Effect of Dupilumab on the Temporospatial Characteristics of Mucus Plugs in Patients With Moderate-to-Severe Asthma
Bibliographic record
Abstract
Abstract RATIONALE: Mucus plugs play a critical role in asthma pathophysiology, contributing to airflow obstruction and adverse clinical outcomes.1 Dupilumab, an interleukin-4 and interleukin-13 pathway blocker, has been shown to reduce airway mucus burden, as quantified by the computed tomography (CT) mucus score.2,3 However, the resolution of mucus plugs after treatment with dupilumab has not been fully investigated. Here, we characterized the effect of dupilumab on the spatial and temporal behaviour of mucus plugs. METHODS: This retrospective analysis included 28 adults with uncontrolled moderate-to-severe asthma; 20 received dupilumab, while 8 received placebo every 2-weeks for 16-weeks. At week-0 and week-16, full-inspiration chest CT scans were acquired as previously described.2 CT scans were reviewed and annotated for all mucus plugs using VIDA|vision software (VIDA Diagnostics) to determine the total mucus plug count (TMPC) and the temporospatial behaviour of individual mucus plugs. Subsequently, patients were classified into the following groups: complete resolution of plugs (all plugs resolved at week-16), persistent plugs (≥1 plug resolved at week-16, but not all), persistently no plugs (no plugs at week-0 and week-16), and new-onset plugs (no plugs at week-0 and ≥1 plug at week-16). Individual mucus plugs were characterized as resolved, fixed-location persistent (FLP), or new-onset. RESULTS: Figure 1 summarizes the effect of dupilumab (13F/7M, age=50±14 years, week-0 TMPC=6[0-23]) in comparison to placebo (3F/5M, age=60±9 years, week-0 TMPC=9[0-31]) on the total mucus burden and temporospatial behaviour of mucus plugs. At week-16, the TMPC was decreased following dupilumab but not placebo (Figure 1A). Figure 1B shows that 40% of patients treated with dupilumab had complete resolution of mucus plugs (13% in placebo), 55% had persistent plugs (75% in placebo), and 5% had persistently no plugs (0% in placebo). Considering the temporospatial behaviour of individual mucus plugs, the proportion of resolved plugs was greater (92%, 141 of 154 plugs vs. 62%, 59 of 95 plugs; p<0.0001) and FLPs was lower (8%, 13 of 154 plugs vs. 38%, 36 of 95 plugs; p<0.0001) following dupilumab compared to placebo (Figure 1C). The number of new-onset plugs was lower following dupilumab compared to placebo (0[0-4] vs. 9[0-18], p=0.001 (Figure 1D)). CONCLUSIONS: Treatment with dupilumab can reduce persistent plugs, decrease the formation of new plugs and completely resolve CT-evaluated mucus plugging in a subset of patients with asthma over a 16-week period. 1C Venegas et al. Allergol Int (2024); 2S Svenningsen et al. AJRCCM (2023); 3E Dunican et al. JCI (2018).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".