Inhibitory effects of molnupiravir on Crimean–Congo hemorrhagic fever virus polymerase
Bibliographic record
Abstract
Abstract The order Bunyavirales encompasses a diverse group of segmented negative-sense RNA viruses, many of which can cause severe human disease. Their global distribution paired with multiple routes of transmission by arthropods and rodents cause risks to public health. However, effective antiviral drugs are not yet approved. Molnupiravir is an orally available prodrug of β-D-N4-hydroxycytidine (NHC) and shows a broad spectrum of antiviral activity. This includes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and also several members of the Bunyavirales, such as Crimean–Congo hemorrhagic fever virus (CCHFV). For SARS-CoV-2, the active triphosphate form of NHC (NHC-TP) targets the viral RNA-dependent RNA polymerase (RdRp) and causes lethal mutagenesis; however, the mechanism of inhibition of CCHFV RdRp remains elusive. To address this problem, we employed a combination of biochemical studies, structural modeling, and cell-based, antiviral assays. The results of this study support a unifying mechanism consistent with lethal mutagenesis. Binding and/or incorporation of NHC-TP by CCHFV RdRp is likely facilitated by a conserved glutamine in close proximity to the active site. Next-generation sequencing revealed that NHC induced predominantly G-to-A and C-to-U transitions in CCHFV. Collectively, these results provide mechanistic evidence to consider the development of mutagenic nucleotides as possible treatments for CCHFV infection.
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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".