Imperial College London
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Professor Reiko J. Tanaka

Faculty of Engineering, Department of Bioengineering

Professor of Computational Systems Biology & Medicine
 
 
 
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Contact

 

+44 (0)20 7594 6374r.tanaka Website

 
 
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Location

 

RSM 3.10Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Miyano:2022:10.1111/all.14870,
author = {Miyano, T and Irvine, A and Tanaka, R},
doi = {10.1111/all.14870},
journal = {Allergy},
pages = {582--594},
title = {A mathematical model to identify optimal combinations of drug targets for dupilumab poor responders in atopic dermatitis},
url = {http://dx.doi.org/10.1111/all.14870},
volume = {77},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundSeveral 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.MethodsWe conducted modelbased metaanalysis 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 interferongamma) by describing systemlevel AD pathogenesis. Using this model, we simulated the clinical efficacy of hypothetical therapies on virtual patients.ResultsOur model reproduced reported time courses of %improved EASI and EASI75 of the nine drugs. The global sensitivity analysis and model simulation indicated the baseline level of IL13 could stratify dupilumab good responders. Model simulation on the efficacies of hypothetical therapies revealed that simultaneous inhibition of IL13 and IL22 was effective, whereas application of the nine biologic drugs was ineffective, for dupilumab poor responders (EASI75 at 24 weeks: 21.6% vs. max. 1.9%).ConclusionOur model identified IL13 as a potential predictive biomarker to stratify dupilumab good responders, and simultaneous inhibition of IL13 and IL22 as a promising drug therapy for dupilumab poor responders. This model will serve as a computational platform for modelinformed 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.
AU  - Miyano,T
AU  - Irvine,A
AU  - Tanaka,R
DO  - 10.1111/all.14870
EP  - 594
PY  - 2022///
SN  - 0105-4538
SP  - 582
TI  - A mathematical model to identify optimal combinations of drug targets for dupilumab poor responders in atopic dermatitis
T2  - Allergy
UR  - http://dx.doi.org/10.1111/all.14870
UR  - http://hdl.handle.net/10044/1/88768
VL  - 77
ER  -
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