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Application of systems biology-based in silico tools to optimize treatment strategy identification in Still’s disease
BACKGROUND: Systemic juvenile idiopathic arthritis (sJIA) and adult-onset Still’s disease (AOSD) are manifestations of an autoinflammatory disorder with complex pathophysiology and significant morbidity, together also termed Still’s disease. The objective of the current study is to set in silico mod...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8063416/ https://www.ncbi.nlm.nih.gov/pubmed/33892792 http://dx.doi.org/10.1186/s13075-021-02507-w |
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author | Segú-Vergés, Cristina Coma, Mireia Kessel, Christoph Smeets, Serge Foell, Dirk Aldea, Anna |
author_facet | Segú-Vergés, Cristina Coma, Mireia Kessel, Christoph Smeets, Serge Foell, Dirk Aldea, Anna |
author_sort | Segú-Vergés, Cristina |
collection | PubMed |
description | BACKGROUND: Systemic juvenile idiopathic arthritis (sJIA) and adult-onset Still’s disease (AOSD) are manifestations of an autoinflammatory disorder with complex pathophysiology and significant morbidity, together also termed Still’s disease. The objective of the current study is to set in silico models based on systems biology and investigate the optimal treat-to-target strategy for Still’s disease as a proof-of-concept of the modeling approach. METHODS: Molecular characteristics of Still’s disease and data on biological inhibitors of interleukin (IL)-1 (anakinra, canakinumab), IL-6 (tocilizumab, sarilumab), and glucocorticoids as well as conventional disease-modifying anti-rheumatic drugs (DMARDs, methotrexate) were used to construct in silico mechanisms of action (MoA) models by means of Therapeutic Performance Mapping System (TPMS) technology. TPMS combines artificial neuronal networks, sampling-based methods, and artificial intelligence. Model outcomes were validated with published expression data from sJIA patients. RESULTS: Biologicals demonstrated more pathophysiology-directed efficiency than non-biological drugs. IL-1 blockade mainly acts on proteins implicated in the innate immune system, while IL-6 signaling blockade has a weaker effect on innate immunity and rather affects adaptive immune mechanisms. The MoA models showed that in the autoinflammatory/systemic phases of Still’s disease, in which the innate immunity plays a pivotal role, the IL-1β-neutralizing antibody canakinumab is more efficient than the IL-6 receptor-inhibiting antibody tocilizumab. MoA models reproduced 67% of the information obtained from expression data. CONCLUSIONS: Systems biology-based modeling supported the preferred use of biologics as an immunomodulatory treatment strategy for Still’s disease. Our results reinforce the role for IL-1 blockade on innate immunity regulation, which is critical in systemic autoinflammatory diseases. This further encourages early use on Still’s disease IL-1 blockade to prevent the development of disease or drug-related complications. Further analysis at the clinical level will validate the findings and help determining the timeframe of the window of opportunity for canakinumab treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13075-021-02507-w. |
format | Online Article Text |
id | pubmed-8063416 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80634162021-04-23 Application of systems biology-based in silico tools to optimize treatment strategy identification in Still’s disease Segú-Vergés, Cristina Coma, Mireia Kessel, Christoph Smeets, Serge Foell, Dirk Aldea, Anna Arthritis Res Ther Research Article BACKGROUND: Systemic juvenile idiopathic arthritis (sJIA) and adult-onset Still’s disease (AOSD) are manifestations of an autoinflammatory disorder with complex pathophysiology and significant morbidity, together also termed Still’s disease. The objective of the current study is to set in silico models based on systems biology and investigate the optimal treat-to-target strategy for Still’s disease as a proof-of-concept of the modeling approach. METHODS: Molecular characteristics of Still’s disease and data on biological inhibitors of interleukin (IL)-1 (anakinra, canakinumab), IL-6 (tocilizumab, sarilumab), and glucocorticoids as well as conventional disease-modifying anti-rheumatic drugs (DMARDs, methotrexate) were used to construct in silico mechanisms of action (MoA) models by means of Therapeutic Performance Mapping System (TPMS) technology. TPMS combines artificial neuronal networks, sampling-based methods, and artificial intelligence. Model outcomes were validated with published expression data from sJIA patients. RESULTS: Biologicals demonstrated more pathophysiology-directed efficiency than non-biological drugs. IL-1 blockade mainly acts on proteins implicated in the innate immune system, while IL-6 signaling blockade has a weaker effect on innate immunity and rather affects adaptive immune mechanisms. The MoA models showed that in the autoinflammatory/systemic phases of Still’s disease, in which the innate immunity plays a pivotal role, the IL-1β-neutralizing antibody canakinumab is more efficient than the IL-6 receptor-inhibiting antibody tocilizumab. MoA models reproduced 67% of the information obtained from expression data. CONCLUSIONS: Systems biology-based modeling supported the preferred use of biologics as an immunomodulatory treatment strategy for Still’s disease. Our results reinforce the role for IL-1 blockade on innate immunity regulation, which is critical in systemic autoinflammatory diseases. This further encourages early use on Still’s disease IL-1 blockade to prevent the development of disease or drug-related complications. Further analysis at the clinical level will validate the findings and help determining the timeframe of the window of opportunity for canakinumab treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13075-021-02507-w. BioMed Central 2021-04-23 2021 /pmc/articles/PMC8063416/ /pubmed/33892792 http://dx.doi.org/10.1186/s13075-021-02507-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Segú-Vergés, Cristina Coma, Mireia Kessel, Christoph Smeets, Serge Foell, Dirk Aldea, Anna Application of systems biology-based in silico tools to optimize treatment strategy identification in Still’s disease |
title | Application of systems biology-based in silico tools to optimize treatment strategy identification in Still’s disease |
title_full | Application of systems biology-based in silico tools to optimize treatment strategy identification in Still’s disease |
title_fullStr | Application of systems biology-based in silico tools to optimize treatment strategy identification in Still’s disease |
title_full_unstemmed | Application of systems biology-based in silico tools to optimize treatment strategy identification in Still’s disease |
title_short | Application of systems biology-based in silico tools to optimize treatment strategy identification in Still’s disease |
title_sort | application of systems biology-based in silico tools to optimize treatment strategy identification in still’s disease |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8063416/ https://www.ncbi.nlm.nih.gov/pubmed/33892792 http://dx.doi.org/10.1186/s13075-021-02507-w |
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