Olodaterol exerts anti-inflammatory effects on COPD airway epithelial cells
Bibliographic record
Abstract
BACKGROUND: Airway inflammation is a key feature of chronic obstructive pulmonary disease (COPD) and inhaled corticosteroids (ICS) remain the main treatment for airway inflammation. Studies have noted the increased efficacy of ICS and long-acting beta 2 agonist (LABA) combination therapy in controlling exacerbations and improving airway inflammation than either monotherapy. Further studies have suggested that LABAs may have inherent anti-inflammatory potential, but this has not been well-studied. OBJECTIVE: We hypothesize that the LABA olodaterol can inhibit airway inflammation resulting from exposure to respiratory syncytial virus (RSV) via its binding receptor, the β2-adrenergic receptor. METHODS: Human bronchial epithelial brushing from patients with and without COPD were cultured into air-liquid interface (ALI) cultures and treated with or without olodaterol and RSV infection to examine the effect on markers of inflammation including interleukin-8 (IL-8) and mucus secretion. The cell line NCI-H292 was utilized for gene silencing of the β2-adrenergic receptor via siRNA as well as receptor blocking via ICI 118,551 and butaxamine. RESULTS: At baseline, COPD-ALIs produced greater amounts of IL-8 than control ALIs. Olodaterol reduced RSV-mediated IL-8 secretion in both COPD and control ALIs and also significantly reduced Muc5AC staining in COPD-ALIs infected with RSV. A non-significant reduction was seen in control ALIs. Gene silencing of the β2-adrenergic receptor in NCI-H292 negated the ability of olodaterol to inhibit IL-8 secretion from both RSV infection and lipopolysaccharide stimulus, as did blocking of the receptor with ICI 118,551 and butaxamine. CONCLUSIONS: Olodaterol exhibits inherent anti-inflammatory properties on the airway epithelium, in addition to its bronchodilation properties, that is mediated through the β2-adrenergic receptor and independent of ICS usage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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; both teacher heads agree on what is shown here.
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".