Introduction

Breast cancer (BC) is one of the most diagnosed malignancies among women worldwide1. Recent therapeutic advancements have increased the number of available treatment options for this disease. Trastuzumab emtansine (T-DM1) was the most representative antibody–drug conjugate (ADC) for HER2+ BCs until T-DXd development. Owing to its demonstrated efficacy and safety profile in clinical trials2,3,4, T-DM1 has been actively used to treat metastatic HER2+ BCs after progression from docetaxel + trastuzumab + pertuzumab (THP) as a second-line treatment and is the standard treatment for residual disease after neoadjuvant chemotherapy5,6. T-DM1 is an antibody–drug conjugate that combines the humanized monoclonal antibody trastuzumab with the cytotoxic compound DM1, a maytansine derivative, through covalent bonding. T-DM1 distinctively integrates the mechanisms of action of trastuzumab and DM1, demonstrating potent antitumor properties by effectively targeting cells in experimental models resistant to both trastuzumab and lapatinib3. This compound harnesses the precise targeting ability of the monoclonal antibody and the solid cytotoxic effects of DM1, resulting in enhanced synergistic activity against cancer cells7.

According to the Food and Drug Administration (FDA) Label Information, the most frequently reported adverse reactions (frequency > 25%) are fatigue, nausea, musculoskeletal pain, thrombocytopenia, headache, increased transaminase levels, and constipation8. The toxic effects of maytansine, including gastrointestinal issues, central and peripheral nervous system problems, liver function abnormalities, bone marrow suppression, and phlebitis, underline its potential for serious adverse drug reactions (ADRs) when used as a part of T-DM19. Despite these known issues, the broader and prolonged use of therapeutic agents has raised concerns regarding the under detection of ADRs, emphasizing the need for continuous monitoring of potential long-term and cumulative toxicities10. The limited follow-up periods in the initial clinical trials likely underreported long-term hepatic ADRs, and extensive real-world data on the long-term ADRs of T-DM1 are scarce11, indicating a gap in the ongoing monitoring and analysis of its safety profile. Continuous surveillance and detailed evaluation of long-term ADRs are crucial not only for enhancing our understanding of drug safety over extended periods but also for improving patient management strategies to mitigate these risks in daily clinical practice.

Post-marketing surveillance, similar to that provided by spontaneous drug reporting systems, such as the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), offers critical data on real-world drug safety12. These systems can reveal adverse events that may not be apparent under controlled conditions and within the limited timeframe of clinical trials. T-DM1 was the first successful ADC since the development of trastuzumab (an anti-HER2 monoclonal antibody) and has shown remarkable efficacy, including in the EMILIA trial3. Paradoxically, its extended use of T-DM1 in metastatic settings, often going beyond two years, raises the necessity of observing its long-term drug safety in terms of hepatobiliary disorders, which are critical clinical considerations for treatment modification or discontinuation. While previous studies have investigated hepatobiliary toxicity associated with T-DM113,14,15, they primarily focused on severe adverse effects, leaving a gap in addressing the broader clinical needs for comprehensive safety evaluations over extended use.

In this study, we aimed to investigate the hepatobiliary disorders that might be associated with T-DM1 using the FAERS dataset, with an extended focus on understanding prolonged and chronic responses over time. This study aimed to provide deeper insights into the long-term impact of T-DM1 and enhance our knowledge of its safety profile in real-world clinical settings.

Results

Case identification

The initial data from the merged original data files in the demographic table (hereafter referred to as 'DEMO') in the FAERS dataset included 12,669,518 records. After preprocessing steps including data deduplication, the final dataset used for analysis consisted of 11,837,088 records from DEMO and 11,633,752 records from the indication for use table (hereafter referred to as 'INDI') (Fig. 1). We extracted data on suspected ADRs and divided them into two groups: 3387 cases primarily treated with T-DM1 and 11,833,701 cases treated with other drugs. For the subgroup analysis, we first extracted 175,481 distinct cases with BC indication by joining 11,633,752 INDI records. Subsequent data curation steps involved the exclusion of 633 records with matching equal terms between indication preferred terms (PTs) and suspected ADR PTs, which indicated potential reporting discrepancies. This resulted in a refined 174,848 cohort of BC cases with valid records. Within this cohort, 2591 cases were associated with T-DM1 as the primary suspected drug, whereas the remaining 172,239 cases were linked with other drugs as the primary suspect.

Figure 1
figure 1

Flowchart of case identification for suspected ADR reports related to trastuzumab emtansine and subgroup for breast cancer indications in the FAERS database.

