The Nucleoside/Nucleotide Analogs Tenofovir and Emtricitabine Are Inactive against SARS-CoV-2
Bibliographic record
Abstract
The urgent response to the COVID-19 pandemic required accelerated evaluation of many approved drugs as potential antiviral agents against the causative pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using cell-based, biochemical, and modeling approaches, we studied the approved HIV-1 nucleoside/tide reverse transcriptase inhibitors (NRTIs) tenofovir (TFV) and emtricitabine (FTC), as well as prodrugs tenofovir alafenamide (TAF) and tenofovir disoproxilfumarate (TDF) for their antiviral effect against SARS-CoV-2. A comprehensive set of in vitro data indicates that TFV, TAF, TDF, and FTC are inactive against SARS-CoV-2. None of the NRTIs showed antiviral activity in SARS-CoV-2 infected A549-hACE2 cells or in primary normal human lung bronchial epithelial (NHBE) cells at concentrations up to 50 µM TAF, TDF, FTC, or 500 µM TFV. These results are corroborated by the low incorporation efficiency of respective NTP analogs by the SARS-CoV-2 RNA-dependent-RNA polymerase (RdRp), and lack of the RdRp inhibition. Structural modeling further demonstrated poor fitting of these NRTI active metabolites at the SARS-CoV-2 RdRp active site. Our data indicate that the HIV-1 NRTIs are unlikely direct-antivirals against SARS-CoV-2, and clinicians and researchers should exercise caution when exploring ideas of using these and other NRTIs to treat or prevent COVID-19.
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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".