Human and computational models of atopic dermatitis

A review and perspectives by an expert panel of the International Eczema Council

Kilian Eyerich (Lead / Corresponding author), Sara J. Brown, Bethany E. Perez White, Reiko J. Tanaka, Robert Bissonette, Sandipan Dhar, Thomas Bieber, Dirk J. Hijnen, Emma Guttman-Yassky, Alan Irvine, Jacob P. Thyssen, Christian Vestergaard, Thomas Werfel, Andreas Wollenberg, Amy S. Paller, Nick J. Reynolds (Lead / Corresponding author)

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD.

Original languageEnglish
Pages (from-to)36-45
Number of pages10
JournalJournal of Allergy and Clinical Immunology
Volume143
Issue number1
Early online date7 Nov 2018
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

Eczema
Atopic Dermatitis
Therapeutics
Biological Models
Precision Medicine
Skin
Genetic Models
Systems Analysis
Computer Simulation
Comorbidity
Molecular Biology
Animal Models
Biomarkers
Mutation

Keywords

  • Atopic dermatitis
  • atopic eczema
  • endotype
  • human models
  • machine learning
  • mechanistic models
  • precision medicine
  • skin equivalents
  • systems biology
  • tissue culture models

Cite this

Eyerich, Kilian ; Brown, Sara J. ; Perez White, Bethany E. ; Tanaka, Reiko J. ; Bissonette, Robert ; Dhar, Sandipan ; Bieber, Thomas ; Hijnen, Dirk J. ; Guttman-Yassky, Emma ; Irvine, Alan ; Thyssen, Jacob P. ; Vestergaard, Christian ; Werfel, Thomas ; Wollenberg, Andreas ; Paller, Amy S. ; Reynolds, Nick J. / Human and computational models of atopic dermatitis : A review and perspectives by an expert panel of the International Eczema Council. In: Journal of Allergy and Clinical Immunology. 2019 ; Vol. 143, No. 1. pp. 36-45.
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abstract = "Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD.",
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author = "Kilian Eyerich and Brown, {Sara J.} and {Perez White}, {Bethany E.} and Tanaka, {Reiko J.} and Robert Bissonette and Sandipan Dhar and Thomas Bieber and Hijnen, {Dirk J.} and Emma Guttman-Yassky and Alan Irvine and Thyssen, {Jacob P.} and Christian Vestergaard and Thomas Werfel and Andreas Wollenberg and Paller, {Amy S.} and Reynolds, {Nick J.}",
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Eyerich, K, Brown, SJ, Perez White, BE, Tanaka, RJ, Bissonette, R, Dhar, S, Bieber, T, Hijnen, DJ, Guttman-Yassky, E, Irvine, A, Thyssen, JP, Vestergaard, C, Werfel, T, Wollenberg, A, Paller, AS & Reynolds, NJ 2019, 'Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council', Journal of Allergy and Clinical Immunology, vol. 143, no. 1, pp. 36-45. https://doi.org/10.1016/j.jaci.2018.10.033

Human and computational models of atopic dermatitis : A review and perspectives by an expert panel of the International Eczema Council. / Eyerich, Kilian (Lead / Corresponding author); Brown, Sara J.; Perez White, Bethany E.; Tanaka, Reiko J.; Bissonette, Robert; Dhar, Sandipan; Bieber, Thomas; Hijnen, Dirk J.; Guttman-Yassky, Emma; Irvine, Alan; Thyssen, Jacob P.; Vestergaard, Christian; Werfel, Thomas; Wollenberg, Andreas; Paller, Amy S.; Reynolds, Nick J. (Lead / Corresponding author).

In: Journal of Allergy and Clinical Immunology, Vol. 143, No. 1, 01.01.2019, p. 36-45.

Research output: Contribution to journalArticle

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AU - Eyerich, Kilian

AU - Brown, Sara J.

AU - Perez White, Bethany E.

AU - Tanaka, Reiko J.

AU - Bissonette, Robert

AU - Dhar, Sandipan

AU - Bieber, Thomas

AU - Hijnen, Dirk J.

AU - Guttman-Yassky, Emma

AU - Irvine, Alan

AU - Thyssen, Jacob P.

AU - Vestergaard, Christian

AU - Werfel, Thomas

AU - Wollenberg, Andreas

AU - Paller, Amy S.

AU - Reynolds, Nick J.

N1 - Copyright © 2018. Published by Elsevier Inc.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Atopic dermatitis (AD) is a prevalent disease worldwide and is associated with systemic comorbidities representing a significant burden on patients, their families, and society. Therapeutic options for AD remain limited, in part because of a lack of well-characterized animal models. There has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico to better define pathophysiologic mechanisms and identify novel therapeutic targets and biomarkers that predict therapeutic response. This review critically appraises a range of models, including genetic mutations relevant to AD, experimental challenge of human skin in vivo, tissue culture models, integration of “omics” data sets, and development of predictive computational models. Although no one individual model recapitulates the complex AD pathophysiology, our review highlights insights gained into key elements of cutaneous biology, molecular pathways, and therapeutic target identification through each approach. Recent developments in computational analysis, including application of machine learning and a systems approach to data integration and predictive modeling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development, and precision medicine. Such predictive modeling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD.

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