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Can we predict who will benefit most from biologics in severe asthma? A post-hoc analysis of two phase 3 trials
BACKGROUND: Individualized prediction of treatment response may improve the value proposition of advanced treatment options in severe asthma. This study aimed to investigate the combined capacity of patient characteristics in predicting treatment response to mepolizumab in patients with severe asthm...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10155396/ https://www.ncbi.nlm.nih.gov/pubmed/37131185 http://dx.doi.org/10.1186/s12931-023-02409-2 |
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author | Chen, Wenjia Reddel, Helen K. FitzGerald, J Mark Beasley, Richard Janson, Christer Sadatsafavi, Mohsen |
author_facet | Chen, Wenjia Reddel, Helen K. FitzGerald, J Mark Beasley, Richard Janson, Christer Sadatsafavi, Mohsen |
author_sort | Chen, Wenjia |
collection | PubMed |
description | BACKGROUND: Individualized prediction of treatment response may improve the value proposition of advanced treatment options in severe asthma. This study aimed to investigate the combined capacity of patient characteristics in predicting treatment response to mepolizumab in patients with severe asthma. METHODS: Patient-level data were pooled from two multinational phase 3 trials of mepolizumab in severe eosinophilic asthma. We fitted penalized regression models to quantify reductions in the rate of severe exacerbations and the 5-item Asthma Control Questionnaire (ACQ5) score. The capacity of 15 covariates towards predicting treatment response was quantified by the Gini index (measuring disparities in treatment benefit) as well as observed treatment benefit within the quintiles of predicted treatment benefit. RESULTS: There was marked variability in the ability of patient characteristics to predict treatment response; covariates explained greater heterogeneity in predicting treatment response to asthma control than to exacerbation frequency (Gini index 0.35 v. 0.24). Key predictors for treatment benefit for severe exacerbations included exacerbation history, blood eosinophil count, baseline ACQ5 score and age, and those for symptom control included blood eosinophil count and presence of nasal polyps. Overall, the average reduction in exacerbations was 0.90/year (95%CI, 0.87‒0.92) and average reduction in ACQ5 score was 0.18 (95% CI, 0.02‒0.35). Among the top 20% of patients for predicted treatment benefit, exacerbations were reduced by 2.23/year (95% CI, 2.03‒2.43) and ACQ5 score were reduced by 0.59 (95% CI, 0.19‒0.98). Among the bottom 20% of patients for predicted treatment benefit, exacerbations were reduced by 0.25/year (95% CI, 0.16‒0.34) and ACQ5 by -0.20 (95% CI, -0.51 to 0.11). CONCLUSION: A precision medicine approach based on multiple patient characteristics can guide biologic therapy in severe asthma, especially in identifying patients who will not benefit as much from therapy. Patient characteristics had a greater capacity to predict treatment response to asthma control than to exacerbation. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT01691521 (registered September 24, 2012) and NCT01000506 (registered October 23, 2009). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12931-023-02409-2. |
format | Online Article Text |
id | pubmed-10155396 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101553962023-05-04 Can we predict who will benefit most from biologics in severe asthma? A post-hoc analysis of two phase 3 trials Chen, Wenjia Reddel, Helen K. FitzGerald, J Mark Beasley, Richard Janson, Christer Sadatsafavi, Mohsen Respir Res Research BACKGROUND: Individualized prediction of treatment response may improve the value proposition of advanced treatment options in severe asthma. This study aimed to investigate the combined capacity of patient characteristics in predicting treatment response to mepolizumab in patients with severe asthma. METHODS: Patient-level data were pooled from two multinational phase 3 trials of mepolizumab in severe eosinophilic asthma. We fitted penalized regression models to quantify reductions in the rate of severe exacerbations and the 5-item Asthma Control Questionnaire (ACQ5) score. The capacity of 15 covariates towards predicting treatment response was quantified by the Gini index (measuring disparities in treatment benefit) as well as observed treatment benefit within the quintiles of predicted treatment benefit. RESULTS: There was marked variability in the ability of patient characteristics to predict treatment response; covariates explained greater heterogeneity in predicting treatment response to asthma control than to exacerbation frequency (Gini index 0.35 v. 0.24). Key predictors for treatment benefit for severe exacerbations included exacerbation history, blood eosinophil count, baseline ACQ5 score and age, and those for symptom control included blood eosinophil count and presence of nasal polyps. Overall, the average reduction in exacerbations was 0.90/year (95%CI, 0.87‒0.92) and average reduction in ACQ5 score was 0.18 (95% CI, 0.02‒0.35). Among the top 20% of patients for predicted treatment benefit, exacerbations were reduced by 2.23/year (95% CI, 2.03‒2.43) and ACQ5 score were reduced by 0.59 (95% CI, 0.19‒0.98). Among the bottom 20% of patients for predicted treatment benefit, exacerbations were reduced by 0.25/year (95% CI, 0.16‒0.34) and ACQ5 by -0.20 (95% CI, -0.51 to 0.11). CONCLUSION: A precision medicine approach based on multiple patient characteristics can guide biologic therapy in severe asthma, especially in identifying patients who will not benefit as much from therapy. Patient characteristics had a greater capacity to predict treatment response to asthma control than to exacerbation. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT01691521 (registered September 24, 2012) and NCT01000506 (registered October 23, 2009). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12931-023-02409-2. BioMed Central 2023-05-02 2023 /pmc/articles/PMC10155396/ /pubmed/37131185 http://dx.doi.org/10.1186/s12931-023-02409-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Chen, Wenjia Reddel, Helen K. FitzGerald, J Mark Beasley, Richard Janson, Christer Sadatsafavi, Mohsen Can we predict who will benefit most from biologics in severe asthma? A post-hoc analysis of two phase 3 trials |
title | Can we predict who will benefit most from biologics in severe asthma? A post-hoc analysis of two phase 3 trials |
title_full | Can we predict who will benefit most from biologics in severe asthma? A post-hoc analysis of two phase 3 trials |
title_fullStr | Can we predict who will benefit most from biologics in severe asthma? A post-hoc analysis of two phase 3 trials |
title_full_unstemmed | Can we predict who will benefit most from biologics in severe asthma? A post-hoc analysis of two phase 3 trials |
title_short | Can we predict who will benefit most from biologics in severe asthma? A post-hoc analysis of two phase 3 trials |
title_sort | can we predict who will benefit most from biologics in severe asthma? a post-hoc analysis of two phase 3 trials |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10155396/ https://www.ncbi.nlm.nih.gov/pubmed/37131185 http://dx.doi.org/10.1186/s12931-023-02409-2 |
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