Application of systems biology-based in silico tools to optimize treatment strategy identification in Still’s disease
Abstract 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 si...
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| Format: | Article |
| Language: | English |
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BMC
2021-04-01
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| Series: | Arthritis Research & Therapy |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13075-021-02507-w |
| _version_ | 1818972345624166400 |
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| author | Cristina Segú-Vergés Mireia Coma Christoph Kessel Serge Smeets Dirk Foell Anna Aldea |
| author_facet | Cristina Segú-Vergés Mireia Coma Christoph Kessel Serge Smeets Dirk Foell Anna Aldea |
| author_sort | Cristina Segú-Vergés |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-20T15:06:48Z |
| format | Article |
| id | doaj.art-90648241b76a4414a9ae761e3e05b9b4 |
| institution | Directory Open Access Journal |
| issn | 1478-6362 |
| language | English |
| last_indexed | 2024-12-20T15:06:48Z |
| publishDate | 2021-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Arthritis Research & Therapy |
| spelling | doaj.art-90648241b76a4414a9ae761e3e05b9b42022-12-21T19:36:26ZengBMCArthritis Research & Therapy1478-63622021-04-0123111210.1186/s13075-021-02507-wApplication of systems biology-based in silico tools to optimize treatment strategy identification in Still’s diseaseCristina Segú-Vergés0Mireia Coma1Christoph Kessel2Serge Smeets3Dirk Foell4Anna Aldea5Anaxomics BiotechAnaxomics BiotechDepartment of Pediatric Rheumatology & Immunology, University Children’s HospitalNovartis, HaaksbergwegDepartment of Pediatric Rheumatology & Immunology, University Children’s HospitalNovartisAbstract 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.https://doi.org/10.1186/s13075-021-02507-wSystems biologySystemic juvenile idiopathic arthritisAdult-onset Still’s diseaseTreat-to-targetArtificial intelligenceMachine learning |
| spellingShingle | Cristina Segú-Vergés Mireia Coma Christoph Kessel Serge Smeets Dirk Foell Anna Aldea Application of systems biology-based in silico tools to optimize treatment strategy identification in Still’s disease Arthritis Research & Therapy Systems biology Systemic juvenile idiopathic arthritis Adult-onset Still’s disease Treat-to-target Artificial intelligence Machine learning |
| 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 | Systems biology Systemic juvenile idiopathic arthritis Adult-onset Still’s disease Treat-to-target Artificial intelligence Machine learning |
| url | https://doi.org/10.1186/s13075-021-02507-w |
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