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Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions

Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract inf...

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Autores principales: Arsène, Simon, Couty, Claire, Faddeenkov, Igor, Go, Natacha, Granjeon-Noriot, Solène, Šmít, Daniel, Kahoul, Riad, Illigens, Ben, Boissel, Jean-Pierre, Chevalier, Aude, Lehr, Lorenz, Pasquali, Christian, Kulesza, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9008035/
https://www.ncbi.nlm.nih.gov/pubmed/35418135
http://dx.doi.org/10.1038/s41467-022-29534-8
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author Arsène, Simon
Couty, Claire
Faddeenkov, Igor
Go, Natacha
Granjeon-Noriot, Solène
Šmít, Daniel
Kahoul, Riad
Illigens, Ben
Boissel, Jean-Pierre
Chevalier, Aude
Lehr, Lorenz
Pasquali, Christian
Kulesza, Alexander
author_facet Arsène, Simon
Couty, Claire
Faddeenkov, Igor
Go, Natacha
Granjeon-Noriot, Solène
Šmít, Daniel
Kahoul, Riad
Illigens, Ben
Boissel, Jean-Pierre
Chevalier, Aude
Lehr, Lorenz
Pasquali, Christian
Kulesza, Alexander
author_sort Arsène, Simon
collection PubMed
description Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI. Ratio-based efficacy metrics are only impacted under strict lockdown whereas absolute benefit already is with intermediate NPIs (eg. mask-wearing). Consequently, despite NPI, trials may meet their relative efficacy endpoints (provided recruitment hurdles can be overcome) but are difficult to assess with respect to clinical relevance. These results advocate to report a variety of metrics for benefit assessment, to use adaptive trial design and adapted statistical analyses. They also question eligibility criteria misaligned with the actual disease burden.
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spelling pubmed-90080352022-04-28 Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions Arsène, Simon Couty, Claire Faddeenkov, Igor Go, Natacha Granjeon-Noriot, Solène Šmít, Daniel Kahoul, Riad Illigens, Ben Boissel, Jean-Pierre Chevalier, Aude Lehr, Lorenz Pasquali, Christian Kulesza, Alexander Nat Commun Article Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI. Ratio-based efficacy metrics are only impacted under strict lockdown whereas absolute benefit already is with intermediate NPIs (eg. mask-wearing). Consequently, despite NPI, trials may meet their relative efficacy endpoints (provided recruitment hurdles can be overcome) but are difficult to assess with respect to clinical relevance. These results advocate to report a variety of metrics for benefit assessment, to use adaptive trial design and adapted statistical analyses. They also question eligibility criteria misaligned with the actual disease burden. Nature Publishing Group UK 2022-04-13 /pmc/articles/PMC9008035/ /pubmed/35418135 http://dx.doi.org/10.1038/s41467-022-29534-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Arsène, Simon
Couty, Claire
Faddeenkov, Igor
Go, Natacha
Granjeon-Noriot, Solène
Šmít, Daniel
Kahoul, Riad
Illigens, Ben
Boissel, Jean-Pierre
Chevalier, Aude
Lehr, Lorenz
Pasquali, Christian
Kulesza, Alexander
Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions
title Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions
title_full Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions
title_fullStr Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions
title_full_unstemmed Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions
title_short Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions
title_sort modeling the disruption of respiratory disease clinical trials by non-pharmaceutical covid-19 interventions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9008035/
https://www.ncbi.nlm.nih.gov/pubmed/35418135
http://dx.doi.org/10.1038/s41467-022-29534-8
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