Endocrine toxicities in Chimeric Antigen Receptor T-Cell Therapy: A pharmacovigilance study from 2017 to 2023 Quarter 1
Bibliographic record
Abstract
Aim: Chimeric antigen receptor (CAR)-T cell therapy represents a revolutionary immunotherapy and cutting-edge strategy for cancer treatment. However, the pharmacological safety of this approach in the endocrine system has yet to be sufficiently validated. This study aims to explore the potential toxicity signals of CAR T-cell therapy in the endocrine system and their clinical relevance. Methods: This study utilized data from the FDA Adverse Event Reporting System (FAERS) database, covering 2017 to the first quarter of 2023 (Q1). Signal detection of adverse events was achieved through the information component method combined with the reporting odds ratio method. Results: A total of 34,216,716 records were available in the FAERS database, and 60,730 records were screened for CAR T-cell therapy as the primary or secondary suspected agent, identifying 12 positive endocrine signals (preferred term), which represent a rare occurrence in the existing literature on CAR T-cell therapy. Hyperglycemia topped the list with 42 reported cases (ROR025=1.01), followed by hypercalcemia (n=26,ROR025=1.45) and adrenal insufficiency (n=15,ROR025=0.66). Exophthalmos-related reports for tisagenlecleucel therapy showed the highest death rate among the positive signals detected (5/6, 83.3%). Adverse event reports related to conditions with fatal outcomes, such as adrenal insufficiency (7/15, 46.7%), and hypercalcemia (13/25, 52.0%), demonstrate significant overlap with cytokine release syndrome (CRS). Conclusions: It is crucial for healthcare professionals to closely monitor the potential adverse events related to the endocrine system that may arise from CAR T-cell therapy. These events necessitate thorough observation after treatment administration and the creation of targeted prevention and treatment strategies
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; both teacher heads agree on what is shown here.
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".