Determining the antiviral activity of two polyene macrolide antibiotics following treatment in Kidney Cells infected with SARS-CoV-2
Bibliographic record
Abstract
Abstract Background There has been much speculation that polyene macrolide antibiotics, such as amphotericin B (AmB) and Nystatin (NYS) may have antiviral activity against several viruses including SARS-CoV-2. Objective The objective of this short communication was to determine the antiviral activity of two polyene macrolides, AmB and NYS, following treatment in kidney cells infected with SARS-CoV-2. Methods A serial dilution of AmB, NYS, and irbesartan (a drug known to bind to the ACE-2 receptor as a positive control) were then added (n=4 at each concentration) to the infected Vero’76 kidney cells in 100 µL media. Cells were also examined for contamination at 24 hours, and for cytopathic effect (CPE) and cytotoxicity (if noticeable) under a microscope at 48 hours. In a second study, AmB and Remdesivir were incubated in kidney cells infected with the virus and inhibition of the virus was determined by an immunoassay. Results and Conclusions Amphotericin B (AmB) showed a significant reduction in the TCID50 titer, with the 50% effective concentration (EC50) of 1.24 µM, which was 2.5 times lower than the cytotoxicity concentration. NYS and Irbesartan both exhibited substantially less active and would not be considered a suitable choice for further investigations. In addition, when measuring viral inhibition by immunoassay, AmB was significantly more potent than remdesivir (EC50 31.8 nM vs. 1.15 µM). Taken together, these preliminary findings suggest that AmB may have significant activity against SARS-CoV-2. However, further cell and animal studies are warranted.
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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) | 0.001 | 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".