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

Kilian Eyerich*, 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

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

51 Citations (Scopus)
86 Downloads (Pure)


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
JournalJournal of Allergy and Clinical Immunology
Issue number1
Pages (from-to)36-45
Number of pages10
Publication statusPublished - 1 Jan 2019

Bibliographical note

Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.


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


Dive into the research topics of 'Human and computational models of atopic dermatitis: A review and perspectives by an expert panel of the International Eczema Council'. Together they form a unique fingerprint.

Cite this