Targeting neutrophil serine proteases in bronchiectasis
Bibliographic record
Abstract
Persistent neutrophilic inflammation is a central feature in both the pathogenesis and progression of bronchiectasis. Neutrophils release neutrophil serine proteases (NSPs), such as neutrophil elastase (NE), cathepsin G and proteinase 3. When chronically high levels of free NSP activity exceed those of protective antiproteases, structural lung destruction, mucosal-related defects, further susceptibility to infection and worsening of clinical outcomes can occur. Despite the defined role of prolonged, high levels of NSPs in bronchiectasis, no drug that controls neutrophilic inflammation is licensed for the treatment of bronchiectasis. Previous methods of suppressing neutrophilic inflammation (such as direct inhibition of NE) have not been successful; however, an emerging therapy designed to address neutrophil-mediated pathology, inhibition of the cysteine protease cathepsin C (CatC, also known as dipeptidyl peptidase 1), is a promising approach to ameliorate neutrophilic inflammation, since this may reduce the activity of all NSPs implicated in bronchiectasis pathogenesis, and not just NE. Current data suggest that CatC inhibition may effectively restore the protease-antiprotease balance in bronchiectasis and improve disease outcomes as a result. Clinical trials for CatC inhibitors in bronchiectasis have reported positive phase III results. In this narrative review, we discuss the role of high NSP activity in bronchiectasis, and how this feature drives the associated morbidity and mortality seen in bronchiectasis. This review discusses therapeutic approaches aimed at treating neutrophilic inflammation in the bronchiectasis lung, summarising clinical trial outcomes and highlighting the need for more treatment strategies that effectively address chronic neutrophilic inflammation in bronchiectasis.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 teacher head, 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".