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

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  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101231360 Publication Model: eCollection Cited Medium: Internet ISSN: 1549-1676 (Electronic) Linking ISSN: 15491277 NLM ISO Abbreviation: PLoS Med Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science, [2004]-
    • Subject Terms:
      Randomized Controlled Trials as Topic* ; Sample Size* ; COVID-19 Drug Treatment*; Antiviral Agents/*therapeutic use; Humans ; Models, Biological ; SARS-CoV-2 ; Treatment Outcome ; Viral Load ; Virus Replication ; Virus Shedding
    • Abstract:
      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.
      Competing Interests: The authors have declared that no competing interests exist.
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    • Grant Information:
      R01 AI028433 United States AI NIAID NIH HHS; R01 OD011095 United States OD NIH HHS; U24 GM132013 United States GM NIGMS NIH HHS; 3U24GM132013 United States NH NIH HHS
    • Accession Number:
      0 (Antiviral Agents)
    • Publication Date:
      Date Created: 20210706 Date Completed: 20210714 Latest Revision: 20231107
    • Publication Date:
      20240105
    • Accession Number:
      PMC8259968
    • Accession Number:
      10.1371/journal.pmed.1003660
    • Accession Number:
      34228712