Inhibition of the Activation of Multiple Serine Proteases with a Cathepsin C Inhibitor Requires Sustained Exposure to Prevent Pro-enzyme Processing
Bibliographic record
Abstract
Cathepsin C is a cysteine protease required for the activation of several pro-inflammatory serine proteases and, as such, is of interest as a therapeutic target. In cathepsin C-deficient mice and humans, the N-terminal processing and activation of neutrophil elastase, cathepsin G, and proteinase-3 is abolished and is accompanied by a reduction of protein levels. Pharmacologically, the consequence of cathepsin C inhibition on the activation of these serine proteases has not been described, due to the lack of stable and non-toxic inhibitors and the absence of appropriate experimental cell systems. Using novel reversible peptide nitrile inhibitors of cathepsin C, and cell-based assays with U937 and EcoM-G cells, we determined the effects of pharmacological inhibition of cathepsin C on serine protease activity. We show that indirect and complete inhibition of neutrophil elastase, cathepsin G, and proteinase-3 is achievable in intact cells with selective and non-cytotoxic cathepsin C inhibitors, at concentrations approximately 10-fold higher than those required to inhibit purified cathepsin C. The concentration of inhibitor needed to block processing of these three serine proteases was similar, regardless of the cell system used. Importantly, cathepsin C inhibition must be sustained to maintain serine protease inhibition, because removal of the reversible inhibitors resulted in the activation of pro-enzymes in intact cells. These findings demonstrate that near complete inhibition of multiple serine proteases can be achieved with cathepsin C inhibitors and that cathepsin C inhibition represents a viable but challenging approach for the treatment of neutrophil-based inflammatory diseases.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".