. A variety of mechanistic, mathematical modeling techniques have been applied to explore the link between common cancer treatments, such as chemotherapy, radiation, targeted therapy, and immunotherapy, and cardiotoxicity, nevertheless there is limited coverage in the different types of cardiac dysfunction that may be associated with these treatments. Moreover, cardiac modeling has a rich heritage of mathematical modeling and is well suited for the further development of novel approaches for understanding the cardiotoxicities associated with cancer therapeutics. There are many opportunities to combine mechanistic, bottom-up approaches with data-driven, top-down approaches to improve personalized, precision oncology to better understand, and ultimately mitigate, cardiac dysfunction in cancer patients.
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Affiliation(s)
- Camara L Casson
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Sofia A John
- Department of Statistics, University of Florida, Gainesville, FL 32611, USA
| | - Meghan C Ferrall-Fairbanks
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA; University of Florida Health Cancer Center, University of Florida, Gainesville, FL 32611, USA.
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van der Vegt SA, Wang YJ, Polonchuk L, Wang K, Waters SL, Baker RE. A model-informed approach to assess the risk of immune checkpoint inhibitor-induced autoimmune myocarditis. Front Pharmacol 2022; 13:966180. [PMID: 36249751 PMCID: PMC9555336 DOI: 10.3389/fphar.2022.966180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/23/2022] [Indexed: 11/17/2022] Open
Abstract
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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Affiliation(s)
- Solveig A. van der Vegt
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
- *Correspondence: Solveig A. van der Vegt,
| | - Ying-Jie Wang
- Department of Cardiovascular Medicine, Radcliffe Department of Medicine, Wellcome Centre of Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Liudmila Polonchuk
- Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Ken Wang
- Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Sarah L. Waters
- Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Ruth E. Baker
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
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Jiménez-Alejandre R, Ruiz-Fernández I, Martín P. Pathophysiology of Immune Checkpoint Inhibitor-Induced Myocarditis. Cancers (Basel) 2022; 14:4494. [PMID: 36139654 PMCID: PMC9497311 DOI: 10.3390/cancers14184494] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
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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Affiliation(s)
| | | | - Pilar Martín
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
- CIBER de Enfermedades Cardiovasculares (CIBER-CV), 28029 Madrid, Spain
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Lourenço WDJ, Reis RF, Ruiz-Baier R, Rocha BM, dos Santos RW, Lobosco M. A Poroelastic Approach for Modelling Myocardial Oedema in Acute Myocarditis. Front Physiol 2022; 13:888515. [PMID: 35860652 PMCID: PMC9289286 DOI: 10.3389/fphys.2022.888515] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 05/27/2022] [Indexed: 12/28/2022] Open
Abstract
Myocarditis is a general set of mechanisms that manifest themselves into the inflammation of the heart muscle. In 2017, more than 3 million people were affected by this disease worldwide, causing about 47,000 deaths. Many aspects of the origin of this disease are well known, but several important questions regarding the disease remain open. One of them is why some patients develop a significantly localised inflammation while others develop a much more diffuse inflammation, reaching across large portions of the heart. Furthermore, the specific role of the pathogenic agent that causes inflammation as well as the interaction with the immune system in the progression of the disease are still under discussion. Providing answers to these crucial questions can have an important impact on patient treatment. In this scenario, computational methods can aid specialists to understand better the relationships between pathogens and the immune system and elucidate why some patients develop diffuse myocarditis. This paper alters a recently developed model to study the myocardial oedema formation in acute infectious myocarditis. The model describes the finite deformation regime using partial differential equations to represent tissue displacement, fluid pressure, fluid phase, and the concentrations of pathogens and leukocytes. A sensitivity analysis was performed to understand better the influence of the most relevant model parameters on the disease dynamics. The results showed that the poroelastic model could reproduce local and diffuse myocarditis dynamics in simplified and complex geometrical domains.
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Affiliation(s)
- Wesley de Jesus Lourenço
- Graduate Program on Computational Modelling, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Ruy Freitas Reis
- Department of Computer Science, Institute of Exact Sciences, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Ricardo Ruiz-Baier
- School of Mathematics and Victorian Heart Institute, Monash University, Melbourne, VIC, Australia
- Research Core on Natural and Exact Sciences, Universidad Adventista de Chile, Chillán, Chile
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Bernardo Martins Rocha
- Graduate Program on Computational Modelling, Federal University of Juiz de Fora, Juiz de Fora, Brazil
- Department of Computer Science, Institute of Exact Sciences, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Rodrigo Weber dos Santos
- Graduate Program on Computational Modelling, Federal University of Juiz de Fora, Juiz de Fora, Brazil
- Department of Computer Science, Institute of Exact Sciences, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Marcelo Lobosco
- Graduate Program on Computational Modelling, Federal University of Juiz de Fora, Juiz de Fora, Brazil
- Department of Computer Science, Institute of Exact Sciences, Federal University of Juiz de Fora, Juiz de Fora, Brazil
- *Correspondence: Marcelo Lobosco,
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