Evaluation of HZX-960, a novel DCN1–UBC12 interaction inhibitor, as a potential antifibrotic compound for liver fibrosis
Bibliographic record
Abstract
Liver fibrosis is a very common health problem and currently lacks effective treatments. Cullin RING E3 ligases (CRLs) regulate the turnover of ∼20% of mammalian cell proteins. Neddylation, the process by which NEDD8 is covalently attached to cullin proteins through sequential enzymatic reactions, is critical for the activation of CRLs and was recently found to be elevated in liver fibrosis. NEDD8-activating enzyme E1-specific inhibition led to the reduced liver damage characterized by decreased apoptosis, inflammation, and fibrosis. However, the relevance of a co-E3 ligase, DCN1, in liver fibrosis remains unclear. Here, a novel and potent DCN1–UBC12 interaction inhibitor HZX-960 was discovered with an IC50 value of 9.37 nmol/L, which could inhibit the neddylation of cullin3. Importantly, we identified that HZX-960 treatment could attenuate transforming growth factor β-induced liver fibrotic responses by reducing the deposition of collagen I and α-smooth muscle actin, and upregulating cellular NF-E2-related factor 2, hemeoxygenase 1, and NADPH quinone oxidoreductase-1 levels in two hepatic stellate cell lines. Additionally, DCN1 was shown to be unregulated in CCl4-induced mice liver tissue, and liver fibrotic signaling in mice was reduced by HZX-960. Therefore, our data demonstrated that HZX-960 possessed anti-liver fibrosis ability and that DCN1 may be a potential therapeutic target for liver fibrosis treatment.
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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".