Dupilumab Reduces Exacerbations Independent of Changes in Biomarkers in Moderate-to-Severe Asthma
Bibliographic record
Abstract
Background Changes from baseline in fractional exhaled nitric oxide (FeNO) and blood eosinophil count (Eos) may be related to efficacy outcomes in dupilumab-treated patients with moderate-to-severe asthma. Objective This post hoc analysis investigated biomarker changes in placebo- and dupilumab-treated patients with uncontrolled moderate-to-severe asthma enrolled in QUEST (NCT02414854). Methods Spline analyses of annualized severe exacerbation rate (AER) and change from baseline in pre-bronchodilator (BD) forced expiratory volume in 1 second (FEV 1 ) at week 52 were performed as a function of the fold change in FeNO at week 52 and the maximum fold change in Eos over weeks 0-12 (also change from baseline in pre-BD FEV 1 at week 12). Results The combined placebo and dupilumab groups comprised 638 and 1264 patients, respectively. FeNO levels declined rapidly by week 2 and then gradually to week 52 in patients treated with dupilumab versus placebo; Eos, after initially increasing with dupilumab, declined slightly from baseline in both treatment groups. AER during QUEST showed no significant association with the change in biomarkers in either treatment group. The change from baseline in pre-BD FEV 1 at week 52 was inversely associated with the fold change in FeNO in both groups, with a significant difference between the dupilumab and placebo curves ( P = .014), and was positively associated with the fold change in Eos in both groups ( P = .022). Conclusions Relative changes in FeNO and Eos were not associated with AER, regardless of treatment arm. However, changes in both biomarkers showed a predictive value for lung function improvement; for FeNO, this was specific to the dupilumab treatment arm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".