Abstract PR004: Brain penetrant allosteric EGFR inhibitors for NSCLC
Bibliographic record
Abstract
Abstract Approximately 30-50% of Non-Small Cell Lung Cancer (NSCLC) patients already have CNS disease at time of diagnosis or else develop it during treatment, and there remains a need for better outcomes in these patients. The 3rd-generation covalent EGFR inhibitor osimertinib is an effective therapy for NSCLC patients, improving upon 1st-generation inhibitors gefitinib and erlotinib, and achieving enhanced brain penetration relative to those agents. However, the survival benefit relative to the 1st-generation inhibitors is observed primarily in patients with exon19 deletions rather than the L858R mutation. We developed a series of mutant-selective allosteric EGFR inhibitors that specifically target the L858R mutation (and subsequent resistance mutations), with high selectivity over wt EGFR and across the kinome. Early compounds including the isoindolinone JBJ-09-063 demonstrated good in vivo efficacy in mouse tumor models, despite limited bioavailability. Diversification and optimization of the chemistry, involving a scaffold hopping approach and the replacement of the thiazole amide with benzimidazole, delivered a set of compounds with promising potency and rodent PK. A subset of these achieved good brain exposure in mice and efficacy in an intracranial H1975 brain metastasis model. The mutant-selectivity and ability to co-bind with osimertinib makes an allosteric EGFR inhibitor an ideal partner for combination therapy, with the potential to deliver enhanced outcomes in L858R-driven NSCLC, especially for patients with CNS disease. Citation Format: David Scott, Courtney Cullis, Tyler Beyett, Frederic Feru, Thomas Gero, Krista Gipson, Praful Gokhalle, Nathanael Gray, David Heppner, Sampson Huang, Pasi Jänne, Sandeepraj Pusalkar, Michael Eck. Brain penetrant allosteric EGFR inhibitors for NSCLC [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 PR004
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.016 |
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".