Nirsevimab: review of pharmacology, antiviral activity and emerging clinical experience for respiratory syncytial virus infection in infants
Bibliographic record
Abstract
Respiratory syncytial virus (RSV) is a leading cause of hospitalization and infant mortality worldwide. There are currently no approved vaccines against RSV, and immunoprophylaxis with the mAb palivizumab is limited to extremely vulnerable infants in resource-rich settings due to its high cost and the need for monthly injections throughout the RSV season. Nirsevimab (formerly MEDI8897) is a highly potent, long-acting, human, recombinant mAb that received approval for the prevention of RSV infection in newborns and infants during their first RSV season from the EMA and the UK's Medicines and Healthcare products Regulatory Agency in November 2022 based on positive results in Phase 2b and 3 clinical trials. Nirsevimab targets the highly conserved site Ø of the prefusion conformation of the RSV fusion (F) protein and contains a triple amino acid substitution in the Fc domain that extends its half-life, allowing for a single dose to cover a typical RSV season in regions with temperate climates. In this article I review key attributes of nirsevimab with an emphasis on pharmacology, pharmacokinetics, antiviral activity, and the potential for resistance and escape variants. I also summarize current progress in clinical trials and consider future research priorities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".