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Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

BACKGROUND: Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evid...

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Autores principales: Iwanami, Shoya, Ejima, Keisuke, Kim, Kwang Su, Noshita, Koji, Fujita, Yasuhisa, Miyazaki, Taiga, Kohno, Shigeru, Miyazaki, Yoshitsugu, Morimoto, Shimpei, Nakaoka, Shinji, Koizumi, Yoshiki, Asai, Yusuke, Aihara, Kazuyuki, Watashi, Koichi, Thompson, Robin N., Shibuya, Kenji, Fujiu, Katsuhito, Perelson, Alan S., Iwami, Shingo, Wakita, Takaji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259968/
https://www.ncbi.nlm.nih.gov/pubmed/34228712
http://dx.doi.org/10.1371/journal.pmed.1003660
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author Iwanami, Shoya
Ejima, Keisuke
Kim, Kwang Su
Noshita, Koji
Fujita, Yasuhisa
Miyazaki, Taiga
Kohno, Shigeru
Miyazaki, Yoshitsugu
Morimoto, Shimpei
Nakaoka, Shinji
Koizumi, Yoshiki
Asai, Yusuke
Aihara, Kazuyuki
Watashi, Koichi
Thompson, Robin N.
Shibuya, Kenji
Fujiu, Katsuhito
Perelson, Alan S.
Iwami, Shingo
Wakita, Takaji
author_facet Iwanami, Shoya
Ejima, Keisuke
Kim, Kwang Su
Noshita, Koji
Fujita, Yasuhisa
Miyazaki, Taiga
Kohno, Shigeru
Miyazaki, Yoshitsugu
Morimoto, Shimpei
Nakaoka, Shinji
Koizumi, Yoshiki
Asai, Yusuke
Aihara, Kazuyuki
Watashi, Koichi
Thompson, Robin N.
Shibuya, Kenji
Fujiu, Katsuhito
Perelson, Alan S.
Iwami, Shingo
Wakita, Takaji
author_sort Iwanami, Shoya
collection PubMed
description BACKGROUND: Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. METHODS AND FINDINGS: A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d(−1) (95% CI: 1.06 to 1.27 d(−1)), 0.777 d(−1) (0.716 to 0.838 d(−1)), and 0.450 d(−1) (0.378 to 0.522 d(−1)) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. CONCLUSIONS: In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.
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spelling pubmed-82599682021-07-19 Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study Iwanami, Shoya Ejima, Keisuke Kim, Kwang Su Noshita, Koji Fujita, Yasuhisa Miyazaki, Taiga Kohno, Shigeru Miyazaki, Yoshitsugu Morimoto, Shimpei Nakaoka, Shinji Koizumi, Yoshiki Asai, Yusuke Aihara, Kazuyuki Watashi, Koichi Thompson, Robin N. Shibuya, Kenji Fujiu, Katsuhito Perelson, Alan S. Iwami, Shingo Wakita, Takaji PLoS Med Research Article BACKGROUND: Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. METHODS AND FINDINGS: A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d(−1) (95% CI: 1.06 to 1.27 d(−1)), 0.777 d(−1) (0.716 to 0.838 d(−1)), and 0.450 d(−1) (0.378 to 0.522 d(−1)) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. CONCLUSIONS: In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model. Public Library of Science 2021-07-06 /pmc/articles/PMC8259968/ /pubmed/34228712 http://dx.doi.org/10.1371/journal.pmed.1003660 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Iwanami, Shoya
Ejima, Keisuke
Kim, Kwang Su
Noshita, Koji
Fujita, Yasuhisa
Miyazaki, Taiga
Kohno, Shigeru
Miyazaki, Yoshitsugu
Morimoto, Shimpei
Nakaoka, Shinji
Koizumi, Yoshiki
Asai, Yusuke
Aihara, Kazuyuki
Watashi, Koichi
Thompson, Robin N.
Shibuya, Kenji
Fujiu, Katsuhito
Perelson, Alan S.
Iwami, Shingo
Wakita, Takaji
Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study
title Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study
title_full Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study
title_fullStr Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study
title_full_unstemmed Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study
title_short Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study
title_sort detection of significant antiviral drug effects on covid-19 with reasonable sample sizes in randomized controlled trials: a modeling study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259968/
https://www.ncbi.nlm.nih.gov/pubmed/34228712
http://dx.doi.org/10.1371/journal.pmed.1003660
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