A real-world pharmacovigilance analysis of ALK inhibitor-associated pleural and pericardial effusion using the FDA Adverse Events Reporting System (FAERS) database from 2013 to 2024
Bibliographic record
Abstract
INTRODUCTION: The advent of anaplastic lymphoma kinase (ALK) inhibitors has revolutionized the treatment of ALK-rearranged malignancies, establishing these agents as vital components of precision oncology. Despite their proven efficacy in prolonging progression-free and overall survival, ALK inhibitors are associated with notable adverse events, particularly cardiopulmonary complications such as pleural and pericardial effusions. METHODS: This study investigates the real-world prevalence and risk of these effusions associated with five ALK inhibitors, crizotinib, ceritinib, alectinib, brigatinib, and lorlatinib, through disproportionality analysis using the FAERS pharmacovigilance database. RESULTS: The data revealed elevated reporting odds ratios (RORs) for pleural and pericardial effusions, with notable variability among the agents. Crizotinib exhibited RORs of 7.76 (95% CI: 6.60-9.12) and 9.00 (95% CI: 7.10-11.41) for pleural and pericardial effusions, respectively. Ceritinib demonstrated RORs of 7.36 (95% CI: 5.16-10.50) and 10.80 (95% CI: 6.79-17.19), respectively. Alectinib showed lower RORs of 4.76 (95% CI: 3.80-5.97) and 6.67 (95% CI: 4.92-9.04). Brigatinib displayed elevated RORs of 8.70 (95% CI: 6.58-11.52) and 7.87 (95% CI: 4.95-12.51). Lorlatinib presented the highest risk, with RORs of 8.61 (95% CI: 6.72-11.02) and 12.57 (95% CI: 9.08-17.38). CONCLUSIONS: This study highlights the critical need for vigilant pharmacovigilance and a multidisciplinary approach to balance the oncologic benefits of ALK inhibitors against their cardiopulmonary risks. By enhancing awareness and fostering proactive management, these findings aim to support the safe and effective use of ALK inhibitors in treating ALK-rearranged malignancies.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".