Benralizumab attenuates blood and airway eosinophilia in severe asthmatics with inadequate response to anti-IL-5 neutralizing antibodies
Bibliographic record
Abstract
Introduction: Targeting the IL-5 ligand does not suppress airway eosinophilia in a subset of severe asthmatics. We investigated if targeting the IL-5 receptor with benralizumab would suppress airway eosinophilia leading to greater asthma control. Methods: Sixteen severe asthmatics (9 females, mean age 58±14 years) on daily high-dose corticosteroid therapy (14 prednisone-dependent, median dose 7.5 mg) with ACQ≥1.5 and sputum eosinophils>3% despite mepolizumab (n=13, 100 mg, subcutaneous) or Reslizumab (n=3, 3mg/kg, intravenous) for ≥6 months were recruited in a sequential placebo-controlled trial (NCT03470311). Clinical outcomes were assessed at baseline (V1), after two months of placebo treatment (V3), and after 5 injections of benralizumab (V10). Results: Benralizumab completely depleted sputum and blood eosinophils in all 16 patients, with clinical improvements in FEV1, and ACQ-5 (Fig1). However, the decrease in fractional exhaled nitric oxide, FeNO (p>0.05) was modest. Despite complete suppression of eosinophilia and mean decrease in asthma control questionnaire (ACQ-5) by 1.21 points, 37% (6/16) patients still documented ACQ >1.5. Conclusions: Targeting IL-5R with benralizumab suppresses sputum and blood eosinophilia that is uncontrolled by anti-IL5 neutralizing antibodies in severe asthmatics. Residual symptoms, associated with raised FeNO may indicate IL-4R activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".