Reported Drug Spectrum and Disproportionality Signals for Malignant Neoplasm Progression in FAERS: A Real-World Pharmacovigilance Study
Bibliographic record
Abstract
Abstract This study aimed to identify drugs disproportionately reported with malignant neoplasm progression, an uncommon but clinically important endpoint, using large spontaneous reporting systems. Public reports were analyzed from the FDA Adverse Event Reporting System (FAERS; 2004Q1–2024Q4) and the Japanese Adverse Drug Event Report database (JADER; 2004–2024). Cases were defined using MedDRA Preferred Terms for malignant neoplasm/tumour progression, and reports in which progression was recorded as an indication or medical history were excluded. Suspected drugs were standardized to generic names, and disproportional reporting was quantified using reporting odds ratios (RORs). Signals identified in FAERS were examined in JADER for cross-validation.FAERS contained 321, 020 progression-related reports, corresponding to 84, 977 unique cases after deduplication. Reporting increased over time and was associated with severe outcomes (death 27.63%; hospitalization 13.66%). Among the 50 drugs prioritized by report volume and signal strength, most were anticancer or immunomodulating agents (64%), and the highest report counts involved pembrolizumab, nivolumab, carboplatin, and enzalutamide. Disproportionality analysis detected positive signals for 41 drugs, with the strongest signals observed for afatinib, gefitinib, and osimertinib. In JADER (8, 929 cases), 22 of the 41 FAERS-positive signals were replicated with consistent direction but different magnitude.These findings are hypothesis-generating and suggest that tumor progression reporting clusters around specific therapies, particularly immunotherapies and targeted agents.These results support closer post marketing monitoring of selected drug event pairs and incentivize epidemiological and case-control studies to validate signals and elucidate clinical significance.
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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".