Anthracyclines inhibit SARS-CoV-2 infection
Bibliographic record
Abstract
Vaccines and drugs are two effective medical interventions to mitigate SARS-CoV-2 infection. Three SARS-CoV-2 inhibitors, remdesivir, paxlovid, and molnupiravir, have been approved for treating COVID-19 patients, but more are needed, because each drug has its limitation of usage and SARS-CoV-2 constantly develops drug resistance mutations. In addition, SARS-CoV-2 drugs have the potential to be repurposed to inhibit new human coronaviruses, thus help to prepare for future coronavirus outbreaks. We have screened a library of microbial metabolites to discover new SARS-CoV-2 inhibitors. To facilitate this screening effort, we generated a recombinant SARS-CoV-2 Delta variant carrying the nano luciferase as a reporter for measuring viral infection. Six compounds were found to inhibit SARS-CoV-2 at the half maximal inhibitory concentration (IC50) below 1 μM, including the anthracycline drug aclarubicin that markedly reduced viral RNA-dependent RNA polymerase (RdRp)-mediated gene expression, whereas other anthracyclines inhibited SARS-CoV-2 by activating the expression of interferon and antiviral genes. As the most commonly prescribed anti-cancer drugs, anthracyclines hold the promise of becoming new SARS-CoV-2 inhibitors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".