Design of a “Two-in-One” Mutant-Selective Epidermal Growth Factor Receptor Inhibitor That Spans the Orthosteric and Allosteric Sites
Bibliographic record
Abstract
Inhibitors targeting the epidermal growth factor receptor (EGFR) are an effective therapy for patients with non-small cell lung cancer harboring drug-sensitive activating mutations in the EGFR kinase domain. Drug resistance due to treatment-acquired mutations has motivated the development of successive generations of inhibitors that bind in the ATP site. The third-generation agent osimertinib is now a first-line treatment for this disease. Recently, allosteric inhibitors have been developed to overcome drug-resistant mutations that confer a resistance to osimertinib. Here, we present the structure-guided design and synthesis of a mutant-selective lead compound, which consists of a pyridinyl imidazole-fused benzylisoindolinedione scaffold that simultaneously occupies the orthosteric and allosteric sites. The compound potently inhibits enzymatic activity in L858R/T790M/C797S mutant EGFR (4.9 nM), with a significantly lower activity for wild-type EGFR (47 nM). Additionally, this compound achieves modest cetuximab-independent and mutant-selective cellular efficacies on the L858R (1.2 μM) and L858R/T790M (4.4 μM) variants.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".