In The Journal of allergy and clinical immunology
BACKGROUND : Chronic spontaneous urticaria (CSU) is a rare, heterogeneous, severely debilitating, and often poorly controlled skin disease resulting in an itchy eruption which can be persistent. Antihistamines and omalizumab, an anti-IgE monoclonal antibody, are the only licenced therapies. Although CSU pathogenesis is not yet fully understood, mast cell activation through the IgE:FcεRI (high affinity IgE receptor) axis appears central to the disease process.
OBJECTIVE : We sought to model CSU pathophysiology and identify in silico the mechanism of action of different CSU therapeutic strategies currently in use or under development.
METHODS : Therapeutic Performance Mapping System (TPMS) technology, based on systems biology and machine learning, was used to create a CSU interactome, validated with gene expression data from CSU patients, and a CSU model, which was used to evaluate CSU pathophysiology and the mechanism of action of different therapeutic strategies.
RESULTS : Our models reflect the known role of mast cells activation as central process of CSU pathophysiology, as well as recognized roles for different therapeutic strategies over this and other innate and adaptive immune processes. They also allow determining similarities and differences between them: anti-IgE and Bruton tyrosine kinase (BTK) inhibitors present a more direct role on mast cell biology through abrogation of FcεRI signalling activity, while anti-interleukins and anti-Siglec-8 have a role on adaptive immunity modulation.
CONCLUSION : In silico CSU models reproduced known CSU and therapeutic strategies features. Our results could help advance understanding of therapeutic mechanisms of action, and further advance treatment research by patient profile.
Segú-Vergés Cristina, Gómez Jessica, Terradas Pau, Artigas Laura, Smeets Serge, Ferrer Marta, Savic Sinisa
2022-Dec-29
artificial intelligence, chronic spontaneous urticaria, machine learning, mast cells, system biology
