Remdesivir is a direct-acting antiviral that inhibits RNA-dependent RNA polymerase from severe acute respiratory syndrome coronavirus 2 with high potency
Bibliographic record
Abstract
Effective treatments for coronavirus disease 2019 (COVID-19) are urgently needed to control this current pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Replication of SARS-CoV-2 depends on the viral RNA-dependent RNA polymerase (RdRp), which is the likely target of the investigational nucleotide analogue remdesivir (RDV). RDV shows broad-spectrum antiviral activity against RNA viruses, and previous studies with RdRps from Ebola virus and Middle East respiratory syndrome coronavirus (MERS-CoV) have revealed that delayed chain termination is RDV's plausible mechanism of action. Here, we expressed and purified active SARS-CoV-2 RdRp composed of the nonstructural proteins nsp8 and nsp12. Enzyme kinetics indicated that this RdRp efficiently incorporates the active triphosphate form of RDV (RDV-TP) into RNA. Incorporation of RDV-TP at position i caused termination of RNA synthesis at position i +3. We obtained almost identical results with SARS-CoV, MERS-CoV, and SARS-CoV-2 RdRps. A unique property of RDV-TP is its high selectivity over incorporation of its natural nucleotide counterpart ATP. In this regard, the triphosphate forms of 2′-C-methylated compounds, including sofosbuvir, approved for the management of hepatitis C virus infection, and the broad-acting antivirals favipiravir and ribavirin, exhibited significant deficits. Furthermore, we provide evidence for the target specificity of RDV, as RDV-TP was less efficiently incorporated by the distantly related Lassa virus RdRp, and termination of RNA synthesis was not observed. These results collectively provide a unifying, refined mechanism of RDV-mediated RNA synthesis inhibition in coronaviruses and define this nucleotide analogue as a direct-acting antiviral.
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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) | 0.001 | 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 | 0.001 |
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".