Late Breaking Abstract - Benralizumab reduces eosinophils and ANCA in sputum in EGPA
Bibliographic record
Abstract
Introduction: Benralizumab (30mg subcutaneous Q4W) is noninferior to mepolizumab (300mg sc Q4W) to induce clinical remission in patients with refractory EGPA (PMID: 38393328). Aims: We evaluated if treatment arms were associated with eosinophil reduction and ANCA in sputum in the randomized, double-blind, head-to-head MANDARA trial ( NCT04157348 ). Methods: In a mechanistic sub-study (n=16) of the MANDARA trial, n=7 patients received benralizumab (52±11years, 57% female) and, n=9 received mepolizumab (58±16years, 78% female). Eosinophils, eosinophil activity (eosinophil peroxidase, EPX), T1/T2 cytokines and ANCA were assessed in blood and sputum pre- and post-treatment (PMID: 30179583). Results: Benralizumab reduced sputum eosinophils to <2.3% in 6/7 (86%) patients compared to mepolizumab (6/9, 66%) (χ2, P<0.01), but did not significantly reduce sputum EPX. At baseline, spANCA in both arms were comparable (mepo:4/9, 44%; benra: 3/7, 43%; only one sero-positive). Post-treatment, benralizumab significantly reduced spANCA levels compared to mepolizumab (P<0.05), with 2/7 (28%) remaining spANCA+. By comparison, there was an increase in the proportion of patients with spANCA on mepolizumab from baseline (6/9, 67%). Increase in spANCA correlated with spEPX (rho=0.65; P=0.01). The two patients on benralizumab who remained spANCA+ had elevated spEPX and ACQ-6≥1.5 at the time-point of sampling, despite having achieved clinical remission. Reduction of T2 cytokines was comparable in both arms. However, an increase in spIL-17A post-mepolizumab (P<0.05) was noted that correlated with an increase in spANCA. Conclusions: Benralizumab has a greater effect on reducing sputum eosinophils and ANCA levels in EGPA compared to mepolizumab.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".