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

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 a) 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 revealed 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-stratification and therefore holds promise to become a powerful model-informed AD drug development and personalized medicine tool.