New use for an old drug: COX‐independent anti‐inflammatory effects of sulindac in models of cystic fibrosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Pulmonary disease is the main cause of morbidity and mortality in cystic fibrosis (CF) patients due to exacerbated inflammation. To date, the only anti-inflammatory drug available to CF patients is high-dose ibuprofen, which can slow pulmonary disease progression, but whose cyclooxygenase-dependent digestive adverse effects limit its clinical use. Here we have tested sulindac, another non-steroidal anti-inflammatory drug with an undefined anti-inflammatory effect in CF airway epithelial cells. EXPERIMENTAL APPROACH: Using in vitro and in vivo models, we NF-κB activity and IL-8 secretion. In HeLa-F508del cells, we performed luciferase reporter gene assays in order to measure i) IL-8 promoter activity, and ii) the activity of synthetic promoter containing NF-κB responsive elements. We quantified IL-8 secretion in airway epithelial CFBE cells cultured at an air-liquid interface and in a mouse model of CF. KEY RESULTS: Sulindac inhibited the transcriptional activity of NF-κB and decreased IL-8 transcription and secretion in TNF-α stimulated CF cells via a cyclooxygenase-independent mechanism. This effect was confirmed in vivo in a mouse model of CF induced by intra-tracheal instillation of LPS, with a significant decrease of the induction of mRNA for MIP-2, following treatment with sulindac. CONCLUSION AND IMPLICATIONS: Overall, sulindac decrease lung inflammation by a mechanism independent of cycolooxygenase. This drug could be beneficially employed in CF.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".