Therapeutic potential of anti-IL-6 therapies for granulocytic airway inflammation in asthma
Bibliographic record
Abstract
BACKGROUND: Determining the cellular and molecular phenotypes of inflammation in asthma can identify patient populations that may best benefit from targeted therapies. Although elevated IL-6 and polymorphisms in IL-6 signalling are associated with lung dysfunction in asthma, it remains unknown if elevated IL-6 levels are associated with a specific cellular inflammatory phenotype, and how IL-6 blockade might impact such inflammatory responses. METHODS: Patients undergoing exacerbations of asthma were phenotyped according to their airway inflammatory characteristics (normal cell count, eosinophilic, neutrophilic, mixed granulocytic), sputum cytokine profiles, and lung function. Mice were exposed to the common allergen, house dust-mite (HDM), in the presence or absence of endogenous IL-6. The intensity and nature of lung inflammation, and levels of pro-granulocytic cytokines and chemokines under these conditions were analyzed. RESULTS: Elevated IL-6 was associated with a lower FEV1 in patients with mixed eosinophilic-neutrophilic bronchitis. In mice, allergen exposure increased lung IL-6 and IL-6 was produced by dendritic cells and alveolar macrophages. Loss-of-function of IL-6 signalling (knockout or antibody-mediated neutralization) abrogated elevations of eosinophil and neutrophil recruiting cytokines/chemokines and allergen-induced airway inflammation in mice. CONCLUSIONS: We demonstrate the association of pleiotropic cellular airway inflammation with IL-6 using human and animal data. These data suggest that exacerbations of asthma, particularly those with a combined eosinophilic and neutrophilic bronchitis, may respond to therapies targeting the IL-6 pathway and therefore, provide a rational basis for initiation of clinical trials to evaluate this.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".