Update on Antiviral Strategies Against COVID-19: Unmet Needs and Prospects
Bibliographic record
Abstract
By December 2020, the COVID-19 pandemic had caused more than 74 million confirmed cases and 1.6 million related deaths around the world. However, only a few drugs have been approved in certain areas and for use in conditional patients, and the vaccine candidates were only recently approved or authorized for emergency use without being fully implemented worldwide, suggesting that we are yet to reach effective control of the current outbreak as its uninhibited transmission continues precariously. Over the past few months, several therapeutic candidates have been proven ineffective in large clinical trials, while some other agents exhibited promising preliminary results. Meanwhile, the investigation of SARS-CoV-2-specific antivirals is underway. Despite still being preclinical, these agents could be beneficial for the long-term control of COVID-19 and deserve more research focus. In this article, we update the current status of therapeutic candidates that have been examined for COVID-19 management, including the virus-targeting inhibitors and host-targeting agents, with their antiviral efficacy in vitro, in vivo, and in clinical studies. Finally, we highlight the current challenges and future prospect of developing potent therapeutic agents against COVID-19.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".