P.072 Safety and effectiveness of Nipocalimab in adolescent participants in the open label phase 2/3 Vibrance-mg clinical study
Bibliographic record
Abstract
Background: Nipocalimab (a fully human, effectorless anti-neonatal Fc receptor (FcRn) monoclonal antibody) may ameliorate gMG disease manifestations by selectively targeting FcRn IgG recycling and lowering IgG, including pathogenic autoantibodies in generalized myasthenia gravis (gMG). The objective was to evaluate the effectiveness and safety of intravenous nipocalimab added to background standard-of-care therapy in adolescents with gMG. Methods: Seropositive patients (12-<18 years) with gMG (MGFA Class II-IV) on stable therapy but inadequately controlled, were enrolled in a 24-week open label study. Nipocalimab was administered as a 30 mg/kg IV loading dose followed by 15 mg/kg IV every 2 Weeks. Results: Seven adolescents were enrolled; 5 completed 24-weeks of dosing. The mean(SD) age was 14.1(1.86) years; seven were anti-AChR+, six were female. Mean(SD) baseline MG-ADL/QMG scores were 4.29(2.430)/12.50(3.708). Nipocalimab showed a significant reduction in total serum IgG at week-24; the mean(SD) change from baseline to week-24 for total serum IgG was -68.98%(7.561). The mean(SD) change in MG-ADL/QMG scores at week-24 was -2.40(0.418)/-3.80(2.683); 4 of 5 patients achieved minimum symptom expression (MG-ADL score 0-1) by week-24. Nipocalimab was well-tolerated; there were no serious adverse events. There were no clinically meaningful laboratory changes. Conclusions: Nipocalimab demonstrated efficacy and safety in this 6-month trial in seropositive adolescents with gMG.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".