A quantitative systems pharmacology workflow towards optimal design and biomarker stratification of atopic dermatitis clinical trials

Abstract Background The development of atopic dermatitis (AD) drugs is confronted by many disease phenotypes and trial design options, which are hard to explore experimentally. Objective Optimize AD trial design using simulations. Methods We constructed a quantitative systems pharmacology (QSP) model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution is derived from known relationships between AD biomarkers and disease severity and b) calibrated using disease severity evolution under SoC regimens. Results We applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example, a) the choice of endpoint is more important than the choice of dosing-regimen and b) patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis reveals that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population Conclusion This AD QSP workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases so as to optimize several trial protocol parameters and biomarker-stratificaiton and therefore holds promise to become a powerful model-informed drug development tool. Key Messages Disease and treatment models can quantify pre-existing knowledge about complex immune diseases such as atopic dermatitis and drug’s efficacy data under one common umbrella. Embedding QSP models into trial simulation setup can give insight into clinical trial optimization. Complex QSP models can help with patient selection and biomarker identification. Capsule Summary This study shows the relevance of QSP model and computer simulations in assisting clinical development in the field of atopic dermatitis by assessing the impact of trial protocol on treatment effect and guiding biomarker programs.