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◆ Frontiers in pharmacology2026-01-01

Drug-specific safety signal prioritization of antibody-drug conjugates in breast cancer: integrating FAERS pharmacovigilance, machine learning, and clinical contextualization.

Yuhan Tang, Shaochun Liu, Yingze Zhu, Linlin Fan, Xiaoxi Han, Wenjie Ma, Haolu Zhang, Zhiqi Sui, Hui Pang, Wenhui Zhao

一句话结论

The three ADCs showed distinct breast-cancer FAERS safety-reporting patterns. Machine learning provided exploratory report-level prioritization, while institutional observations provided clinical context. These findings support pharmacovigilance signal prioritization but should not be interpreted as incidence, comparative clinical risk, causal effects, or patient-level toxicity prediction.

原始摘要(原文)
BACKGROUND: Antibody-drug conjugates (ADCs) are increasingly used across breast cancer subtypes, but their postmarketing safety-reporting patterns differ across agents and are difficult to interpret across spontaneous-reporting and clinical settings. We integrated breast-cancer-restricted FDA Adverse Event Reporting System (FAERS) analyses, temporal machine-learning prioritization, and an independent institutional cohort to characterize drug-specific safety patterns across complementary evidence layers. METHODS: FAERS quarterly files from 2013Q1 through 2025Q4 were deduplicated and analyzed at the report level. The primary dataset comprised 13,529 breast-cancer primary-suspect reports involving trastuzumab emtansine (T-DM1), trastuzumab deruxtecan (T-DXd), or sacituzumab govitecan; 20,526 all-indication reports were retained for supportive sensitivity analysis. Analyses included clinically reviewed preferred-term disproportionality, 11 prespecified adverse events of special interest (AESIs), intraclass adjusted reporting odds, reported time to onset, and temporally validated exploratory machine-learning models for report-level AESI prioritization. An independent retrospective cohort of 105 patients was analyzed descriptively for clinical contextualization. RESULTS: The primary dataset included 3,962 T-DM1, 6,452 T-DXd, and 3,115 sacituzumab govitecan reports. Compared with T-DM1, T-DXd showed higher adjusted reporting odds for interstitial lung disease (ILD)/pneumonitis (adjusted reporting odds ratio [aROR], 3.23; 95% CI, 2.55-4.09) and gastrointestinal toxicity (aROR, 2.45; 95% CI, 2.07-2.90), whereas sacituzumab govitecan showed higher reporting odds for hematologic (aROR, 1.95; 95% CI, 1.67-2.28) and gastrointestinal toxicity (aROR, 2.91; 95% CI, 2.44-3.46). T-DM1 showed higher hepatobiliary reporting odds than both comparators. Temporal-validation AUROC values ranged from 0.593 to 0.772. In the institutional cohort, hepatobiliary toxicity was most frequent with T-DM1 (32.3%), ILD/pneumonitis with T-DXd (12.0%), and hematologic toxicity with sacituzumab govitecan (66.7%). CONCLUSION: The three ADCs showed distinct breast-cancer FAERS safety-reporting patterns. Machine learning provided exploratory report-level prioritization, while institutional observations provided clinical context. These findings support pharmacovigilance signal prioritization but should not be interpreted as incidence, comparative clinical risk, causal effects, or patient-level toxicity prediction.
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Drug-specific safety signal prioritization of antibody-drug conjugates in breast cancer: integrating FAERS pharmacovigilance, machine learning, and clinical contextualization. — 科研速览 Science Skim