A Real-World Pharmacovigilance Analysis of Lorlatinib-Associated Metabolic Effects Using the FDA Adverse Events Reporting System (FAERS) Database From 2013 to 2024
Bibliographic record
Abstract
OBJECTIVE: The advent of anaplastic lymphoma kinase (ALK) inhibitors, including lorlatinib, has transformed the treatment of ALK-rearranged malignancies. While lorlatinib effectively overcomes resistance mutations and penetrates the central nervous system, its use is associated with metabolic adverse events, including hypercholesterolemia, hypertriglyceridemia, and weight gain. These complications increase cardiovascular risks, disrupt metabolic homeostasis, and may affect therapy adherence. METHODS: This study utilizes data from the FDA Adverse Event Reporting System and employs disproportionality analysis to investigate the prevalence and nature of lorlatinib-associated metabolic adverse events. RESULTS: Significant associations were identified between lorlatinib and lipid-related adverse events, including hypercholesterolemia (reporting odds ratio [ROR] = 98.46; 95% CI: 79.28-122.29), hypertriglyceridemia (ROR = 66.10; 95% CI: 49.60-88.11), increased body mass index (ROR = 81.57; 95% CI: 48.87-136.14), and increased blood cholesterol (ROR = 23.42; 95% CI: 19.69-27.86). Additional associations were noted for increased blood triglycerides (ROR = 28.14; 95% CI: 22.15-35.75) and dyslipidemia (ROR = 53.60; 95% CI: 38.51-74.60). CONCLUSION: These findings highlight the need for proactive monitoring and management of metabolic side effects in patients receiving lorlatinib. A multidisciplinary approach-incorporating pharmacologic interventions, lifestyle modifications, and regular monitoring-is essential to mitigate metabolic risks. This study enhances the understanding of lorlatinib's safety profile and informs clinical strategies to balance efficacy and tolerability in ALK inhibitor therapy.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| 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".