Format

Send to

Choose Destination
J Allergy Clin Immunol. 2019 Jan;143(1):36-45. doi: 10.1016/j.jaci.2018.10.033. Epub 2018 Nov 7.

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

Author information

1
Department of Dermatology and Allergy, Technical University of Munich, Munich, Germany; Center of Allergy and Environment (ZAUM), HMGU and Technical University of Munich, Munich, Germany. Electronic address: kilian.eyerich@tum.de.
2
Skin Research Group, School of Medicine, University of Dundee, Dundee, United Kingdom; Department of Dermatology, Ninewells Hospital and Medical School, Dundee, United Kingdom.
3
Department of Dermatology and Skin Tissue Engineering Core, Feinberg School of Medicine, Northwestern University, Chicago, Ill.
4
Department of Bioengineering, Imperial College London, London, United Kingdom.
5
Innovaderm Research, Montreal, Quebec, Canada.
6
Department of Pediatric Dermatology, Institute of Child Health, Kolkata, India.
7
Department of Dermatology and Allergy, University of Bonn, Bonn, Germany; Christine Kühne-Center for Allergy Research and Education, Davos, Switzerland.
8
Department of Dermatology, Erasmus University Medical Center (Erasmus MC), Rotterdam, the Netherlands.
9
Icahn School of Medicine at Mount Sinai Medical Center, New York, NY.
10
Trinity College Dublin, National Children's Research Centre, Paediatric Dermatology Our Lady's Children's Hospital, Dublin, Ireland.
11
Department of Dermatology and Allergy, National Allergy Research Centre, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
12
Department of Dermatology, Aalborg University Hospital, Aalborg, Denmark.
13
Medizinische Hochschule Hannover, Hannover, Germany.
14
Department of Dermatology and Allergy, Ludwig-Maximilians-Universität Munich, Munich, Germany.
15
Departments of Dermatology and Pediatrics and the Skin Disease Research Center, Northwestern University Feinberg School of Medicine, Chicago, Ill.
16
Dermatological Sciences, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Dermatology, Royal Victoria Infirmary, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom. Electronic address: nick.reynolds@ncl.ac.uk.

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.

KEYWORDS:

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

PMID:
30414395
PMCID:
PMC6626639
[Available on 2020-01-01]
DOI:
10.1016/j.jaci.2018.10.033
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center