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Descriptive analysis

The characteristics of the 11,837,088 cases in the dataset covering all cases and 174,848 cases with BC indication records are displayed in Table 1. In the entire dataset, the T-DM1-related and other drug-related number of cases reported was 3387 and 11,833,701, respectively. Among the T-DM1 cases, 2907 individuals (85.7%) were female, with a median age of 57 years (range, 48–65 years [Q1-Q3]). The proportion of females in the T-DM1 group (6, 218, 977 [52.5%]) was higher than that in the group that used other drugs. Compared with the T-DM1 group, the other drug groups had similar median age (58 years) but a broader age range, ranging from 43 to 70 years. In the subgroup of patients with BC indications, the number of cases reported related to T-DM1 and other drugs was 2519 and 172,329, respectively. In the total cohort, 148,670 individuals (86.3%) were female, and the median age (Q1, Q3) was 61 (51, 70) years. The median age was lower in the T-DM1 group, with a median age of 57 years (48–65 years) than in the other drug groups, with a median age of 61 years (51–70 years). The proportion of females in the T-DM1 group was higher than that in the other drug groups.

Table 1 Descriptive characteristics of adverse events in T-DM1 case reports with breast cancer indications in the Food and Drug Administration Adverse Event Reporting System (FAERS) from January 1, 2013, to December 31, 2022.
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Disproportionality analysis

