Pamrevlumab did not meet its primary endpoint for ambulatory patients with Duchenne Muscular Dystrophy: the LELANTOS-2 trial
Bibliographic record
Abstract
ABSTRACT Background In Duchenne muscular dystrophy (DMD), fibrosis is linked to connective tissue growth factor (CTGF) overexpression. Pamrevlumab, a fully human monoclonal antibody that inhibits CTGF activity, showed promise as a DMD treatment in a phase 2 trial. Objective LELANTOS-2 ( NCT04632940 ) was a global phase 3 study of the safety and efficacy of pamrevlumab for ambulatory males 6 to <12 years old with DMD. Methods Patients were randomized 1:1 to pamrevlumab 35 mg/kg every 2 weeks for 52 weeks or placebo. All received a stable corticosteroid regimen (deflazacort or prednisone/prednisolone). Primary endpoint was change in North Star Ambulatory Assessment (NSAA) total score from baseline to Week 52. Treatment-emergent adverse events (TEAEs) were noted. Patients who completed the main study period were eligible to enroll in the open-label extension (OLE). Results Seventy-three patients enrolled (mean [SD] age, 9.0 [1.5] years; pamrevlumab [n=37], placebo [n=36]). Between-group baseline characteristics were similar. The difference in change in NSAA score was not significant (least squares [LS] mean [SE]: pamrevlumab, –3.022 [0.5505] vs placebo, –2.494 [0.6962]; p =0.5553). Across all secondary endpoints, there were no significant differences between patients treated with pamrevlumab or placebo. Nearly all patients (pamrevlumab, n=35 [97.2%]; placebo, n=35 [97.2%]) experienced TEAEs (most mild/moderate). Sixty-eight (34 from each original treatment group) patients enrolled in and received pamrevlumab during the OLE. OLE efficacy and safety were consistent with the main study period. No deaths occurred. Conclusions Pamrevlumab failed to meet its primary endpoint. Its future as a DMD treatment is uncertain.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".