Development of a SIN1 Targeting Inhibitor as a Novel Therapeutic Approach for the Treatment of Malignancies
Bibliographic record
Abstract
The essential role of mTOR in promoting tumorigenesis of many cancers makes it an attractive therapeutic target. However, catalytic mTOR inhibitors, which block both mTORC1 and mTORC2, result in activation of negative feedback loops as a resistance mechanism. Selective mTORC2 inhibitors are expected to have the desired antitumor effects without engaging resistance mechanisms; however, to date, no such mTORC2 inhibitors have been developed. Using in silico screening and medicinal chemistry optimization, we identified several small molecules that bind to the unique mTORC2 component, stress-activated protein kinase-interacting protein 1 (SIN1). We demonstrate that this SIN1 inhibitor alters posttranslational modification, protein-protein interactions, and blocks mTORC2- and rapamycin-sensitive mTORC1-mediated signaling. The SIN1 inhibitor also inhibits wild-type RAS activation and downstream MAPK signaling, as well as cell proliferation of multiple cancer cell line types. SIN1 inhibition can enhance the efficacy of FDA-approved antineoplastic agents in vitro and may provide a novel approach for the treatment of different types of malignancies.
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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) | 0.002 | 0.001 |
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".