Will Auranofin Become a Golden New Treatment Against COVID-19?
Bibliographic record
Abstract
Auranofin is an FDA-approved disease-modifying anti-rheumatic drug that has been used for decades for treatment of rheumatoid arthritis. This gold(I) compound has anti-inflammatory properties because it reduces IL-6 expression via inhibition of the NF-κB-IL-6-STAT3 signaling pathway. Also, by inhibiting redox enzymes such as thioredoxin reductase, auranofin increases cellular oxidative stress and promotes apoptosis. Auranofin also possesses antiviral properties. Recently, it was reported that auranofin reduced by 95% SARS-CoV-2 RNA in infected human cells in vitro and decreased SARS-CoV-2-induced cytokine expression, including IL-6. During SARS-CoV-2 infection, a cytokine storm involving IL-6 increases severity of illness and worsens prognosis. Therefore, auranofin could, in our point of view, reduce pathology due to SARS-CoV-2-induced IL-6. COVID-19 is a rapidly-evolving respiratory disease now distributed worldwide. Strikingly high numbers of new COVID-19 cases are reported daily. We have begun a race to vaccinate people, but due to the complex logistics of this effort, the virus will continue to spread before all humans can be immunized, and new variants that may be less well contained by current vaccines are of concern. The COVID-19 pandemic has overwhelmed health care systems and new treatments to reduce mortality are urgently needed. We encourage to further evaluate the potential of auranofin in the treatment of COVID-19 in vitro and in animal models of SARS-CoV-2 infection and, if preliminary data are promising, in clinical trials with COVID-19 patients. In our opinion, auranofin has the potential to become a valuable addition to available therapies in this pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".