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Lookup NU author(s): Dr Fedor ShmarovORCiD, Dr Graham Smith, Dr Sophie Weatherhead, Professor Nick ReynoldsORCiD, Dr Paolo Zuliani
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Copyright: © 2022 Shmarov et al.Despite increased understanding about psoriasis pathophysiology, currently there is a lack of predictive computational models. We developed a personalisable ordinary differential equations model of human epidermis and psoriasis that incorporates immune cells and cytokine stimuli to regulate the transition between two stable steady states of clinically healthy (non-lesional) and disease (lesional psoriasis, plaque) skin. In line with experimental data, an immune stimulus initiated transition from healthy skin to psoriasis and apoptosis of immune and epidermal cells induced by UVB phototherapy returned the epidermis back to the healthy state. Notably, our model was able to distinguish disease flares. The flexibility of our model permitted the development of a patient-specific “UVB sensitivity” parameter that reflected subject-specific sensitivity to apoptosis and enabled simulation of individual patients’ clinical response trajectory. In a prospective clinical study of 94 patients, serial individual UVB doses and clinical response (Psoriasis Area Severity Index) values collected over the first three weeks of UVB therapy informed estimation of the “UVB sensitivity” parameter and the prediction of individual patient outcome at the end of phototherapy. An important advance of our model is its potential for direct clinical application through early assessment of response to UVB therapy, and for individualised optimisation of phototherapy regimes to improve clinical outcome. Additionally by incorporating the complex interaction of immune cells and epidermal keratinocytes, our model provides a basis to study and predict outcomes to biologic therapies in psoriasis.
Author(s): Shmarov F, Smith GR, Weatherhead SC, Reynolds NJ, Zuliani P
Publication type: Article
Publication status: Published
Journal: PLoS Computational Biology
Year: 2022
Volume: 18
Issue: 9
Online publication date: 30/09/2022
Acceptance date: 30/05/2022
Date deposited: 25/10/2022
ISSN (print): 1553-734X
ISSN (electronic): 1553-7358
Publisher: Public Library of Science
URL: https://doi.org/10.1371/journal.pcbi.1010267
DOI: 10.1371/journal.pcbi.1010267
PubMed id: 36178923
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