The role of afatinib in the management of non-small cell lung carcinoma
Bibliographic record
Abstract
INTRODUCTION: Despite initial patient benefit, drug resistance to first-generation EGFR tyrosine kinase inhibitors (TKIs) is inevitable. One of the key mechanisms responsible for the development of acquired drug resistance is the secondary T790M missense mutation in exon 20 of the EGFR kinase domain. Afatinib is an ATP-competitive small molecule inhibitor that potently and irreversibly inhibits EGFR and mutated EGFR including the T790M variant, as well as other members of the ErbB family in preclinical studies. AREAS COVERED: The authors describe the rationale and provide the preclinical background to afatinib and its potential as a NSCLC therapy. Specifically, the authors detail the drug's pharmaco-kinetic profile and review its clinical efficacy and toxicity profile. EXPERT OPINION: Afatinib is an effective treatment option for therapy-naive advanced NSCLC harboring an activating EGFR mutation. Furthermore, it is also of potential benefit to patients with acquired resistance to EGFR kinase inhibitors. In the future, the authors envision the clinical development of third-generation EGFR mutation-specific inhibitors in NSCLC, which may potentially spare normal tissue toxicity. Nevertheless, afatinib currently represents a bona fide treatment option in the NSCLC therapeutic armamentarium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".