A dual Cys<scp>LT</scp><sub>1/2</sub> antagonist attenuates allergen‐induced airway responses in subjects with mild allergic asthma
Bibliographic record
Abstract
Abstract Background The cysteinyl leukotrienes (cysLTs) play a key role in the pathophysiology of asthma. In addition to functioning as potent bronchoconstrictors, cysLTs contribute to airway inflammation through eosinophil and neutrophil chemotaxis, plasma exudation, and mucus secretion. We tested the activity of the dual cysLT1/2 antagonist, ONO‐6950, against allergen‐induced airway responses. Methods Subjects with documented allergen‐induced early (EAR) and late asthmatic response (LAR) were randomized in a three‐way crossover study to receive ONO‐6950 (200 mg) or montelukast (10 mg) or placebo q.d. on days 1–8 of the three treatment periods. Allergen was inhaled on day 7 two hours postdose, and forced expiratory volume in 1 s (FEV1) was measured for 7 h following challenge. Sputum eosinophils and airway hyperresponsiveness were measured before and after allergen challenge. The primary outcome was the effect of ONO‐6950 vs placebo on the EAR and LAR. Results Twenty‐five nonsmoking subjects with mild allergic asthma were enrolled and 20 subjects completed all three treatment periods per protocol. ONO‐6950 was well tolerated. Compared to placebo, ONO‐6950 significantly attenuated the maximum % fall in FEV1 and area under the %FEV1/time curve during the EAR and LAR asthmatic responses (P < 0.05) and allergen‐induced sputum eosinophils. There were no significant differences between ONO‐6950 and montelukast. Conclusions Attenuation of EAR, LAR, and airway inflammation is consistent with cysLT1 blockade. Whether dual cysLT1/2 antagonism offers additional benefit for treatment of asthma requires further study.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".