A mathematical model to identify optimal combinations of drug targets for dupilumab poor responders in atopic dermatitis. Issue 2 (5th May 2021)

• Record Type: Journal Article
• Authors: Miyano, Takuya; Irvine, Alan D.; Tanaka, Reiko J.
• Is Part Of: Allergy. Volume 77:Issue 2(2022)
• Page Start: 582
• Page End: 594
• Publication Date: 2021-05-05
• Subjects: atopic dermatitis -- dupilumab -- model‐based meta‐analysis -- poor responders -- quantitative systems pharmacology; Allergy -- Periodicals; 616.97
• DOI: 10.1111/all.14870
• Languages: English
• ISSNs: 0105-4538

Abstract

Abstract: Background: Several biologics 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. Methods: We 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 system‐level AD pathogenesis. Using this model, we simulated the clinical efficacy of hypothetical therapies on virtual patients. Results: Our model reproduced reported time courses of %improved EASI and EASI‐75 of the nine drugs. The global sensitivity analysis and model simulation indicated the baseline level of IL‐13 could stratify dupilumab good responders. Model simulation on the efficacies of hypothetical therapies revealed that simultaneous inhibition of IL‐13 and IL‐22 was effective, whereas application of the nine biologic drugs was ineffective, for dupilumab poor responders (EASI‐75 at 24 weeks: 21.6% vs. max. 1.9%). Conclusion: Our model identified IL‐13 as a potential predictive biomarker to stratify dupilumab good responders, and simultaneous inhibition of IL‐13 and IL‐22 as a promising drug therapy for dupilumab poor responders. 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 and the mechanisms behind patient variability in drug response. Abstract : We developed a mathematical model that describes system‐level AD pathogenesis and reproduces reported clinical efficacies of published clinical trials for nine biological drugs. We simulated clinical efficacy of hypothetical therapies on virtual dupilumab poor responders. Simultaneous inhibition of IL‐13 and IL‐22 is the most effective among combinations of two cytokines, whereas inhibition of either IL‐13 or IL‐22 alone is ineffective. Abbreviations: AD, atopic dermatitis; EASI, Eczema Area and Severity Index; IFN‐γ, interferon‐gamma; IL, interleukin.