Mechanism and spectrum of inhibition of viral polymerases by 2′-deoxy-2′-β-fluoro-4′-azidocytidine or azvudine
Bibliographic record
Abstract
The therapeutic value of antiviral nucleoside analogs was highlighted during the coronavirus disease 2019 (COVID-19) pandemic, with remdesivir and molnupiravir repurposed for their broad-spectrum antiviral activity. The cytidine analog azvudine (FNC) has recently gained attention as a potential treatment for human immunodeficiency virus type 1 (HIV-1) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Considering the distinct substrate specificities of HIV-1 reverse transcriptase (RT) and SARS-CoV-2 RNA-dependent RNA polymerase (RdRp), a unifying mechanism of inhibition remains elusive. Here, we assessed the inhibitory effects of FNC's active triphosphate form, FNC-TP, across several viral polymerases. The relative efficiency of FNC-TP incorporation followed the order: HIV-1 RT > hepatitis C virus (HCV) RdRp > respiratory syncytial virus (RSV) RdRp > dengue virus type 2 (DENV-2) RdRp ≫ SARS-CoV-2 RdRp. Its incorporation caused chain-termination in all polymerases tested. Antiviral activity against HIV-1 has previously been demonstrated and is here shown with DENV-2. Collectively, the data show that inhibition of viral polymerases by FNC-TP can translate to antiviral activity against both retroviruses and RNA viruses, but the link is not evident for SARS-CoV-2. FNC-TP is a poor substrate for SARS-CoV-2 RdRp, and FNC lacks significant antiviral activity against SARS-CoV-2 in cell culture.
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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".