Differential treatment response to mepolizumab in severe eosinophilic asthma with nasal polyps
Bibliographic record
Abstract
Background: In severe eosinophilic asthma (SEA), mepolizumab, an anti-interleukin 5 (IL-5) monoclonal antibody (mAb) can reduce asthma exacerbations and oral corticosteroid (OCS) requirements, but effect on nasal polyps (NP) recurrence and need for surgery is uncertain. We aimed to study the treatment (Rx) response in SEA patients with NP (SEAwNP) and assess for predictors of suboptimal Rx response individually for the asthma and NP components. Method: We measured sputum cell counts, cytokines, autoantibodies and nasoendoscopic total polyp score (TPS) in 6 patients with SEAwNP on mepolizumab (100mcg q4weeks) over 15 months, every 3 months. Optimal Rx response to the SEA component was defined as sputum eosinophil counts ≤ 3% after 4 months of treatment and >1 of the following: a) ACT score ≥20, b) Reduction of OCS dosage by ≥50%, c) 0 exacerbation requiring an OCS burst. Rx response for NP was defined as a) reduction in TPS by ≥1, (b) no recurrence. Results: There was optimal Rx response in 3/6(50%) patients with respect to their SEA component, while only 30% for NP. The suboptimal response in SEAwNP is predicted by sputum eosinophil counts (>3%), even when asthma controlled (ACT≥20). Suboptimal response in SEA and NP is associated with rise in sputum IL-5 and IL-13 respectively; baseline levels cannot predict treatment response. IL-5 is correlated with anti-EPX antibodies (r=0.52,p=0.0026) in individual patients. However collectively, anti-EPX levels remained constant, regardless of treatment response. Conclusions: Mepolizumab has differential Rx response to the nasal polyp component in SEAwNP patients. Trends in sputum eosinophil and IL-5 and IL13 can predict suboptimal treatment response.
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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) | 0.000 | 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".