VR23: A Quinoline–Sulfonyl Hybrid Proteasome Inhibitor That Selectively Kills Cancer via Cyclin E–Mediated Centrosome Amplification
Bibliographic record
Abstract
The proteasome is clinically validated as a target for cancer therapeutics. However, proteasome-inhibitory agents that are cancer selective have yet to be developed. In this study, we report the identification of a safe and effective proteasome inhibitor with selective anticancer properties. We screened a chemical library constructed using a hybrid approach that incorporated a 4-piperazinylquinoline scaffold and a sulfonyl phamarcophore. From this library, we identified 7-chloro-4-(4-(2,4-dinitrophenylsulfonyl)piperazin-1-yl)quinoline (VR23) as a small molecule that potently inhibited the activities of trypsin-like proteasomes (IC50 = 1 nmol/L), chymotrypsin-like proteasomes (IC50 = 50-100 nmol/L), and caspase-like proteasomes (IC50 = 3 μmol/L). Data from molecular docking and substrate competition assays established that the primary molecular target of VR23 was β2 of the 20S proteasome catalytic subunit. Notably, VR23 was structurally distinct from other known proteasome inhibitors and selectively killed cancer cells by apoptosis, with little effect on noncancerous cells. Mechanistic investigations showed that cancer cells exposed to VR23 underwent an abnormal centrosome amplification cycle caused by the accumulation of ubiquitinated cyclin E. In combinations with the clinically approved chymotrypsin-like proteasome inhibitor bortezomib, VR23 produced a synergistic effect in killing multiple myeloma cells, including those that were resistant to bortezomib. VR23 was effective in vivo in controlling multiple myelomas and metastatic breast cancer cells, in the latter case also enhancing the antitumor activity of paclitaxel while reducing its side effects. Overall, our results identify VR23 as a structurally novel proteasome inhibitor with desirable properties as an anticancer agent.
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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) | 0.000 | 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) | 0.001 | 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 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".