Coadministration of Anti-Viral Monoclonal Antibodies With Routine Pediatric Vaccines and Implications for Nirsevimab Use: A White Paper
Bibliographic record
Abstract
Routine childhood vaccinations are key for the protection of children from a variety of serious and potentially fatal diseases. Current pediatric vaccine schedules mainly cover active vaccines. Active vaccination in infants is a highly effective approach against several infectious diseases; however, thus far, for some important viral pathogens, including respiratory syncytial virus (RSV), vaccine development and license by healthcare authorities have not been accomplished. Nirsevimab is a human-derived, highly potent monoclonal antibody (mAb) with an extended half-life for RSV prophylaxis in all infants. In this manuscript, we consider the potential implications for the introduction of an anti-viral mAb, such as nirsevimab, into the routine pediatric vaccine schedule, as well as considerations for coadministration. Specifically, we present evidence on the general mechanism of action of anti-viral mAbs and experience with palivizumab, the only approved mAb for the prevention of RSV infection in preterm infants, infants with chronic lung disease of prematurity and certain infants with hemodynamically significant heart disease. Palivizumab has been used for over two decades in infants who also receive routine vaccinations without any alerts concerning the safety and efficacy of coadministration. Immunization guidelines (Advisory Committee on Immunization Practices, Joint Committee on Vaccination and Immunization, National Advisory Committee on Immunization, Centers for Disease Control and Prevention, American Academy of Pediatrics, The Association of the Scientific Medical Societies in Germany) support coadministration of palivizumab with routine pediatric vaccines, noting that immunobiologics, such as palivizumab, do not interfere with the immune response to licensed live or inactivated active vaccines. Based on the mechanism of action of the new generation of anti-viral mAbs, such as nirsevimab, which is highly specific targeting viral antigenic sites, it is unlikely that it could interfere with the immune response to other vaccines. Taken together, we anticipate that nirsevimab could be concomitantly administered to infants with routine pediatric vaccines during the same clinic visit.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".