Diroximel fumarate (DRF) in patients with relapsing–remitting multiple sclerosis: Interim safety and efficacy results from the phase 3 EVOLVE-MS-1 study
Bibliographic record
Abstract
Background: Diroximel fumarate (DRF) is a novel oral fumarate for patients with relapsing–remitting multiple sclerosis (RRMS). DRF and the approved drug dimethyl fumarate yield bioequivalent exposure to the active metabolite monomethyl fumarate; thus, efficacy/safety profiles are expected to be similar. However, DRF’s distinct chemical structure may result in a differentiated gastrointestinal (GI) tolerability profile. Objective: To report interim safety/efficacy findings from patients in the ongoing EVOLVE-MS-1 study. Methods: EVOLVE-MS-1 is an ongoing, open-label, 96-week, phase 3 study assessing DRF safety, tolerability, and efficacy in RRMS patients. Primary endpoint is safety and tolerability; efficacy endpoints are exploratory. Results: As of March 2018, 696 patients were enrolled; median exposure was 59.9 (range: 0.1–98.9) weeks. Adverse events (AEs) occurred in 84.6% (589/696) of patients; the majority were mild (31.2%; 217/696) or moderate (46.8%; 326/696) in severity. Overall treatment discontinuation was 14.9%; 6.3% due to AEs and <1% due to GI AEs. At Week 48, mean number of gadolinium-enhancing lesions was significantly reduced from baseline (77%; p < 0.0001) and adjusted annualized relapse rate was low (0.16; 95% confidence interval: 0.13–0.20). Conclusion: Interim data from EVOLVE-MS-1 suggest DRF is a well-tolerated treatment with a favorable safety/efficacy profile for patients with RRMS.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".