Preclinical evaluation of the third-generation, bi-steric mechanistic target of rapamycin complex 1-selective inhibitor RMC-6272 in <i>NF2</i>-deficient models
Bibliographic record
Abstract
Abstract Background NF2-associated meningiomas are progressive, highly morbid, and nonresponsive to chemotherapies, highlighting the need for improved treatments. We have established aberrant activation of the mechanistic target of rapamycin (mTOR) signaling in NF2-deficient tumors, leading to clinical trials with first- and second-generation mTOR inhibitors. However, results have been mixed, showing stabilized tumor growth without shrinkage offset by adverse side effects. To address these limitations, here we explored the potential of third-generation, bi-steric mTOR complex 1 (mTORC1) inhibitors using the preclinical tool compound RMC-6272. Methods Employing human NF2-deficient meningioma lines, we compared mTOR inhibitors rapamycin (first-generation), INK128 (second-generation), and RMC-6272 (third-generation) using in vitro dose–response testing, cell-cycle analysis, and immunoblotting. Furthermore, the efficacy of RMC-6272 was assessed in NF2-null 3D-spheroid meningioma models, and its in vivo potential was evaluated in 2 orthotopic meningioma mouse models. Results Treatment of meningioma cells revealed that, unlike rapamycin, RMC-6272 demonstrated superior growth inhibitory effects, cell-cycle arrest, and complete inhibition of phosphorylated 4E-BP1 (mTORC1 readout). Moreover, RMC-6272 had a longer retention time than INK128 and inhibited the expression of several eIF4E-sensitive targets on the protein level. RMC-6272 treatment of NF2 spheroids showed significant shrinkage in size as well as reduced proliferation. Furthermore, in vivo studies in mice revealed effective blockage of meningioma growth by RMC-6272, compared with vehicle controls. Conclusions Our study in preclinical models of NF2 supports possible future clinical evaluation of third-generation, investigational mTORC1 inhibitors, such as RMC-5552, as a potential treatment strategy for NF2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".