TY - JOUR

T1 - Human and computational models of atopic dermatitis

T2 - A review and perspectives by an expert panel of the International Eczema Council

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 The Authors. Published by Elsevier Inc. All rights reserved.

PY - 2019

Y1 - 2019

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.

AB - 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.

U2 - 10.1016/j.jaci.2018.10.033

DO - 10.1016/j.jaci.2018.10.033

M3 - Review

C2 - 30414395

VL - 143

SP - 36

EP - 45

JO - Journal of Allergy and Clinical Immunology

JF - Journal of Allergy and Clinical Immunology

SN - 0091-6749

IS - 1

ER -