Two randomised controlled phase 2 studies of the oral neutrophil elastase inhibitor alvelestat in alpha-1 antitrypsin deficiency
Bibliographic record
Abstract
Background Alpha-1 antitrypsin deficiency (AATD) is a genetic disorder that causes emphysema from lack of the alpha-1 antitrypsin (AAT) serpin antiprotease, leading to protease–antiprotease imbalance. Weekly intravenous AAT therapy (augmentation) is the only specific treatment available. Alvelestat is an oral inhibitor of neutrophil elastase (NE) in development as a novel approach to AATD therapy. Here, we tested the safety and mechanistic efficacy of alvelestat in severe AATD. Methods We conducted two complementary, double-blind, randomised, placebo-controlled, 12-week trials, incorporating two doses of alvelestat in AATD. ATALANTa investigated 120 mg twice daily, including a subset of participants also receiving augmentation; ASTRAEUS tested 120 and 240 mg twice daily without augmentation. Primary and secondary end-points were the change in blood NE (the putative target) and its activity in AATD (Aα-Val 360 and desmosine/isodesmosine) as well as safety and tolerability. Results We enrolled 161 participants (63 in ATALANTa and 98 in ASTRAEUS). Blood NE was significantly suppressed in both studies at both doses, with the greatest effect (>90% suppression) at alvelestat 240 mg twice daily. There was no effect of alvelestat 120 mg on disease activity biomarkers, while 240 mg demonstrated significant reduction in Aα-Val 360 and desmosine. The most common adverse event was headache, particularly at the 240 mg dose. No safety signals of concern were detected. Conclusions Alvelestat effectively suppressed NE and its activity at both doses, but only the 240 mg twice-daily dose demonstrated relevant efficacy compared to placebo on disease activity biomarkers with a favourable safety profile. These findings support progression of the 240 mg twice-daily dose into a clinical end-point study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".