AbstractBackgroundSeveral biologic drugs for atopic dermatitis (AD) have demonstrated good efficacy in clinical trials, but with a substantial proportion of patients being identified as poor responders. This study aims to understand the pathophysiological backgrounds of patient variability in drug response, especially for dupilumab, and to identify promising drug targets in dupilumab poor responders.MethodsWe conducted model-based meta-analysis of recent clinical trials of AD biologics and developed a mathematical model that reproduces reported clinical efficacies for nine biological drugs (dupilumab, lebrikizumab, tralokinumab, secukinumab, fezakinumab, nemolizumab, tezepelumab, GBR 830, and recombinant interferon-gamma) by describing systems-level AD pathogenesis. Using this model, we simulated the clinical efficacy of hypothetical therapies on virtual patients.ResultsIL-13 in the skin was affirmed, by the global sensitivity analysis of our model, as a potential predictive biomarker to stratify dupilumab good responders. The model simulation identified simultaneous inhibition of IL-13 and IL-22 as a promising drug target for dupilumab poor responders, whereas inhibition of either IL-13 or IL-22 alone in these non-responders was ineffective.ConclusionWe present a mathematical model of AD pathogenesis developed by integration of clinical efficacy data of multiple drugs. This model will serve as a computational platform for model-informed drug development for precision medicine, as it allows evaluation of the effects of new potential drug targets, including combination therapeutics, at an individual patient level and the mechanisms behind patient variability in drug response. Similar mathematical models can be developed for other diseases and drugs, for patient stratification and identification of predictive biomarkers.
A mathematical model to identify optimal combinations of drug targets for dupilumab poor responders in atopic dermatitis
