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

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.

Incomplete antiviral treatment may induce longer durations of viral shedding during SARS-CoV-2 infection

“…This is because the treatment with RDV was initiated at day 0.5, which was close to the peak viral load in nose (i.e., day 0.680; Fig 1B ). Because only a very small fraction of target cells remain uninfected after the viral load peak ( 29 , 30 ), the number of ongoing de novo infections that the RDV can potentially interrupt is limited. Therefore, even if RDV blocked virus production with relatively high efficacy up to 100%, the viral load decay was not significantly influenced ( Figs 1E and S1A ).…”

“…There are various studies using mathematical models that can explain SARS-CoV-2 infection dynamics and give insight into treatment strategy ( 29 , 42 , 43 , 44 ). Another mathematical study of nasal viral load also showed a longer duration of virus shedding under RDV treatment; however, that study did not directly estimate treatment efficacy ( 45 ).…”

Comparing antiviral strategies against COVID-19 via multiscale within-host modelling

“…They showed that if drugs such as remdesivir are administered very early, this may help control viral load, but may not have a major effect in severely ill patients. Iwanami et al also introduced a mathematical model to describe the within-host viral dynamics of SARS-CoV-2 and demonstrated that late timing of treatment initiation can mask the effect of antivirals in clinical studies of COVID-19 [7]. However, none of these models have included the impact of the immune response directly into their model, which plays an important role in the outcomes of the infection.…”

Safely return to schools and offices: early and frequent screening with high sensitivity antigen tests effectively identifies COVID-19 patients

“…To describe the temporal change in viral load of each infected individual in the school or the office, a mathematical model describing the viral dynamics of SARS-CoV-2 was employed as in our previous studies Ejima, Kim, Ludema, et al, 2021;Iwanami et al, 2021;Jeong et al, 2021;Kim et al, 2021). Briefly explaining, the model is composed of two compartments: the viral load (copies/mL) at time ‫,ݐ‬ ܸሺ‫ݐ‬ሻ, and the ratio between the number of uninfected cells at time to and that at time 0, ݂ሺ‫ݐ‬ሻ.…”