Targeting the Ubiquitin-Proteasome System for Cancer Therapeutics by Small-Molecule Inhibitors
Bibliographic record
Abstract
The ubiquitin-proteasome system (UPS) is a critical regulator of cellular protein levels and activity. It is, therefore, not surprising that its dysregulation is implicated in numerous human diseases, including many types of cancer. Moreover, since cancer cells exhibit increased rates of protein turnover, their heightened dependence on the UPS makes it an attractive target for inhibition via targeted therapeutics. Indeed, the clinical application of proteasome inhibitors in treatment of multiple myeloma has been very successful, stimulating the development of small-molecule inhibitors targeting other UPS components. On the other hand, while the discovery of potent and selective chemical compounds can be both challenging and time consuming, the area of targeted protein degradation through utilization of the UPS machinery has seen promising developments in recent years. The repertoire of proteolysis-targeting chimeras (PROTACs), which employ E3 ligases for the degradation of cancer-related proteins via the proteasome, continues to grow. In this review, we will provide a thorough overview of small-molecule UPS inhibitors and highlight advancements in the development of targeted protein degradation strategies for cancer therapeutics.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".