Efficacy and Safety of Atrasentan in Patients (Pts) with IgAN from East (E) Asia: Phase 3 ALIGN Interim Data
Bibliographic record
Abstract
Background: In the ALIGN interim analysis (IA), atrasentan, a highly selective, potent ETA receptor antagonist, reduced 24-hour urine protein–creatinine ratio (24h-UPCR) by 36.1% vs placebo (pbo) at Week (W) 36 and had a favorable safety profile (NCT04573478). Atrasentan (VANRAFIA®) received US FDA accelerated approval for proteinuria reduction in adults with primary IgAN at risk of rapid disease progression. IgAN is most prevalent in Asian pts; we report ALIGN IA results for pts from E Asia. Methods: ALIGN is an ongoing, double-blind, Phase III trial in adults with IgAN and 24-hour total urine protein ≥1 g/day on optimized supportive care. Pts were randomized to receive atrasentan 0.75 mg/day or pbo. An IA occurred when 270 pts completed or discontinued before the W36 visit. Exploratory efficacy and safety analyses were conducted for pts from E Asia (n=120; China mainland 45.0%, Japan 18.3%, South Korea 26.7%, other 10.0%; atrasentan n=59, pbo n=61) and all pts of Asian race (n=154). Results: Baseline demographics were balanced between arms.In pts from E Asia, atrasentan reduced 24h-UPCR at W36 by 41.1% (95% CI 25.4, 53.5; Figure) vs pbo. In pts with baseline 24h-UPCR ≥1.5 g/g or <1.5 g/g, 24h-UPCR reduction at W36 vs pbo was 47.1% (95% CI 23.0, 63.6) and 37.5% (95% CI 14.2, 54.5), respectively (Figure). Treatment-emergent AEs occurred in 88.1% of pts on atrasentan and 88.5% on pbo, and led to treatment discontinuation in 3.4% and 3.3% of pts, respectively. Data were consistent for all pts of Asian race. Conclusion: Atrasentan was well tolerated and led to a clinically meaningful 24h-UPCR reduction vs pbo in pts from E Asia, supporting the potential of atrasentan as a foundational treatment for Asian pts with IgAN. Funding: Commercial Support - Novartis
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".