Molnupiravir promotes SARS-CoV-2 mutagenesis via the RNA template
Bibliographic record
Abstract
The RNA-dependent RNA polymerase of the severe acute respiratory syndrome coronavirus 2 is an important target in current drug development efforts for the treatment of coronavirus disease 2019. Molnupiravir is a broad-spectrum antiviral that is an orally bioavailable prodrug of the nucleoside analogue β-D-N 4 -hydroxycytidine (NHC). Molnupiravir or NHC can increase G to A and C to U transition mutations in replicating coronaviruses. These increases in mutation frequencies can be linked to increases in antiviral effects; however, biochemical data of molnupiravir-induced mutagenesis have not been reported. Here we studied the effects of the active compound NHC 5'-triphosphate (NHC-TP) against the purified severe acute respiratory syndrome coronavirus 2 RNA-dependent RNA polymerase complex. The efficiency of incorporation of natural nucleotides over the efficiency of incorporation of NHC-TP into model RNA substrates followed the order GTP (12,841) > ATP (424) > UTP (171) > CTP (30), indicating that NHC-TP competes predominantly with CTP for incorporation. No significant inhibition of RNA synthesis was noted as a result of the incorporated monophosphate in the RNA primer strand. When embedded in the template strand, NHC-monophosphate supported the formation of both NHC:G and NHC:A base pairs with similar efficiencies. The extension of the NHC:G product was modestly inhibited, but higher nucleotide concentrations could overcome this blockage. In contrast, the NHC:A base pair led to the observed G to A (G:NHC:A) or C to U (C:G:NHC:A:U) mutations. Together, these biochemical data support a mechanism of action of molnupiravir that is primarily based on RNA mutagenesis mediated via the template strand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".