{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:10:28Z","timestamp":1788952228903,"version":"build-2803163510"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100032579","name":"Beijing Medical Award Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100032579","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014418","name":"Harbin Medical University Cancer Hospital","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Antibody\u2013drug 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The primary dataset included 3,962\u00a0T-DM1, 6,452\u00a0T-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\u20134.09) and gastrointestinal toxicity (aROR, 2.45; 95% CI, 2.07\u20132.90), whereas sacituzumab govitecan showed higher reporting odds for hematologic (aROR, 1.95; 95% CI, 1.67\u20132.28) and gastrointestinal toxicity (aROR, 2.91; 95% CI, 2.44\u20133.46). 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%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2026.1941296","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:17:19Z","timestamp":1788949039000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Drug-specific safety signal prioritization of antibody-drug conjugates in breast cancer: integrating FAERS pharmacovigilance, machine learning, and clinical contextualization"],"prefix":"10.3389","volume":"17","author":[{"given":"Yuhan","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Oncology, Harbin Medical University Cancer Hospital","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaochun","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Oncology, Harbin Medical University Cancer Hospital","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingze","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Oncology, Harbin Medical University Cancer Hospital","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Oncology, Harbin Medical University Cancer Hospital","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxi","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Oncology, Harbin Medical University Cancer Hospital","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Oncology, Harbin Medical University Cancer Hospital","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haolu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of General Surgery, West China Ya\u2019an Hospital, Sichuan University","place":["Ya\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqi","family":"Sui","sequence":"additional","affiliation":[{"name":"Department of Oncology, Harbin Medical University Cancer Hospital","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Pang","sequence":"additional","affiliation":[{"name":"Department of Oncology, Harbin Medical University Cancer Hospital","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Oncology, Harbin Medical University Cancer Hospital","place":["Harbin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1007\/s40264-023-01317-0","article-title":"Developing an artificial intelligence-guided signal detection in the food and drug administration adverse event reporting system (FAERS): a proof-of-concept study using galcanezumab and simulated data","volume":"46","author":"Al-Azzawi","year":"2023","journal-title":"Drug Safety"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1056\/NEJMoa2028485","article-title":"Sacituzumab govitecan in metastatic triple-negative breast cancer","volume":"384","author":"Bardia","year":"2021","journal-title":"N. 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