A cell-based assay to discover inhibitors of SARS-CoV-2 RNA dependent RNA polymerase
Bibliographic record
Abstract
Antiviral therapeutics is one effective avenue to control and end this devastating COVID-19 pandemic. The viral RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 has been recognized as a valuable target of antivirals. However, the cell-free SARS-CoV-2 RdRp biochemical assay requires the conversion of nucleotide prodrugs into the active triphosphate forms, which regularly occurs in cells yet is a complicated multiple-step chemical process in vitro, and thus hinders the utility of this cell-free assay in the rapid discovery of RdRp inhibitors. In addition, SARS-CoV-2 exoribonuclease provides the proof-reading capacity to viral RdRp, thus creates relatively high resistance threshold of viral RdRp to nucleotide analog inhibitors, which must be examined and evaluated in the development of this class of antivirals. Here, we report a cell-based assay to evaluate the efficacy of nucleotide analog compounds against SARS-CoV-2 RdRp and assess their tolerance to viral exoribonuclease-mediated proof-reading. By testing seven commonly used nucleotide analog viral polymerase inhibitors, Remdesivir, Molnupiravir, Ribavirin, Favipiravir, Penciclovir, Entecavir and Tenofovir, we found that both Molnupiravir and Remdesivir showed the strong inhibition of SARS-CoV-2 RdRp, with EC50 value of 0.22 μM and 0.67 μM, respectively. Moreover, our results suggested that exoribonuclease nsp14 increases resistance of SARS-CoV-2 RdRp to nucleotide analog inhibitors. We also determined that Remdesivir presented the highest resistance to viral exoribonuclease activity in cells. Therefore, we have developed a cell-based SARS-CoV-2 RdRp assay which can be deployed to discover SARS-CoV-2 RdRp inhibitors that are urgently needed to treat COVID-19 patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".