Identification of a new inhibitor of Ran GTPase with potential therapeutic value in epithelial ovarian cancer
Bibliographic record
Abstract
ABSTRACT Compiled research studies suggest that the small GTPase Ran is critical in cancer initiation and progression. Compounds that block Ran activity would be valuable for cancer treatment. Yet, to date, there is no inhibitor proven to be efficient and specific to Ran. Here, by learning lessons from the discovery of KRAS G12C inhibitors, we generated a structural model of the switch II pocket of GDP-bound Ran to identify potential inhibitors of Ran by virtual screening. After in vitro verification, compound M26 was detected. Subsequent hit optimization lead us to identify compound M36 as an inhibitor of Ran with a promising therapeutic value. Binding of M36 to Ran was confirmed by cellular thermal shift assay. The specificity of M36 towards Ran was demonstrated by the evaluation of the active GTP-bound forms of a range of GTPases including Ran, RhoA, Cdc42 and Rac1; and by the expression of a dominant active mutant of Ran. Remarkably, similar to depletion of Ran by siRNA, M36 exhibits a specific toxicity in aneuploid ovarian cancer cells and represses DNA repair systems. In accordance with this, we demonstrated a synergistic relationship between M36 and the FDA approved PARP inhibitor Olaparib. In vivo , M36 presents acceptable pharmacokinetic properties and, more importantly, inhibits the tumor growth of an aggressive epithelial ovarian cancer xenograft model. Clinically relevant, M36 was able to induce cell death in ex vivo EOC patient derived micro-dissected tumor. Overall, our study is the first to provide a small-molecule compound inhibitor of Ran with a promising therapeutic potential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".