LY-CoV1404 (bebtelovimab) potently neutralizes SARS-CoV-2 variants
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-neutralizing monoclonal antibodies (mAbs) can reduce the risk of hospitalization from coronavirus disease 2019 (COVID-19) when administered early. However, SARS-CoV-2 variants of concern (VOCs) have negatively affected therapeutic use of some authorized mAbs. Using a high-throughput B cell screening pipeline, we isolated LY-CoV1404 (bebtelovimab), a highly potent SARS-CoV-2 spike glycoprotein receptor binding domain (RBD)-specific antibody. LY-CoV1404 potently neutralizes authentic SARS-CoV-2, B.1.1.7, B.1.351, and B.1.617.2. In pseudovirus neutralization studies, LY-CoV1404 potently neutralizes variants, including B.1.1.7, B.1.351, B.1.617.2, B.1.427/B.1.429, P.1, B.1.526, B.1.1.529, and the BA.2 subvariant. Structural analysis reveals that the contact residues of the LY-CoV1404 epitope are highly conserved, except for N439 and N501. The binding and neutralizing activity of LY-CoV1404 is unaffected by the most common mutations at these positions (N439K and N501Y). The broad and potent neutralization activity and the relatively conserved epitope suggest that LY-CoV1404 has the potential to be an effective therapeutic agent to treat all known variants.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".