Safety and immunogenicity of a new formulation of interferon β-1a (Rebif® New Formulation) in a Phase IIIb study in patients with relapsing multiple sclerosis: 96-week results
Bibliographic record
Abstract
BACKGROUND: A new formulation of subcutaneous (s.c.) interferon-beta-1a has been developed (Rebif New Formulation, RNF), produced without fetal bovine serum and without human serum albumin as an excipient, with the aim of improving injection tolerability, and reducing immunogenicity. OBJECTIVES: This article reports 96-week analyses of a Phase IIIb, open-label study of the safety and immunogenicity of RNF compared with historical (EVIDENCE study) and recent (REGARD study) data on the original formulation. METHODS: Patients with relapsing multiple sclerosis (McDonald criteria) and an Expanded Disability Status Scale score < 6.0 received RNF, 44 microg s.c. three times weekly. RESULTS: The proportion of neutralizing antibody-positive (NAb+) patients (serum NAb status >or=20 neutralizing units/mL) at week 96 (last observation carried forward; primary endpoint) was 17.4% (exact 95% confidence interval [CI]: 13.0-22.5), compared with 21.4% (95% CI: 17.2-26.2) in the EVIDENCE study, and 27.3% (95% CI: 22.8-32.1) in the REGARD study. The proportion of patients NAb+ at any time during the 96 weeks was 18.9% (95% CI: 14.4-24.2), compared with 27.1% (95% CI: 22.4-32.2) and 33.7% (95% CI: 28.9-38.7), respectively. Most pre-specified categories of adverse events were reported by patients in the RNF study at a similar or lower proportion than in the EVIDENCE and REGARD studies. Injection-site reactions were experienced by fewer patients than in the EVIDENCE and REGARD studies. CONCLUSIONS: RNF has improved overall immunogenicity and safety profiles compared with the original formulation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".