Selectivity profile of afatinib for EGFR-mutated non-small-cell lung cancer
Bibliographic record
Abstract
EGFR-mutated non-small-cell lung cancer (NSCLC) has long been a research focus in lung cancer studies. Besides reversible tyrosine kinase inhibitors (TKIs), new-generation irreversible inhibitors, such as afatinib, embark on playing an important role in NSCLC treatment. To achieve an optimal application of these inhibitors, the correlation between the EGFR mutation status and the potency of such an inhibitor should be decoded. In this study, the correlation was profiled for afatinib, based on a cohort of patients with the EGFR-mutated NSCLC. Relying on extracted DNAs from the paraffin-embedded tumor samples, EGFR mutations were detected by direct sequencing. Progression-free survival (PFS) and the response level were recorded as study endpoints. These PFS and response values were analyzed and correlated to different mutation types, implying a higher potency of afatinib to classic activation mutations (L858R and deletion 19) and a lower one to T790M-related mutations. To further bridge the mutation status with afatinib-related response or PFS, we conducted a computational study to estimate the binding affinity in a mutant-afatinib system, based on molecular structural modeling and dynamics simulations. The derived binding affinities were well in accordance with the clinical response or PFS values. At last, these computational binding affinities were successfully mapped to the patient response or PFS according to linear models. Consequently, a detailed mutation-response or mutation-PFS profile was drafted for afatinib, implying the selective nature of afatinib to various EGFR mutants and further encouraging the design of specialized therapies or innovative drugs.
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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 | 0.001 |
| 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".