Ingenol Mebutate Is Associated With Increased Reporting Odds for Squamous Cell Carcinoma in Actinic Keratosis Patients, a Pharmacovigilance Study of the FDA Adverse Event Reporting System (FAERS)
Bibliographic record
Abstract
BACKGROUND: Recently the production and marketing of ingenol mebutate in the European Union (EU) and Canada was halted due to a possible increased risk of squamous cell carcinoma (SCC) in patients with actinic keratosis (AK). OBJECTIVE: To investigate the relationship between SCC and topical AK medications including ingenol mebutate in the FDA Adverse Event Reporting System (FAERS). METHODS: Case/non-case analyses were performed in FAERS using data from 2012 to 2020 to examine the reporting odds ratio (ROR) signal for SCC for ingenol mebutate and all classes of topical AK medications under multiple conditions: i. comparison to all other drugs in FAERs, ii. comparison to other topical AK medications, iii. comparison to all other topical AK medications where only a single agent was implicated, iv. comparison of ingenol mebutate vs. imiquimod. RESULTS: A statistically significant ROR for SCC was found for ingenol mebutate under all conditions (i. 31.57 (25.45, 39.16), ii. 50.35 (32.21, 78.82), iii 61.09 (35.36, 105.56), iv. 2.53 (1.27, 5.05). A significant but substantially smaller signal was observed for imiquimod (i. 12.38 (6.42, 32.84), ii. 5.18 (2.61, 10.26), iii 5.42 (2.49, 11.78), but not for fluorouracil or diclofenac. When compared to imiquimod directly, ingenol mebutate had a statistically significant ROR for SCC (2.53 (1.27, 5.05). CONCLUSION: Our findings support an association between SCC and ingenol mebutate. This association is maintained under controls to limit bias and falsely elevated signal including controlling for disease state and cases with multiple drug exposures and when compared to imiquimod as in Phase IV studies of ingenol mebutate.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".