Adverse Events of PD-1, PD-L1, CTLA-4, and LAG-3 Immune Checkpoint Inhibitors: An Analysis of the FDA Adverse Events Database
Bibliographic record
Abstract
This study aimed to identify the 25 most prevalent adverse events (AEs) associated with FDA-approved immune checkpoint inhibitors (ICIs)-specifically, PD-1, PD-L1, CTLA-4, and LAG-3 inhibitors-using data from the FDA Adverse Events Reporting System (FAERS), a publicly available repository of reported drug adverse events, and AERSMine, an open-access pharmacovigilance tool, to investigate these adverse events. For PD-1 inhibitors, the most common AEs were diarrhea, fatigue, and pyrexia, with notable instances of neutropenia and hypothyroidism, particularly with toripalimab and dostarlimab. PD-L1 inhibitors also frequently caused pyrexia, diarrhea, and fatigue, with interstitial lung disease and hypothyroidism showing a class effect, and drug-specific AEs such as hepatotoxicity and chills. CTLA-4 inhibitors predominantly resulted in diarrhea and colitis, with ipilimumab frequently causing pyrexia and rash, while tremelimumab exhibited unique AEs such as biliary tract infection. The LAG-3 inhibitor relatlimab reported fewer AEs, including pyrexia and pneumonia. Rare but significant AEs across all inhibitors included myocarditis and myasthenia gravis. This study provides a detailed overview of the 25 most common AEs associated with ICIs, offering valuable insights for clinical decision-making and AE management. Further research is necessary to elucidate the mechanisms underlying these AEs and to develop targeted interventions to enhance the safety and efficacy of ICI therapy in patients with cancer.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".