Pemivibart is less active against recent SARS-CoV-2 JN.1 sublineages
Bibliographic record
Abstract
Abstract Protection from COVID-19 vaccination is suboptimal in many immunocompromised individuals. In March 2024, the Food and Drug Administration issued an Emergency Use Authorization for pemivibart (Permagard/VYD222), an engineered human monoclonal antibody, for pre-exposure prophylaxis in this vulnerable population. However, SARS-CoV-2 has since evolved extensively, resulting in multiple Omicron JN.1 sublineages. We therefore evaluated the in vitro neutralizing activity of pemivibart against the prevalent forms of JN.1, including KP.2, KP.3, KP.2.3, LB.1, and, importantly, KP.3.1.1, which is now expanding most rapidly. A panel of VSV-based pseudoviruses representing major JN.1 sublineages was generated to assess their susceptibility to pemivibart neutralization in vitro. Structural analyses were then conducted to understand the impact of specific spike mutations on the virus-neutralization results. Pemivibart neutralized both JN.1 and KP.2 in vitro with comparable activity, whereas its potency was decreased slightly against LB.1, KP.2.3, and KP.3 but substantially against KP.3.1.1. Critically, the 50% inhibitory concentration of pemivibart against KP.3.1.1 was ∼6 µg/mL, or ∼32.7 fold higher than that of JN.1 in our study. Structural analyses suggest that Q493E and the S31-deletion mutations in viral spike contribute to the antibody evasion, with the latter having a more pronounced effect. Our findings show that pemivibart has lost substantial neutralizing activity in vitro against KP.3.1.1, the most rapidly expanding lineage of SARS-CoV-2 today. Close monitoring of its clinical efficacy is therefore warranted. These results also highlight the imperative to expand our arsenal of preventive agents to protect millions of immunocompromised individuals who could not respond robustly to COVID-19 vaccines.
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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 | 0.000 |
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".