Impact of Dupilumab Treatment on Lung Function in Patients With Chronic Obstructive Pulmonary Disease (COPD) and Type 2 Inflammation
Bibliographic record
Abstract
Abstract RATIONALE: COPD is associated with declining lung function. Preserving lung function should be considered when selecting an appropriate treatment for patients with COPD and type 2 inflammation. Dupilumab, a fully human monoclonal antibody, blocks the shared receptor component for interleukin (IL)-4 and IL-13, key and central drivers of type 2 inflammation. In the BOREAS (NCT03930732) and NOTUS (NCT04456673) trials, add-on dupilumab 300 mg every 2 weeks significantly reduced the rate of moderate or severe exacerbations vs placebo and improved lung function in patients with COPD, type 2 inflammation, and an increased exacerbation risk despite inhaled triple therapy. Safety was consistent with the known dupilumab safety profile. In this post hoc analysis of pooled data from BOREAS and NOTUS, we assessed whether dupilumab could help preserve lung function in patients with COPD and type 2 inflammation. METHODS: BOREAS and NOTUS, both phase 3, randomized, placebo-controlled trials, enrolled a total of 1,874 patients (aged 40 to 85 years) with COPD, ≥2 moderate or ≥1 severe exacerbations, and type 2 inflammation (blood eosinophil count ≥300 cells/µL at screening). Patients were randomized to either dupilumab 300 mg (n = 938) or placebo (n = 936) once every 2 weeks for 52 weeks. Endpoints assessed were mean (SD) peak post-baseline post-bronchodilator forced expiratory volume in 1 second (FEV1) and time to reach peak FEV1, mean percentage change from baseline at the peak of post-bronchodilator FEV1, and the proportion of patients who achieved an improvement from baseline of <0, 0 to150, 150 to 300, and ≥300 mL in post-bronchodilator FEV1 at peak. RESULTS: Median time to peak post-bronchodilator FEV1 from baseline was 84 days for dupilumab and placebo. More patients receiving dupilumab (308 [33%]) achieved a ≥300 mL change in FEV1, with a peak FEV1 of 2184 (608) mL and a percent change of 50%, compared to placebo (252 [27%]; peak FEV1: 2109 (581) mL, percent change:46%). Fewer patients on dupilumab had <0 (103 [11%] vs. 115 [12%]) or 0 to 150 (283 [30%] vs. 332 [36%]) changes, while a similar number had 150 to 300 change (236 [25%] vs. 230 [25%]). CONCLUSIONS: In patients with COPD and type 2 inflammation, patients treated with dupilumab, compared to those receiving a placebo, are less likely to experience a decline in lung function over 52 weeks of treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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) | 0.002 | 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".