Identification of NAE Inhibitors Exhibiting Potent Activity in Leukemia Cells: Exploring the Structural Determinants of NAE Specificity
Bibliographic record
Abstract
MLN4924 is a selective inhibitor of the NEDD8-activating enzyme (NAE) and has advanced into clinical trials for the treatment of both solid and hematological malignancies. In contrast, the structurally similar compound 1 (developed by Millennium: The Takeda Oncology Company) is a pan inhibitor of the E1 enzymes NAE, ubiquitin activating enzyme (UAE), and SUMO-activating enzyme (SAE) and is currently viewed as unsuitable for clinical use given its broad spectrum of E1 inhibition. Here, we sought to understand the determinants of NAE selectivity. A series of compound 1 analogues were synthesized through iterative functionalization of the purine C6 position and evaluated for NAE specificity. Optimal NAE specificity was achieved through substitution with primary N-alkyl groups, while bulky or secondary N-alkyl substituents were poorly tolerated. When assessed in vitro, inhibitors reduced the growth and viability of malignant K562 leukemia cells. Through this study, we have successfully identified a series of sub-10 nM NAE-specific inhibitors and thereby highlighted the functionalities that promote NAE selectivity.
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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 | 0.001 |
| 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".