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Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council

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Title: Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council
Authors: Eyerich, K
Brown, S
Perez White, B
Tanaka, RJ
Bissonette, R
Dhar, S
Bieber, T
Hijnen, DJ
Guttman-Yassky, E
Irvine, A
Thyssen, JP
Vestergaard, C
Werfel, T
Wollenberg, A
Paller, A
Reynolds, NJ
Item Type: Journal Article
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.
Issue Date: 1-Jan-2019
Date of Acceptance: 30-Oct-2018
URI: http://hdl.handle.net/10044/1/65968
DOI: https://dx.doi.org/10.1016/j.jaci.2018.10.033
ISSN: 0091-6749
Publisher: Elsevier
Start Page: 36
End Page: 45
Journal / Book Title: Journal of Allergy and Clinical Immunology
Volume: 143
Issue: 1
Copyright Statement: © 2018 The Authors. Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
The Royal Society
Riken
Funder's Grant Number: EP/G007446/1
RG160663
2018-1165 (RC003491)
Keywords: Atopic dermatitis
atopic eczema
endotype
human models
machine learning
mechanistic models
precision medicine
skin equivalents
systems biology
tissue culture models
1107 Immunology
Allergy
Publication Status: Published
Online Publication Date: 2018-11-07
Appears in Collections:Faculty of Engineering
Bioengineering



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