Thiol Proteases: Inhibitors and Potential Therapeutic Targets
Bibliographic record
Abstract
A better understanding of the biological roles and the pathological consequences of thiol-dependent enzymes has emerged in recent years, and hence considerable progress has been made in identifying and delineating cysteine proteases that can be considered promising drug targets from those involved in housekeeping functions. Cysteine proteases have been implicated in a wide variety of disease processes ranging from cardiovascular, inflammatory, viral and immunological disorders to cancer. The first milestone in drug development of cysteine protease inhibitors has probably been reached, as IDN-6556 (a broad spectrum caspase inhibitor) has recently received Orphan Drug label by the U.S. Food and Drug Administration for use in the treatment of the patients undergoing liver transplantation and other solid organ transplantation. IDN-6556, which blocks apoptosis, is in Phase II human clinical trial in patients undergoing liver transplantation. In addition, more than ten cysteine protease inhibitors are presently at various phases of clinical development/trials for diverse diseases. This review emphasises on the new development from the literature reports since the year 2000 in the exploration of potential cysteine proteases as prospective drug targets, and the investigation of promising inhibitors that can potentially be developed for the treatment of human diseases. Transglutaminases, another class of thiol-dependent enzymes, are not discussed here.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".