Enozertinib Is a Selective, Brain-Penetrant EGFR Inhibitor for Treating Non–Small Cell Lung Cancers with EGFR Exon 20 and Atypical Mutations
Bibliographic record
Abstract
EGFR mutations are common oncogenic drivers in non-small cell lung cancer (NSCLC), and approximately half of patients develop brain metastases over the course of their disease. Patients with nonclassic EGFR mutations, such as insertions in exon 20, are a high unmet need with a worse prognosis compared with patients with classic EGFR mutations. Here, we describe the discovery and development of enozertinib (formerly ORIC-114), a highly brain-penetrant, orally bioavailable, irreversible inhibitor that targets EGFR exon 20 mutations with unparalleled kinome selectivity. Preclinical studies revealed strong potency and tumor regressions driven by enozertinib across a broad range of atypical EGFR-mutant models. In a phase I clinical trial of enozertinib in patients with advanced NSCLC bearing atypical mutations in EGFR, a patient harboring an EGFR exon 20 insertion experienced sustained complete response of all systemic and brain metastases. Together, these findings identify enozertinib as a promising investigational inhibitor to address the unmet need for brain-penetrant therapies in NSCLC with EGFR exon 20 insertions or other atypical mutations. SIGNIFICANCE: Preclinical and initial phase I clinical data demonstrate the potency, kinome selectivity, efficacy, and brain penetration of enozertinib in NSCLC with EGFR exon 20 insertions and atypical mutations, warranting further clinical development.
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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) | 0.001 | 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".