Table 2 shows the disproportionality signals for hepatobiliary disorders associated with T-DM1 in the database. The hepatobiliary disorder risk reported with T-DM1 was 5.66 times greater than that reported with all other drugs, and its confidence interval (CI) ranged from 2.18 to 2.51. The Information Component (IC) was 2.35, and its 95% credibility interval (Crl) (IC025-IC925) was from 2.18 to 2.51. The results of the disproportionality analysis for each PT included in the hepatobiliary disorder class are shown in Table 2. At PT level, the following potential safety signals were identified: "Alanine aminotransferase increased" (n = 60; ROR (95% CI) = 8.12 (6.29, 10.49); IC (IC025-IC975) = 3 (2.57–3.42)), "Hepatic cirrhosis" (n = 47; ROR (95% CI) = 17.29 (12.95, 23.08); IC (IC025-IC975) = 4.09 (3.6–4.56)), "Hepatic function abnormal" (n = 33; ROR (95% CI) = 6.46 (4.58, 9.11); IC (IC025-IC975) = 2.68 (2.1–3.24)), "Liver disorder" (n = 33; ROR (95% CI) = 5.28 (3.75, 7.44); IC (IC025-IC975) = 2.39 (1.81–2.95)), "Portal hypertension" (n = 33; ROR (95% CI) = 80.31 (56.76, 113.64); IC (IC025-IC975) = 6.28 (5.7–6.84)), "Nodular regenerative hyperplasia" (n = 31; ROR (95% CI) = 296.91 (205.19, 429.63); IC (IC025-IC975) = 8.07 (7.47–8.65)), "Hepatic enzyme increased" (n = 29; ROR (95% CI) = 2.87 (1.99, 4.13); IC (IC025-IC975) = 1.51 (0.89–2.11)), "Hepatotoxicity" (n = 26; ROR (95% CI) = 7.43 (5.05, 10.93); IC (IC025-IC975) = 2.88 (2.23–3.51)), "Liver function test increased" (n = 21; ROR (95% CI) = 5.1 (3.32, 7.84); IC (IC025-IC975) = 2.34 (1.61–3.03)), "Drug-induced liver injury" (n = 20; ROR (95% CI) = 3.94 (2.54, 6.12); IC (IC025-IC975) = 1.97 (1.22–2.68)), "Liver function test abnormal" (n = 20; ROR (95% CI) = 6.17 (3.97, 9.58); IC (IC025-IC975) = 2.62 (1.87–3.32)), "Hepatitis" (n = 16; ROR (95% CI) = 4.99 (3.05, 8.15); IC (IC025-IC975) = 2.31 (1.47–3.09)), "Hyperbilirubinaemia" (n = 14; ROR (95% CI) = 10.01 (5.91, 16.93); IC (IC025-IC975) = 3.31 (2.41–4.14)), "Hepatic failure" (n = 13; ROR (95% CI) = 3.23 (1.87, 5.56); IC (IC025-IC975) = 1.68 (0.75–2.54)), "Cholecystitis" (n = 10; ROR (95% CI) = 6.94 (3.73, 12.92); IC (IC025-IC975) = 2.79 (1.71–3.75)), "Liver injury" (n = 10; ROR (95% CI) = 3.17 (1.71, 5.91); IC (IC025-IC975) = 1.66 (0.59–2.62)), "Venoocclusive liver disease" (n = 10; ROR (95% CI) = 13.09 (7.03, 24.38); IC (IC025-IC975) = 3.7 (2.62–4.66)), "Cholestasis" (n = 9; ROR (95% CI) = 4.31 (2.24, 8.29); IC (IC025-IC975) = 2.1 (0.96–3.11)), "Jaundice" (n = 9; ROR (95% CI) = 2.71 (1.41, 5.22); IC (IC025-IC975) = 1.44 (0.3–2.44)), "Hepatic fibrosis" (n = 7; ROR (95% CI) = 15.52 (7.38, 32.63); IC (IC025-IC975) = 3.95 (2.64–5.05)), "Hepatic steatosis" (n = 7; ROR (95% CI) = 2.9 (1.38, 6.08); IC (IC025-IC975) = 1.53 (0.23–2.64)), "Non-cirrhotic portal hypertension" (n = 7; ROR (95% CI) = 103.72 (48.82, 220.36); IC (IC025-IC975) = 6.65 (5.35–7.76)), "Hepatomegaly" (n = 6; ROR (95% CI) = 4.79 (2.15, 10.67); IC (IC025-IC975) = 2.26 (0.84–3.43)), "Hypertransaminasemia" (n = 6; ROR (95% CI) = 8.98 (4.03, 20.03); IC (IC025-IC975) = 3.16 (1.75–4.33)), "Hepatic cytolysis" (n = 5; ROR (95% CI) = 7.75 (3.22, 18.64); IC (IC025-IC975) = 2.95 (1.39–4.2)), and "Hepatic pain" (n = 5; ROR (95% CI) = 7.67 (3.19, 18.46); IC (IC025-IC975) = 2.93 (1.37–4.19)). In the disproportionality analysis using cases with BC indication records, the risk of hepatobiliary disorder reported with T-DM1 was 3.28 times greater than that reported with other drugs, and its CI was from 2.92 to 3.68 (Table 3). The IC was 1.53, and the 95% Crl ranged from 1.35 to 1.71. At PT level, the following potential safety signals were identified: "Alanine aminotransferase increased" (n = 53; ROR (95% CI) = 3.06 (2.32, 4.05); IC (IC025-IC975) = 1.55 (1.1–2)), "Hepatic cirrhosis" (n = 37; ROR (95% CI) = 13.37 (9.38, 19.04); IC (IC025-IC975) = 3.49 (2.94–4.02)), "Hepatic function abnormal" (n = 29; ROR (95% CI) = 2.4 (1.65, 3.48); IC (IC025-IC975) = 1.22 (0.6–1.82)), "Portal hypertension" (n = 28; ROR (95% CI) = 23.9 (15.53, 36.8); IC (IC025-IC975) = 4.16 (3.52–4.76)), "Nodular regenerative hyperplasia" (n = 26; ROR (95% CI) = 112.32 (60.18, 209.63); IC (IC025-IC975) = 5.43 (4.77–6.05)), "Hepatic enzyme increased" (n = 24; ROR (95% CI) = 2.13 (1.42, 3.2); IC (IC025-IC975) = 1.06 (0.38–1.71)), "Liver disorder" (n = 24; ROR (95% CI) = 2.81 (1.86, 4.23); IC (IC025-IC975) = 1.44 (0.76–2.09)), "Drug-induced liver injury" (n = 21; ROR (95% CI) = 3.63 (2.34, 5.64); IC (IC025-IC975) = 1.8 (1.07–2.49)), "Hepatotoxicity" (n = 21; ROR (95% CI) = 2.56 (1.65, 3.96); IC (IC025-IC975) = 1.31 (0.58–2.01)), "Liver function test increased" (n = 15; ROR (95% CI) = 2.48 (1.48, 4.16); IC (IC025-IC975) = 1.28 (0.4–2.08)), "Hepatitis" (n = 14; ROR (95% CI) = 4.16 (2.42, 7.15); IC (IC025-IC975) = 1.99 (1.08–2.82)), "Liver function test abnormal" (n = 14; ROR (95% CI) = 3.63 (2.12, 6.22); IC (IC025-IC975) = 1.8 (0.9–2.63)), "Hyperbilirubinaemia" (n = 12; ROR (95% CI) = 4.23 (2.36, 7.58); IC (IC025-IC975) = 2.01 (1.03–2.9)), "Cholecystitis" (n = 10; ROR (95% CI) = 5.87 (3.07, 11.2); IC (IC025-IC975) = 2.45 (1.37–3.41)), "Venoocclusive liver disease" (n = 10; ROR (95% CI) = 42.92 (19.46, 94.68); IC (IC025-IC975) = 4.74 (3.66–5.7)), "Cholestasis" (n = 7; ROR (95% CI) = 2.7 (1.27, 5.74); IC (IC025-IC975) = 1.39 (0.09–2.5)), "Non-cirrhotic portal hypertension" (n = 7; ROR (95% CI) = 53.35 (19.85, 143.38); IC (IC025-IC975) = 4.92 (3.62–6.03)), "Hepatic fibrosis" (n = 6; ROR (95% CI) = 17.14 (7, 41.97); IC (IC025-IC975) = 3.8 (2.38–4.97)), "Hepatic cytolysis" (n = 5; ROR (95% CI) = 3.76 (1.53, 9.27); IC (IC025-IC975) = 1.85 (0.29–3.11)), and "Hepatic pain" (n = 5; ROR (95% CI) = 3.61 (1.47, 8.87); IC (IC025-IC975) = 1.8 (0.23–3.05)).

