Real-world dose adjustment study of first-line afatinib in pts with <i>EGFR</i> mutation-positive (<i>EGFR</i>m+) advanced NSCLC.
Bibliographic record
Abstract
e21060 Background: Tolerability-guided dose adjustment of afatinib reduced the incidence and severity of adverse drug reactions (ADRs) without affecting efficacy in the LUX-Lung (LL) studies in EGFRm+ NSCLC. We report the impact of afatinib dose modifications on efficacy and safety in a real-world setting. Methods: This non-interventional, observational, multi-country/site study used medical records of TKI-naïve pts with EGFRm+ (Del19/L858R) NSCLC treated with first-line afatinib. Primary outcomes were % pts with ADRs by severity, time on treatment, and time to progression (TTP; where reported), relative to LL3. Secondary outcomes were % pts with/reasons for modified starting dose. Results: 228 pts from 13 countries were included. Baseline characteristics were in line with LL3, but with more Del19 pts (78% vs 49%); 12% had ECOG PS 2–3. 31% started with <40 mg, mainly due to the pt’s condition. Dose modifications were more frequent in females, older pts, Eastern Asian pts, and lower body weight pts. 51% of pts were still on treatment; main reason for discontinuation was PD (33% overall). 67% of ≥40 mg starters underwent dose reductions, with 86% of those occurring in the first 6 mos. 12% (28) pts increased dose. The main reason for dose modification was ADRs. In ≤30 mg starters, overall ADR incidence was similar to ≥40 mg starters, with fewer G3 (17% vs 25%) and no G4 ADRs. There were no new safety signals, and fewer ≥G3 ADRs and SAEs than in LL3 (25% vs 49% and 5% vs 14%). >60% of the pts received medications to treat diarrhea and manage skin AEs. Median time on treatment and TTP was 18.7 mos and 20.8 mos respectively and was not impacted by reduced starting dose or dose modification (19.4/17.7/19.5 and 25.9/20.0/29.0 mos for pts who started on ≤30 mg/reduced to <40 mg/remained on ≥40 mg). Conclusions: As in LL trials, real-world afatinib dose adjustments reduced the frequency and intensity of ADRs without impacting efficacy. Time on treatment/TTP were similar regardless of dose adjustment or reduced starting dose, confirming the efficacy of this regimen with an acceptable safety profile. The results highlight the benefit of tailoring afatinib dose based on individual pt characteristics and ADRs to optimize outcomes. Clinical trial information: NCT02751879.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".