Identification of FDA-approved bifonazole as a SARS-CoV-2 blocking agent following a bioreporter drug screen
Bibliographic record
Abstract
We established a split nanoluciferase complementation assay to rapidly screen for inhibitors that interfere with binding of the receptor binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein with its target receptor, angiotensin-converting enzyme 2 (ACE2). After a screen of 1,200 US Food and Drug Administration (FDA)-approved compounds, we identified bifonazole, an imidazole-based antifungal agent, as a competitive inhibitor of RBD-ACE2 binding. Mechanistically, bifonazole binds ACE2 around residue K353, which prevents association with the RBD, affecting entry and replication of spike-pseudotyped viruses as well as native SARS-CoV-2 and its variants of concern (VOCs). Intranasal administration of bifonazole reduces lethality in K18-hACE2 mice challenged with vesicular stomatitis virus (VSV)-spike by 40%, with a similar benefit after live SARS-CoV-2 challenge. Our screen identified an antiviral agent that is effective against SARS-CoV-2 and VOCs such as Omicron that employ the same receptor to infect cells and therefore has high potential to be repurposed to control, treat, or prevent coronavirus disease 2019 (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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".