Efficient incorporation and template-dependent polymerase inhibition are major determinants for the broad-spectrum antiviral activity of remdesivir
Bibliographic record
Abstract
Remdesivir (RDV) is a direct-acting antiviral agent that is approved in several countries for the treatment of coronavirus disease 2019 caused by the severe acute respiratory syndrome coronavirus 2. RDV exhibits broad-spectrum antiviral activity against positive-sense RNA viruses, for example, severe acute respiratory syndrome coronavirus and hepatitis C virus, and nonsegmented negative-sense RNA viruses, for example, Nipah virus, whereas segmented negative-sense RNA viruses such as influenza virus or Crimean-Congo hemorrhagic fever virus are not sensitive to the drug. The reasons for this apparent efficacy pattern are unknown. Here, we expressed and purified representative RNA-dependent RNA polymerases and studied three biochemical parameters that have been associated with the inhibitory effects of RDV-triphosphate (TP): (i) selective incorporation of the nucleotide substrate RDV-TP, (ii) the effect of the incorporated RDV-monophosphate (MP) on primer extension, and (iii) the effect of RDV-MP in the template during incorporation of the complementary UTP. We found a strong correlation between antiviral effects and efficient incorporation of RDV-TP. Inhibition in primer extension reactions was heterogeneous and usually inefficient at higher NTP concentrations. In contrast, template-dependent inhibition of UTP incorporation opposite the embedded RDV-MP was seen with all polymerases. Molecular modeling suggests a steric conflict between the 1'-cyano group of the inhibitor and residues of the structurally conserved RNA-dependent RNA polymerase motif F. We conclude that future efforts in the development of nucleotide analogs with a broader spectrum of antiviral activities should focus on improving rates of incorporation while capitalizing on the inhibitory effects of a bulky 1'-modification.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".