D.5 Efgartigimod treatment in participants with anti-acetylcholine receptor seronegative generalized myasthenia Myasthenia Gravis clinical studies
Bibliographic record
Abstract
Background: Antibodies directed against acetylcholine receptor (AChR) are absent in approximately 15% of patients with gMG. Approved treatment options represent an unmet need in the AChR-antibody (Ab)- gMG population. Efgartigimod is an immunoglobulin G1 (IgG1) antibody Fc fragment that selectively reduces IgG levels by blocking neonatal Fc receptor (FcRn)-mediated IgG recycling. Here, we describe efgartigimod efficacy in AChR-Ab- participants with gMG receiving either efgartigimod IV or subcutaneous (SC) efgartigimod PH20 (coformulated with recombinant human hyaluronidase PH20) across clinical studies. Methods: Post hoc analyses were conducted to examine efficacy and safety of efgartigimod IV and/or efgartigimod PH20 SC in AChR-Ab- participants in ADAPT/ADAPT+ and ADAPT-SC/ADAPT-SC+ trials. Results: Among pooled AChR-Ab- participants (n=56), mean (SE) MG-ADL total score improvement from baseline to Week 3 was -3.7 (Cycle 1: 0.44 [n=55]). Consistent MG-ADL improvements occurred with repeated cycles. Clinically meaningful improvements (CMI; ≥2-point MG-ADL decrease) occurred in 76.4% (n=42/55) of participants (Cycle 1, Week 3). In Cycle 1, 23.2% (n=13/56) of participants achieved minimal symptom expression (MG-ADL 0-1). Similar efficacy results occurred across all cycles. Overall safety profile was similar between AChR-Ab+ and AChR-Ab- participants. Conclusions: Both efgartigimod IV and efgartigimod PH20 SC were well tolerated and led to CMI in participants with AChR-Ab- 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".