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J Allergy Clin Immunol. 2018 Nov 7. pii: S0091-6749(18)31573-2. doi: 10.1016/j.jaci.2018.10.033. [Epub ahead of print]

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, Germany; Center of Allergy and Environment (ZAUM), HMGU and Technical University of Munich, Germany.
2
Skin Research Group, School of Medicine, University of Dundee; 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, Illinois.
4
Department of Bioengineering, Imperial College London, London, United Kingdom.
5
Innovaderm Research Inc, Montreal, Quebec, Canada.
6
Department of Pediatric Dermatology, Institute of Child Health, Kolkata, West Bengal, India.
7
Department of Dermatology and Allergy, University of Bonn, Bonn, Germany; Christine K├╝hne-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, New York.
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, Denmark.
12
The Department of Dermatology, Aalborg Universityhospital, Aalborg Denmark.
13
Medizinische Hochschule Hannover, Hannover, Germany.
14
Department of Dermatology and Allergy, Ludwig-Maximilian-University Munich, Munich, Germany.
15
Departments of Dermatology and Pediatrics and the Skin Disease Research Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
16
Dermatological Sciences, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK; Department of Dermatology, Royal Victoria Infirmary, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom.

Abstract

Atopic dermatitis (AD) is a prevalent disease worldwide associated with systemic co-morbidities, representing a significant burden on individuals, their families and society. Therapeutic options for AD remain limited, in part due to lack of well-characterised animal models. To better define pathophysiological mechanisms and to identify novel therapeutic targets and biomarkers that predict therapeutic response, there has been increasing interest in developing experimental approaches to study the pathogenesis of human AD in vivo, in vitro, and in silico. 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 "omic" datasets; and the development of predictive computational models. Whilst 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 the application of machine learning and a systems approach to data integration and predictive modelling, highlight the applicability of these methods to AD subclassification (endotyping), therapy development and precision medicine. Such predictive modelling will highlight knowledge gaps, further inform refinement of biological models, and support new experimental and systems approaches to AD.

KEYWORDS:

Atopic dermatitis; Endotype; Human models; Machine learning; Mechanistic models; Precision medicine; Skin equivalents; Systems biology; Tissue culture models; atopic eczema

PMID:
30414395
DOI:
10.1016/j.jaci.2018.10.033
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