Abstract IA020: Discovery of brain-penetrant inhibitors for the treatment of BRAF mutant tumors
Bibliographic record
Abstract
Abstract Many tumors are driven by oncogenic kinases. While a majority of the most prevalent oncogenic kinases currently have existing targeted therapies available, most of the molecules were not designed to be CNS penetrant and are therefore unable to control CNS metastatic tumors. By employing principles for the design of CNS penetrant molecules, we discovered two brain-penetrant BRAF inhibitors, which both progressed into clinical trials. The first candidate, ARRY-461, was designed to inhibit class I mutant BRAF V600E tumors. The second candidate, ARRY-440, was designed to inhibit class I, II, III, and indel mutant BRAF tumors. Additionally, ARRY-440 was designed to overcome both the paradoxical activation of BRAF as well as inhibit mBRAF:wtCRAF heterodimers, two features that most 1st generation BRAF V600E inhibitors lack. Finally, we were pleased to see that ARRY-440 led to clinical responses in a variety of different BRAF V600E tumors, including in some tumors that harbored both a BRAF V600E mutation and an oncogenic NRAS mutation. Citation Format: Dean Kahn. Discovery of brain-penetrant inhibitors for the treatment of BRAF mutant tumors [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 IA020.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".