P.045 Safety profile overview of Efgartigimod Clinical Trials in participants with diverse Diverse IgG-Mediated Autoimmune Diseases
Bibliographic record
Abstract
Background: Efgartigimod is a human IgG1 antibody Fc-fragment that reduces IgG autoantibody levels through FcRn blockade. This study reports safety of efgartigimod across IgG-mediated disorders. Methods: The safety of intravenous efgartigimod was evaluated in 204 efgartigimod-treated subjects with generalized myasthenia gravis (phase 3 ADAPT and 3-year open-label extension ADAPT+ trials), primary immune thrombocytopenia (phase 3 ADVANCE trial), or pemphigus (open-label phase 2 trial). These studies examined different efgartigimod doses (10–25 mg/kg), including cyclical dosing in generalized myasthenia gravis and continuous weekly dosing in primary immune thrombocytopenia and pemphigus. Results: Across all indications and doses studied, efgartigimod demonstrated a consistent safety profile, with treatment-emergent adverse event (TEAE) rates comparable to placebo (ADAPT, 77.4% efgartigimod/84.3% placebo; ADVANCE, 93.0% efgartigimod/95.6% placebo; and 85% in the pemphigus study). Most TEAEs were mild to moderate in severity. Discontinuation rates due to adverse events were consistently low (ADAPT, 3.6% efgartigimod/3.6% placebo; ADVANCE, 3.5% efgartigimod/2.2% placebo; and 3% of pemphigus study participants). In ADAPT+, no increases in TEAEs or infections occurred with additional efgartigimod dosing (19 cycles). Conclusions: Efgartigimod was well tolerated across indications and doses studied. Most TEAEs, including infections, were mild or moderate in severity and did not increase in frequency with recurrent dosing.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".