Hepatobiliary Adverse Events Associated with Pembrolizumab: A Pharmacovigilance Study from the FDA Adverse Event Reporting System (FAERS) Database
Bibliographic record
Abstract
Background: Immuno-oncology has transformed cancer treatment, with immune checkpoint inhibitors (ICIs) like pembrolizumab playing a key role. While highly effective, these therapies can also cause immune-related adverse events. This study examines the incidence and characteristics of hepatobiliary adverse events (AEs) linked to pembrolizumab, using data from the FDA Adverse Event Reporting System (FAERS). Objective: To investigate the rates of hepatobiliary AEs linked to pembrolizumab, providing insights into the risks of liver and biliary system damage in patients prescribed pembrolizumab. Methods: This study utilized the FAERS database via OpenVigil 2.1. Adverse events (AEs) related to pembrolizumab were identified and compared to those associated with other drugs. Reporting odds ratios (RORs) were calculated to assess the likelihood of hepatobiliary AEs in pembrolizumab-treated patients. Results: In total, 594 hepatic AEs and 181 biliary AEs were identified. Significant hepatic AEs included elevated ALT (ROR 3.00, 95% CI: 2.685–3.351), hepatotoxicity (ROR 6.436, 95% CI: 5.72–7.242), and hepatic cytolysis (ROR 15.721, 95% CI: 13.854–17.84). Immune-mediated hepatitis exhibited the highest ROR of 346.716 (95% CI: 303.568–395.997). For biliary AEs, cholangitis (ROR 19.597, 95% CI: 16.852–22.791) and sclerosing cholangitis (ROR 24.735, 95% CI: 19.888–30.763) were the most prominent. Conclusions: Pembrolizumab is associated with a significant risk of hepatobiliary adverse events, particularly immune-mediated hepatitis and cholangitis. The elevated RORs for these conditions highlight the importance of close monitoring and managing liver and biliary functions in patients undergoing pembrolizumab checkpoint blockade. These findings emphasize the need for personalized treatment strategies to mitigate risks and optimize outcomes in cancer immunotherapy, especially for those with preexisting hepatobiliary conditions.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".