Inhaled anti-TSLP antibody fragment, ecleralimab, blocks responses to allergen in mild asthma
Bibliographic record
Abstract
Background Thymic stromal lymphopoietin (TSLP) is a key upstream regulator driving allergic inflammatory responses. We evaluated the efficacy and safety of ecleralimab, a potent inhaled neutralising antibody fragment against human TSLP, using allergen inhalation challenge (AIC) in subjects with mild atopic asthma. Methods This was a 12-week, randomised, double-blind, placebo-controlled, parallel-design, multicentre allergen bronchoprovocation study conducted at 10 centres across Canada and Germany. Subjects aged 18–60 years with stable mild atopic asthma were randomised (1:1) to receive 4 mg once-daily inhaled ecleralimab or placebo. Primary end-points were the allergen-induced change in forced expiratory volume in 1 s (FEV1) during the late asthmatic response (LAR) measured by area under the curve (AUC3–7h) and maximum percentage decrease (LAR%) on day 84, and the safety of ecleralimab. Allergen-induced early asthmatic response (EAR), sputum eosinophils and fractional exhaled nitric oxide (FENO) were secondary and exploratory end-points. Results 28 subjects were randomised to ecleralimab (n=15) or placebo (n=13). On day 84, ecleralimab significantly attenuated LAR AUC3–7hby 64% (p=0.008), LAR% by 48% (p=0.029), and allergen-induced sputum eosinophils by 64% at 7 h (p=0.011) and by 52% at 24 h (p=0.047) post-challenge. Ecleralimab also numerically reduced EAR AUC0–2h(p=0.097) and EAR% (p=0.105).FENOlevels were significantly reduced from baseline throughout the study (p<0.05), except at 24 h post-allergen (day 43 and day 85). Overall, ecleralimab was safe and well tolerated. Conclusion Ecleralimab significantly attenuated allergen-induced bronchoconstriction and airway inflammation, and was safe in subjects with mild atopic asthma.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".