Ocrelizumab exposure in relapsing–remitting multiple sclerosis: 10-year analysis of the phase 2 randomized clinical trial and its extension
Bibliographic record
Abstract
Open-label extension (OLE) studies help inform long-term safety and efficacy of disease-modifying therapies in multiple sclerosis (MS). We report exploratory analyses from a phase 2 trial on the longest follow-up to date of ocrelizumab-treated patients with relapsing-remitting MS (RRMS). The primary treatment period (PTP) comprised four 24-week treatment cycles; participants were randomized to double-blind ocrelizumab (2000 mg or 600 mg), placebo, or interferon β-1a (open label) for one cycle, then dose-blinded ocrelizumab 1000 mg or 600 mg for the remaining cycles. The PTP was followed by consecutive assessed and unassessed treatment-free periods (TFPs) and then the OLE (ocrelizumab 600 mg every 24 weeks). Safety and efficacy were prospectively assessed. Of 220 participants randomized, 183 (84%) completed the PTP. After the TFP, 103 entered OLE (median OLE ocrelizumab exposure 6.5 years). Most common adverse events across all periods were infusion-related reactions. MRI activity, annualized relapse rate, and confirmed disability progression (CDP) rates remained low throughout. During the assessed TFP, there was a trend toward less and later B-cell repletion, and later CDP, for patients randomized to ocrelizumab; MRI activity was observed in 16.3% of patients, the earliest 24 weeks after the last ocrelizumab dose. This is the longest follow-up of ocrelizumab-treated patients with RRMS, with no new safety signals emerging during an observation period from 2008 to 2020. Results reinforce the sustained efficacy of long-term ocrelizumab. Reduced disease activity was maintained following interruption of 6-month dosing cycles, with no evidence of rebound.
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".