Published Online: December 5, 2016
Atopic dermatitis (AD) is the most common chronic inflammatory skin disease. While the common symptoms and signs of AD include dry itchy skin and inflammation, the disease severity and clinical profile widely vary depending on each patient’s genetics, environmental factors and disease stage. Understanding of the complex interactions among these factors leading to different manifestations of AD will help to develop personalized treatments.
In an article recently published in The Journal of Allergy and Clinical Immunology (JACI), Domínguez-Hüttinger and colleagues applied a novel mathematical modelling approach to address this issue. They collated data from previous mechanistic and clinical studies on AD and developed a mathematical model that integrates the dynamic interactions between skin barrier, immune responses, genetics, and environmental factors.
Mathematical analysis and computer simulations of their mathematical model could predict how the disease develops and can be treated over time for individual virtual patient cohorts. The model analysis suggested that exposure to environmental stressors cause longer and stronger flares if patients have genetic predisposition for AD, that repeated AD flares trigger irreversible overreaction of immune system leading to progression of AD to become chronic, and that moisturising treatments could help prevent all newborns, not just those with genetic risk factors, from developing AD. The mathematical modelling that Domínguez-Hüttinger and colleagues used is a core tool for systems medicine, an interdisciplinary research field that investigates disease mechanisms using a systems approach. It is a potentially powerful tool for identifying the vulnerable patient cohorts who would benefit from preventive treatments and for identifying and testing new and better treatments for AD.
The Journal of Allergy and Clinical Immunology (JACI) is an official scientific journal of the AAAAI, and is the most-cited journal in the field of allergy and clinical immunology.