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• W4386471915 abstract "Abstract Psoriasis is a chronic skin disease affecting 2-3% of the global population. Psoriasis arises from complex interactions between keratinocytes and immune cells, leading to uncontrolled inflammation, immune hyperactivation and perturbed keratinocyte life cycle. Although the latest generation of drugs have greatly improved psoriasis management, the disease remains incurable, and the substantial variability in treatment response calls for novel approaches to comprehend the intricate mechanisms underlying disease development and to discover potential drug targets. In this study, we present a multiscale population model that captures the dynamics of cell-specific phenotypes in psoriasis, integrating discrete logical formalism and population dynamics simulations. Through simulations and network metrics, we identify potential pairwise interventions as alternative treatment options. Specifically, The model predictions suggest that targeting neutrophil activation in conjunction with either PGE2 production or STAT3 signaling shows promise comparable to IL-17 inhibition, which is currently the most used treatment option for moderate and severe cases of psoriasis. Our findings underscore the significance of considering complex intercellular interactions and intracellular signaling cascades in psoriasis, and highlight the importance of computational approaches in unraveling complex biological systems for drug target identification. Author summary In our study, we aimed to uncover the complex mechanisms underlying psoriasis and identify potential treatment options. By utilizing a computational model, we simulated the dynamic interactions between different cell types involved in psoriasis, such as immune cells and keratinocytes. Our model predicts that targeting neutrophil activation, combined with either PGE2 production or STAT3 signaling, may yield comparable effectiveness to the current standard treatment for moderate or severe psoriasis, namely IL-17 inhibition. Our study underscores the importance of computational modeling in unraveling the complexities of disease systems and provides a foundation for identifying new candidate treatment options in psoriasis that should be tested in the lab." @default.
• W4386471915 created "2023-09-07" @default.
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• W4386471915 date "2023-09-06" @default.
• W4386471915 modified "2023-09-27" @default.
• W4386471915 title "A dynamic Boolean model of molecular and cellular interactions represents psoriasis development and predicts drug candidates." @default.
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• W4386471915 doi "https://doi.org/10.1101/2023.09.03.556147" @default.
• W4386471915 hasPublicationYear "2023" @default.
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