Evaluating seizures associated with novel antineoplastic agents during breast cancer treatment using the Food and Drug Administration Adverse Event Reporting System and Canada Vigilance Adverse Reaction Online Database
Bibliographic record
Abstract
Background: There is a rising incidence of neurological adverse events (AEs), such as seizures, associated with novel anticancer agents, warranting investigation. Large-scale studies assessing seizure risk across diverse anticancer drug classes, particularly in breast cancer (BC), remain limited. Objective: This study aimed to systematically evaluate the association between seizures and 14 novel anticancer agents used in BC treatment, compared with traditional chemotherapy, utilizing international pharmacovigilance databases. Design: A large-scale, real-world pharmacovigilance study using data from the US FDA Adverse Event Reporting System (FAERS) and the Canada Vigilance Database (from Q1 2004 to Q1 2025). Methods: Disproportionality analysis was employed to calculate reporting odds ratios (RORs) for identifying significant seizure AE signals. Signals were assessed at both the Standardised MedDRA Query and Preferred Term levels. Pan-cancer transcriptomic data from The Cancer Genome Atlas were integrated to explore biological pathways correlated with drug-induced seizures. Results: Significant and consistent seizure signals were identified for five agents-Lapatinib, Tucatinib, Trastuzumab, Trastuzumab Emtansine (T-DM1), and Atezolizumab-across both databases. In FAERS, over 50% of seizures occurred after 100 days of treatment (median: 68 days); however, fatal cases exhibited a significantly shorter median onset time. Novel agents demonstrated disproportionately higher seizure reporting signals compared to traditional chemotherapy. Pan-cancer analysis revealed negative correlations between seizure RORs and pathways, including asthma and the pentose phosphate pathway. Conclusion: This dual-database pharmacovigilance study identifies potential associations between seizures and five novel BC therapies, underscoring the need for vigilant monitoring during their clinical use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".