Emerging Toxicities of Antibody-Drug Conjugates for Breast Cancer: Clinical Prioritization of Adverse Events from the FDA Adverse Event Reporting System.

BACKGROUND: Antibody-drug conjugates (ADCs) are gaining widespread use in the treatment of breast cancer, although toxicity remains an underexplored issue in the real-world clinical setting. Individual case safety reports collected in large pharmacovigilance databases can advance our knowledge on their safety profile in routine clinical practice. OBJECTIVE: We prioritized adverse events (AEs) reported with ADCs approved for breast cancer using the Food and Drug Administration Adverse Event Reporting System (FAERS). METHODS: We assessed clinical priority of AEs reported in FAERS (February 2013-March 2022) for trastuzumab emtansine (T-DM1), trastuzumab deruxtecan (T-DXd), and sacituzumab govitecan (SG) by attributing a score to each AE disproportionally reported with ADCs. Four criteria were assessed: clinical relevance, reporting rate, reported case fatality rate, and stability of disproportionality signals (consistency of the reporting odds ratio across multiple analyses using three different comparators). RESULTS: We retained 6589 reports (77.4% referring to T-DM1 as suspect), and 572 AEs generated a disproportionality signal in at least one analysis. The majority of these AEs (62%) were classified as moderate clinical priorities (e.g., interstitial lung disease with T-DXd, thrombocytopenia, peripheral neuropathy with T-DM1, febrile neutropenia, and large intestine perforation with SG). Three AEs emerged as high clinical priorities (6 points): septic shock and neutropenic colitis with SG (N = 8 and 13, with median onset 13 and 10 days, respectively), without co-reported immunosuppressive agents; and pulmonary embolism with T-DM1 (N = 31, median onset 109 days, 52% with reported metastasis). CONCLUSION: The heterogeneous spectrum of post-marketing toxicities for ADCs used in breast cancer, as emerging from the FAERS, is largely in line with preapproval evidence. Although causality cannot be proved, we call for increased awareness by oncologists on potential serious unexpected reactions, including early onset of septic shock and neutropenic colitis with SG, and late emergence of pulmonary embolism with T-DM1.

Enhancing Transparency in Defining Studied Drugs: The Open-Source Living DiAna Dictionary for Standardizing Drug Names in the FAERS.

INTRODUCTION: In refining drug safety signals, defining the object of study is crucial. While research has explored the effect of different event definitions, drug definition is often overlooked. The US FDA Adverse Event Reporting System (FAERS) records drug names as free text, necessitating mapping to active ingredients. Although pre-mapped databases exist, the subjectivity and lack of transparency of the mapping process lead to a loss of control over the object of study. OBJECTIVE: We implemented the DiAna dictionary, systematically mapping individual free-text instances to their corresponding active ingredients and linking them to the World Health Organization Anatomical Therapeutic Chemical (WHO-ATC) classification. METHODS: We retrieved all drug names reported to the FAERS (2004-December 2022). Using existing vocabularies and string editing, we automatically mapped free text to ingredients. We manually revised the mapping and linked it to the ATC classification. RESULTS: We retrieved 18,151,842 reports, with 74,143,411 drug entries. We manually checked the first 14,832 terms, up to terms occurring over 200 times (96.88% of total drug entries), to 6282 unique active ingredients. Automatic unchecked translations extend the standardization to 346,854 terms (98.94%). The DiAna dictionary showed a higher sensitivity compared with RxNorm alone, particularly for specific drugs (e.g., rimegepant, adapalene, drospirenone, umeclidinium). The most prominent drug classes in the FAERS were immunomodulating (37.40%) and neurologic drugs (29.19%). CONCLUSION: The DiAna dictionary, as a dynamic open-source tool, provides transparency and flexibility, enabling researchers to actively shape drug definitions during the mapping phase. This empowerment enhances accuracy, reproducibility, and interpretability of results.