Exploring Repurposing Potential of Existing Drugs in the Management of COVID-19 Epidemic: A Critical Review
Bibliographic record
Abstract
studies identified various molecules as possible therapies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), these findings call for substantiation by human studies. We conducted this review aiming at reporting evidences on therapies used so far globally for management of COVID-19 in clinical settings. We searched electronic databases as PubMed, Google Scholar, EMBASE and extracted 612 possible studies as on May 31, 2020. We included original studies of any epidemiological design done on human COVID-19 patients and measured clinical outcomes. Finally, following removal of duplicates and studies meeting exclusion criteria, we derived 22 studies, of which eight were clinical trials, seven were case reports and case series, and seven were observational studies. The most reported therapies were hydroxychloroquine (HCQ) (eight studies) and lopinavir/ritonavir (four studies). We conclude from the evidence generated so far that interferon combined with antivirals, remdesivir, umifenovir and favipiravir were mostly associated with better clinical outcomes. The therapeutic effect of HCQ was established initially by two clinical trials; one of them showing a reinforcing effect by azithromycin but subsequent studies did not elicit any effectiveness rather increased rate of adverse events was reported. Lopinavir/ritonavir was found beneficial when administered with interferon and ribavirin, but one clinical trial on its sole use proved contrary. As many clinical trials are in process, we expect to get concrete evidences on repurposing of existing drugs based on less biased, high powered studies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".