Macrocyclization of Quinazoline-Based EGFR Inhibitors Leads to Exclusive Mutant Selectivity for EGFR L858R and Del19
Bibliographic record
Abstract
Activating mutations in the epidermal growth factor receptor (EGFR) are frequent oncogenic drivers of non-small-cell lung cancer (NSCLC). The most frequent alterations in EGFR are short in-frame deletions in exon 19 (Del19) and the missense mutation L858R, which both lead to increased activity and sensitization of NSCLC to EGFR inhibition. The first approved EGFR inhibitors used for first-line treatment of NSCLC, gefitinib and erlotinib, are quinazoline-based. However, both inhibitors have several known off-targets, and they also potently inhibit wild-type (WT) EGFR, resulting in side effects. Here, we applied a macrocyclic strategy on a quinazoline-based scaffold as a proof-of-concept study with the goal of increasing kinome-wide selectivity of this privileged inhibitor scaffold. Kinome-wide screens and SAR studies yielded 3f, a potent inhibitor for the most common EGFR mutation (EGFR Del19: 119 nM) with selectivity against the WT receptor (EGFR: >10 μM) and the kinome.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".