Publication date: Available online 7 November 2018
Source: Journal of Allergy and Clinical Immunology
Author(s): 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
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.
https://ift.tt/2qBNCtW
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