Disproportionality analysis of ALK inhibitor-induced hemolytic adverse events: a pharmacovigilance study using the FDA Adverse Event Reporting System Database
Bibliographic record
Abstract
Anaplastic lymphoma kinase (ALK) inhibitors have transformed treatment for ALK-rearranged malignancies, particularly non-small cell lung cancer, by disrupting oncogenic signalling. However, hematologic adverse effects, including hemolysis, have emerged as concerns, especially with alectinib. This study evaluates the prevalence of hemolytic events associated with ALK inhibitors using FDA Adverse Event Reporting System (FAERS) data. A retrospective pharmacovigilance analysis was conducted using FAERS data (2013-2023). Disproportionality analysis with OpenVigil 2.1 assessed associations between ALK inhibitors and hemolysis-related events. Reporting odds ratios (RORs) were calculated, with statistical significance defined as ROR > 2.00 and a lower 95% confidence interval (CI) bound > 1.00. Alectinib exhibited strong associations with hemolysis (ROR 24.01, 95% CI: 17.88-32.24; 45 reports) and bilirubin increase (ROR 18.86, 95% CI: 15.92-22.34; 138 reports). Crizotinib and ceritinib showed weaker signals, while brigatinib and lorlatinib had no significant associations. The findings highlight alectinibs potential hemolytic risk, necessitating hematologic monitoring. Proposed mechanisms include immune-mediated hemolysis, direct cytotoxicity, and metabolic variability. Routine hemoglobin and bilirubin assessments, along with clinical vigilance, are essential. Further studies are needed to elucidate mechanisms of causality and optimize patient safety.
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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 | 0.033 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".