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Modeling remdesivir antiviral efficacy in COVID-19 hospitalized patients of the randomized, controlled, open-label DisCoVeRy trial (preprint)
medrxiv; 2021.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2021.10.19.21265209
ABSTRACT
Despite several clinical studies, the antiviral efficacy of remdesivir in COVID-19 hospitalized patients remains controversial. We analyzed nasopharyngeal normalized viral loads collected in the 29 days following randomization from 665 hospitalized patients included in the DisCoVeRy trial, allocated to either standard of care (SoC, N=329) or SoC + remdesivir for 10 days (N=336). We used a mathematical model to reconstruct viral kinetic profiles and estimate the antiviral efficacy of remdesivir in reducing viral production. To identify factors associated with viral kinetics, additional analyses were conducted stratified either on time of treatment initiation ([≤] or > 7 days since symptom onset) or viral load at randomization (< or [≥] 3.5 log10 copies/104 cells). In our model, remdesivir reduced viral production by 2-fold on average (95%CI 1.5-3.2). Using the estimated parameter of the model, simulations predict that remdesivir reduces time to viral clearance by 0.7 day compared to SoC, with large inter-individual variabilities (Inter-Quartile Range, IQR 0.0-1.3 days). Exploratory analyses suggest that remdesivir had a larger impact in patients with a high viral load at randomization, reducing viral production by 5-fold on average (95%CI 2.8-25), leading to a predicted median reduction in the time to viral clearance of 2.4 days (IQR 0.9-4.5 days). In summary, our model shows that remdesivir reduces viral production from infected cells by a factor 2, leading to a median reduction of 0.7 days in the time to viral clearance compared to SoC. The efficacy was larger in patients with high level of viral load at treatment initiation. One sentence summaryRemdesivir reduces the time to SARS-CoV-2 clearance by 1 day in hospitalized patients, and up to 3 days in those with high viral load at admission.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Main subject:
COVID-19
Language:
English
Year:
2021
Document Type:
Preprint
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