Randomized, Double‐Blind, Placebo‐Controlled, Multiple‐Dose Studies to Assess the Safety and Efficacy of Elezanumab when Added to Standard of Care in Relapsing and Progressive Forms of Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Elezanumab is a monoclonal antibody that binds repulsive guidance molecule a (RGMa), an inhibitor of central nervous system regeneration after inflammation or injury. The aim was to assess the safety and efficacy of elezanumab in relapsing and progressive forms of multiple sclerosis (MS). METHODS: RADIUS-R and RADIUS-P were phase 2 trials in relapsing (RADIUS-R) or progressive (RADIUS-P) MS. Participants were randomized to intravenous elezanumab 400mg, 1800mg, or placebo every 4 weeks through week 48. The primary endpoint was the mean Overall Response Score (ORS). RESULTS: In RADIUS-R, 208 participants received elezanumab 400mg (n = 69), elezanumab 1800mg (n = 69), or placebo (n = 70). In RADIUS-P, 123 participants received elezanumab 400mg (n = 40), elezanumab 1800mg (n = 40), or placebo (n = 43). The primary endpoint of ORS was not met in either study. For RADIUS-R, mean ORS was -0.2 with effect size of -0.2 for elezanumab 400mg and -0.2 with effect size of -0.2 for elezanumab 1800mg. For RADIUS-P, mean ORS was 0.0 with effect size of 0.0 for elezanumab 400mg and 0.1 with effect size of 0.1 for elezanumab 1800mg. Elezanumab was well tolerated; the rate of serious adverse events was similar across treatment groups in both studies. Adverse events with ≥10% of elezanumab population were falls, urinary tract infections, headaches in RADIUS-R and RADIUS-P, and also fatigue, infusion-related reactions, and muscular weakness in RADIUS-P. INTERPRETATION: Elezanumab was safe and well tolerated, but did not meet the primary endpoint in either study. ANN NEUROL 2025;98:590-602.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".