P.100 A randomized, open-label study on the effect of Nipocalimab on vaccine responses in healthy participants
Bibliographic record
Abstract
Background: Nipocalimab is a human IgG1 monoclonal antibody targeting FcRn that selectively reduces IgG levels without impacting antigen presentation, T- and B-cell functions. This study describes the effect of nipocalimab on vaccine response. Methods: Open-label, parallel, interventional study randomized participants 1:1 to receive intravenous 30mg/kg nipocalimab at Week0 and 15mg/kg at Week2 and Week4 (active) or no drug (control). On Day 3, participants received Tdap and PPSV®23 vaccinations and were followed through Wk16. Results: Twenty-nine participants completed the study and are included (active, n=15; control, n=14). Participants with a positive anti-tetanus IgG response was comparable between groups at Wk2 and Wk16, but lower at Wk4 (nipocalimab 3/15 [20%] vs control 7/14 [50%]; P=0.089). All maintained anti-tetanus IgG above the protective threshold (0.16IU/mL) through Wk16. While anti-pneumococcal-capsular-polysaccharide (PCP) IgG levels were lower during nipocalimab treatment, the percent increase from baseline at Wk2 and Wk16 was comparable between groups. Post-vaccination, anti-PCP IgG remained above 50mg/L and showed a 2-fold increase from baseline throughout the study in both groups. Nipocalimab co-administration with vaccines was safe and well-tolerated. Conclusions: These findings suggest that nipocalimab does not impact the development of an adequate IgG response to T-cell–dependent/independent vaccines and that nipocalimab-treated patients can follow recommended vaccination schedules.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".