Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Atopic dermatitis (AD) is a chronic, multifactorial inflammatory skin disease with a complex, heterogeneous pathogenesis. Understanding its mechanisms, stratifying patients into biologically relevant endotypes, and predicting treatment responses remain challenging if we use empirical approaches alone. In silico approaches, including mathematical modeling, statistical and machine learning methods, enable the dissection of molecular and cellular interactions, the identification of key clinical and biological drivers, and the extraction of meaningful insights from high-dimensional, noisy datasets, while preserving a systems-level perspective. This review summarizes recent advancements in in silico approaches for AD and outlines strategies to enhance their translational and clinical utility in AD research.
Date Issued
2026-09-01
Date Acceptance
2026-07-13
Citation
JID Innovations, 2026, 6 (5)
ISSN
2667-0267
Publisher
Elsevier
Journal / Book Title
JID Innovations
Volume
6
Issue
5
Copyright Statement
ª 2026 The Author(s). Published by Elsevier Inc. on behalf of Society for Investigative Dermatology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Identifier
10.1016/j.xjidi.2026.100513
Subjects
3R, Atopic dermatitis, In silico models, Machine learning 3D, 3-dimensional
AD, atopic dermatitis
AI, artificial intelligence
CNN, convolutional neural network
DL, deep learning
ML, machine learning
ODE, ordinary differential equation
PINN, physics-informed neural network
QSP, quantitative systems pharmacology
S, staphylococcus
Th, T helper
Publication Status
Published
Article Number
100513
Date Publish Online
2026-07-21