An Investigation into EGFR Tyrosine Kinase Inhibitors: Adverse Events, Cost and Prescribing Trends
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer death worldwide among men and women, and non-small cell lung cancer (NSCLC) accounts for 85% of all diagnosed lung cancer cases in the United States.Mutations in the epidermal growth factor receptor (EGFR) gene cause continuous activation of the EGFR and its downstream signaling pathways involving cell proliferation and apoptosis, leading to excess cell division and tumor growth in NSCLC.EGFR Tyrosine Kinase Inhibitors (TKIs), a targeted therapy for NSCLC patients with EGFR mutations, bind to the ATP-binding site of EGFR and prevent the activation of downstream signaling pathways and slow or stop the growth of cancerous cells.The objective of this study is to evaluate the performance and the cost of three TKIs -Gefitinib, Afatinib, and Erlotinib -within the real world setting.This evaluation was conducted by assessing trends in reported adverse events, costs, and prescriptions using data collected from the FDA Adverse Event Reporting System (FAERS) and the Medicare Part D Database.This work is novel because it is the first to use both FAERS and the Medicare Part D Database to track trends in the aforementioned variables over multiple years, allowing for a comprehensive analysis of the real world effectiveness and economic impact of the three TKIs.From 2001 to 2024, Gefitinib recorded 8,543 adverse events, Erlotinib recorded 14,725, and Afatinib recorded 6,193.The three TKI treatments cost American patients approximately $2.8 billion in total.Analysis of the data revealed that the adverse event rate was higher among the first generation TKIs in comparison to their second generation counterpart and highlighted the significant financial burden TKI treatment puts on patients.Future work should focus on improving the safety of TKIs by monitoring their adverse events and increasing the affordability of these life saving 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".