Long-term safety of cyclical rozanolixizumab in patients with generalized myasthenia gravis: Results from the Phase 3 MycarinG study and an open-label extension
Bibliographic record
Abstract
BACKGROUND: Generalized myasthenia gravis (gMG) is a rare, chronic, fluctuating and heterogeneous autoimmune disease requiring lifelong treatment. The Phase 3 MycarinG study demonstrated the efficacy and safety of one 6-week cycle of weekly rozanolixizumab in adult patients with gMG. Open-label extension studies demonstrated consistent symptom improvement over additional treatment cycles. OBJECTIVE: To present findings from pooled analyses on the long-term safety of repeated cycles of rozanolixizumab. METHODS: Data from the Phase 3 randomized MycarinG study (NCT03971422) and the ongoing open-label extension study MG0007 (NCT04650854) were pooled to assess safety outcomes during cyclical treatment, including incidence of any treatment-emergent adverse events (TEAEs), severe TEAEs, serious TEAEs and TEAEs leading to discontinuations. Additional analyses were performed for TEAEs, including headache, infections, and hypersensitivity reactions. RESULTS: At data cutoff (July 8, 2022), a total of 188 patients in MycarinG and MG0007 had received ≥1 treatment cycle with rozanolixizumab; total time in studies was 174.71 patient-years. Overall, 169/188 (89.9%) patients experienced any TEAE: 89/188 (47.3%) experienced any headache (including migraine, migraine with aura); 85/188 (45.2%) experienced an infection; 25/188 (13.3%) experienced a hypersensitivity reaction. One patient experienced an event of aseptic meningitis. The majority of AEs were mild-to-moderate in intensity, and incidence did not increase with repeated cyclic treatment. A total of 50/188 (26.6%) patients experienced severe TEAEs, the most common of which were MG worsening in 4/133 (3.0%) and 7/131 (5.3%) patients in the rozanolixizumab 7 mg/kg and rozanolixizumab 10 mg/kg groups, respectively, MG crisis in 0 and 4/131 (3.1%) patients, and headache in 1/133 (0.8%) and 7/131 (5.3%) patients. CONCLUSIONS: These pooled results, representing 174.71 patient-years in the studies, demonstrate that treatment with rozanolixizumab in patients with gMG was well tolerated, and TEAEs were consistent and did not increase in incidence over repeated cycles in this patient population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".