Application of Systems Biology-based in Silico Tools to opti
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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 was 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.

The Therapeutic Performance Mapping System (TPMS) technology was used to create in silico mechanisms of action (MoA) models using 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 traditional disease-modifying anti-rheumatic drugs (DMARDs, methotre Artificial neural networks, sampling-based approaches, and artificial intelligence are all used in TPMS.

--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-1?-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.

Finally, the use of biologics as an immunomodulatory treatment strategy for Still's disease was enabled by systems biology-based modeling. These findings support the importance of IL-1 blockade in innate immunity control in systemic autoinflammatory diseases. This supports the use of IL-1 blockade for Still's disease early on to avoid disease or drug-related complications.