Risk Signals of Antibody-Drug Conjugates in Bladder Cancer: A Real-World FAERS Study
Bibliographic record
Abstract
Jinming Liu,1– 3,* Guowang Li,1,2,* Jia Yang,1– 3 Binxu Sun,1– 3 Shanqi Guo1– 3 1Department of Oncology, First Teaching Hospital, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 2National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 3Tianjin Cancer Institute of Traditional Chinese Medicine, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Binxu Sun, Department of Oncology, First Teaching Hospital, Tianjin University of Traditional Chinese Medicine, Tianjin, 300380, People’s Republic of China, Email sunbinxu@126.com Shanqi Guo, Department of Oncology, First Teaching Hospital, Tianjin University of Traditional Chinese Medicine, Tianjin, 300380, People’s Republic of China, Email shanqi.guo@tmu.edu.cnBackground: Antibody-drug conjugates (ADCs) represent a transformative class of therapeutics for advanced bladder cancer. However, their real-world safety profiles are not yet fully characterized.Methods: This retrospective pharmacovigilance study analyzed data from the FDA Adverse Event Reporting System (FAERS) from the first quarter of 2004 to the third quarter of 2024. Disproportionality analyses, including the reporting odds ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN), were used to detect significant adverse drug event (ADE) signals for four ADCs in bladder cancer treatment: enfortumab vedotin (EV), sacituzumab govitecan (SG), trastuzumab deruxtecan (DS-8201), and trastuzumab emtansine (T-DM1).Results: Among 494 analyzed reports, EV constituted the majority (91.7%). Distinct safety signals were identified for each ADC: EV was strongly associated with skin disorders and metabolic disturbances; SG was primarily linked to gastrointestinal events, with emerging signals of renal abnormalities; DS-8201 was associated with systemic administration-related issues; and T-DM1 showed signals for respiratory and bleeding events. Notably, oral candidiasis related to EV was not explicitly highlighted in the current prescribing information.Conclusion: This study delineates the safety profiles of ADC therapies for bladder cancer, confirming known risks and identifying potential new signals. The findings highlight the need for ADC-specific monitoring strategies and proactive management protocols to mitigate toxicities, thereby providing essential evidence for clinical decision-making.Keywords: FAERS database, antibody-drug conjugate, bladder cancer, adverse drug event, disproportionality analysis, real-world study
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".