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Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis

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 tran...

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Autores principales: Shmarov, Fedor, Smith, Graham R., Weatherhead, Sophie C., Reynolds, Nick J., Zuliani, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524682/
https://www.ncbi.nlm.nih.gov/pubmed/36178923
http://dx.doi.org/10.1371/journal.pcbi.1010267
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author Shmarov, Fedor
Smith, Graham R.
Weatherhead, Sophie C.
Reynolds, Nick J.
Zuliani, Paolo
author_facet Shmarov, Fedor
Smith, Graham R.
Weatherhead, Sophie C.
Reynolds, Nick J.
Zuliani, Paolo
author_sort Shmarov, Fedor
collection PubMed
description 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.
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spelling pubmed-95246822022-10-01 Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis Shmarov, Fedor Smith, Graham R. Weatherhead, Sophie C. Reynolds, Nick J. Zuliani, Paolo PLoS Comput Biol Research Article 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. Public Library of Science 2022-09-30 /pmc/articles/PMC9524682/ /pubmed/36178923 http://dx.doi.org/10.1371/journal.pcbi.1010267 Text en © 2022 Shmarov et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Shmarov, Fedor
Smith, Graham R.
Weatherhead, Sophie C.
Reynolds, Nick J.
Zuliani, Paolo
Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis
title Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis
title_full Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis
title_fullStr Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis
title_full_unstemmed Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis
title_short Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis
title_sort individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524682/
https://www.ncbi.nlm.nih.gov/pubmed/36178923
http://dx.doi.org/10.1371/journal.pcbi.1010267
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