Atopic dermatitis (AD) remains a highly heterogeneous inflammatory disease with variable patient response to biologic therapies. This poster highlights a Quantitative Systems Pharmacology (QSP) model developed in Certara IQ to simulate AD pathophysiology, predict therapeutic outcomes, and identify biomarkers associated with dupilumab response.
Download this scientific poster to explore how QSP modeling and machine learning approaches can:
- Predict response variability in atopic dermatitis therapies
- Identify IL-31 as a potential biomarker of dupilumab response
- Evaluate combination therapy strategies for non-responders
- Support precision medicine approaches in inflammatory skin disease
Key learning objectives
- Understand the structure and calibration of the AD QSP model
- Review validation against published clinical trial datasets
- Examine machine learning-based biomarker identification
- Explore the simulated efficacy of combination biologic approaches
Authors
Maria Jesus Munoz Lopez, Douglas W. Chung
