Therapeutic Utility and Medicinal Chemistry of Cathepsin C Inhibitors
Bibliographic record
Abstract
The lysosomal cysteine protease cathepsin C (Cat C), also known as dipeptidyl peptidase I, activates a number of granule-associated serine proteases with pro-inflammatory and immune functions by removal of their inhibitory N-terminal dipeptides. Thus, Cat C is a therapeutic target for the treatment of a number of inflammatory and autoimmune diseases. Cathepsin C null mice and humans with Cat C loss of function mutations (Papillon-Lefèvre syndrome) show deficiencies in disease-relevant proteases including neutrophil elastase, cathepsin G, chymases and granzymes and the Cat C mice are protected in a number of disease models. Several methodologies have been recently reported for assessing the effects of Cat C inhibitors on serine protease activities in cellular assays and prolonged treatment of rats with a reversible, selective Cat C inhibitor reduced the activity of three leukocyte serine proteases. Nearly all potent and selective Cat C inhibitors described are based on the preferred dipeptide substrates bearing either irreversible (e.g. diazomethylketone, acyloxymethyl ketone, o-acyl hydroxamic acid and vinyl sulfone) or reversible (e.g. semicarbazide, nitrile and cyanamide) electrophilic warheads. While potent and highly selective, the best inhibitors described to date still have poor stability and/or rodent pharmacokinetics, likely resulting from their peptidic nature. The lack of selective compounds with appropriate rodent pharmacokinetic properties has hampered the assessment of the effects of Cat C inhibitors on the activation of disease-relevant proteases in vivo and the full evaluation of the therapeutic utility of Cat C inhibitors.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".