Third-generation epidermal growth factor receptor tyrosine kinase inhibitors for the treatment of non-small cell lung cancer
Bibliographic record
Abstract
) gene are the most common targetable genomic drivers of non-small cell lung cancer (NSCLC), occurring in approximately 50% and 10-15% of adenocarcinomas of the lung in Asian and Western populations, respectively. The most common EGFR-activating mutations, the exon 19 deletion and the L858R point mutation occurring in the receptor tyrosine kinase domain, are susceptible to inhibition. The first EGFR tyrosine kinase inhibitors (TKIs) to be evaluated were the reversible first-generation EGFR TKIs, gefitinib and erlotinib, followed by the irreversible second-generation EGFR TKIs, afatinib and dacomitinib. The study of acquired resistance mechanisms to first- and second-generation EGFR TKIs in patients with activating EGFR-mutated NSCLC identified the gatekeeper T790M point mutation, present in over 50% of cases, as the most common mechanism of acquired resistance. The need to overcome this resistance mechanism led to the development of third-generation EGFR TKIs, of which osimertinib is the only one to date with regulatory approval. In this review, we present the clinical context leading to the development of third-generation EGFR TKIs, the mode of action of these inhibitors and the clinical data supporting their use. We review third-generation TKI agents that are approved, in development, and those that failed in clinical trials. Finally, we will touch upon ongoing studies and future directions, such as combination treatment strategies, currently being explored to improve the efficacy of treatment with third-generation EGFR TKIs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".