Remdesivir: A beacon of hope from Ebola virus disease to <scp>COVID</scp>‐19
Bibliographic record
Abstract
Since the emergence of coronavirus disease 2019 (Covid-19), many studies have been performed to characterize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and find the optimum way to combat this virus. After suggestions and assessments of several therapeutic options, remdesivir (GS-5734), a direct-acting antiviral drug previously tested against Ebola virus disease, was found to be moderately effective and probably safe for inhibiting SARS-CoV-2 replication. Finally, on 1 May 2020, remdesivir (GS-5734) was granted emergency use authorization as an investigational drug for the treatment of Covid-19 by the Food and Drug Administration. However, without a doubt, there are challenging days ahead. Here, we provide a review of the latest findings (based on preprints, post-prints, and news releases in scientific websites) related to remdesivir efficacy and safety for the treatment of Covid-19, along with covering remdesivir history from bench-to-bedside, as well as an overview of its mechanism of action. In addition, active clinical trials, as well as challenging issues related to the future of remdesivir in Covid-19, are covered. Up to the date of writing this review (19 May 2020), there is one finished randomized clinical trial and two completed non-randomized studies, in addition to some ongoing studies, including three observational studies, two expanded access studies, and seven active clinical trials registered on the clinicaltrials.gov and isrctn.com websites. Based on these studies, it seems that remdesivir could be an effective and probably safe treatment option for Covid-19. However, more randomized controlled studies are required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".