Abstract IA018: Pan-RAF-MEK molecular glue NST-628 is a potent and brain-penetrant inhibitor of the RAS-MAPK pathway
Bibliographic record
Abstract
Abstract Alterations in the RAS-MAPK signaling cascade are common across multiple solid tumor types and is a driver for many cancers. NST-628 is a potent pan-RAF-MEK molecular glue that prevents phosphorylation and activation of MEK by RAF, overcoming the limitations of traditional RAS-MAPK inhibitors and leading to deep durable inhibition of the pathway. Cellular, biochemical, and structural analysis of RAF-MEK complexes show that NST-628 engages all isoforms of RAF, including ARAF, and prevents the formation of BRAF-CRAF heterodimers, a differentiated mechanism from all current RAF inhibitors. With a potent and durable inhibition of the RAF-MEK signaling complex as well as high intrinsic permeability into the brain, NST-628 demonstrates broad efficacy in cellular and patient-derived tumor models harboring diverse MAPK pathway alterations, including orthotopic intracranial models. Given a mechanism differentiated from previous therapies targeting RAF or MEK catalytic activity, NST-628 is positioned to make an impact clinically in an areas of unmet patient need. Citation Format: Klaus P. Hoeflich. Pan-RAF-MEK molecular glue NST-628 is a potent and brain-penetrant inhibitor of the RAS-MAPK pathway [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr IA018.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".