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Eyerich, K. ; Brown, S.J.* ; White, B.E.P.* ; Tanaka, R.J.* ; Bissonette, R.* ; Dhar, S.* ; Bieber, T.* ; Hijnen, D.J.* ; Guttman-Yassky, E.* ; Irvine, A.* ; Thyssen, J.P.* ; Vestergaard, C.* ; Werfel, T.* ; Wollenberg, A.* ; Paller, A.S.* ; Reynolds, N.J.*

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

J. Allergy Clin. Immunol. 143, 36-45 (2019)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Atopic Dermatitis ; Atopic Eczema ; Endotype ; Human Models ; Machine Learning ; Mechanistic Models ; Precision Medicine ; Tissue Culture Models ; Skin Equivalents ; Systems Biology; In-vitro Model; Patch Test Reactions; Th2 Responses; Skin Barrier; T-cells; Sensitization; Cytokines; Proteins; Association; Activation
Sprache englisch
Veröffentlichungsjahr 2019
Prepublished im Jahr 2018
HGF-Berichtsjahr 2018
ISSN (print) / ISBN 0091-6749
e-ISSN 1097-6825
Zeitschrift The journal of allergy and clinical immunology
Quellenangaben Band: 143, Heft: 1, Seiten: 36-45 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Amsterdam [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Allergy Research (IAF)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Allergy
PSP-Element(e) G-505400-001
DOI 10.1016/j.jaci.2018.10.033
WOS ID WOS:000454918300004
PubMed ID 30414395
Erfassungsdatum 2019-01-21
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