Table 2 Disproportionality analysis results for T-DM1-related drug reactions at the system organ class and preferred term levels in the entire dataset.
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Table 3 Disproportionality analysis results for T-DM1-related adverse drug reactions at the system organ class and preferred term levels in the dataset with breast cancer indications.
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Time-to-onset analysis

Of all the reported cases related to T-DM1, 138 were valid for time-to-onset analysis (Table 4). Box plots illustrating the time of ADR onset with T-DM1 at the Medical Dictionary for Regulatory Activities (MedDRA®) System Organ Class (SOC) and PT levels are shown in Fig. 2a,b, respectively. At the SOC level, the median reported onset time of hepatobiliary disorders associated with T-DM1 was 41 days, while it was 322.5 days for prolonged and chronic hepatobiliary disorders and 301.5 days for suspected prolonged and chronic hepatobiliary disorders, respectively (Table 3). The estimated shape parameters for these disorders were all < 1, with a 95% CI excluding the value of 1, suggesting a decrease in hazard over time. At the PT level, “Alanine aminotransferase increased” and “Hepatic function abnormal” mostly occurred within one month, but the former belonged to the early failure type (estimate of shape parameter: 0.61; 95% CI 0.44–0.78) and the latter was observed to be random (estimate of shape parameter value: 1.13; 95% CI 0.64–1.62). For “hepatic cirrhosis,” the median onset time was 262.5 days, and for “drug-induced liver injury, it was 119 days, with failure rates remaining constant over time. Among the cases with a BC indication, 128 of all the reported cases related to T-DM1 were eligible for time-to-onset analysis (Table 4). The distribution and analysis results of the time-to-onset data showed no differences when compared with the 138 cases from the entire dataset (Fig. 2; Supplementary Fig. 1, Table 4).

Table 4 Time-to-onset analysis results for T-DM1-related adverse drug reactions at the system organ class and the preferred term levels in dataset with all cases and dataset with cases of breast cancer (BC) indications, respectively.
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Figure 2
figure 2

Box plots for onset time of ADRs related to trastuzumab emtansine in the entire data. (a) At the system organ class level, (b) at the preferred term level. For each box plot, Q1, median, and Q3 values are denoted on the right side.

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Discussion

In the realm of HER2-positive metastatic BC treatment, T-DM1 represents a notable advancement in the field of ADCs primarily because of its mechanism of action and efficacy in clinical trials. Although its integration into standard treatment protocols reflects its clinical significance, reports have suggested concerns regarding the cumulative toxicity associated with the long-term administration of T-DM116,17 that should be more thoroughly addressed in the literature regarding its immediate efficacy, underscoring the need for continued research and surveillance to fully understand the long-term impact of T-DM1 and balance its therapeutic benefits against potential risks in clinical decision-making.

Spontaneous drug reporting systems, such as the FAERS, are receiving increasing attention to address these concerns. The FAERS collects reports of adverse events and medication errors and is a crucial tool for post-marketing surveillance. The strengths of the FAERS database include its comprehensive data collection and broad scope18, enabling the identification of potential ADRs that may not be apparent under the controlled conditions of clinical trials. By analyzing such data, researchers can detect patterns and signals of drug-related issues that may require further investigation. It is important to note the limitations inherent in pharmacovigilance studies, such as potential reporting biases and the lack of detailed clinical information, in FAERS19. However, the large sample size and methodological rigor of this study provide a valuable addition to the literature on the safety profile of T-DM1. Nonetheless, we did not normalize the entire list of drug names in the datasets because T-DM1 is a proprietary medication without a generic version, and its trade or ingredient name is rarely ambiguous. Due to the specificity of our research question and analytical methods, as well as the curation efforts already undertaken to standardize terms in the ‘prod_ai’ column of the FAERS data files by the data provider, we were able to effectively separate the target drug from others by searching for T-DM1 references in the drug name and ‘prod_ai’ columns. In addition, the SOC classification was employed to provide a comprehensive view of hepatobiliary disorder related safety profile that may occur with long-term drug use rather than focusing on a specific medical condition20. Appropriate search strategies and the strategy for aggregating search terms, such as the selection between SOC and Standardized MedDRA Queries (SMQs), can vary depending on the specific research question in pharmacovigilance studies. Further discussion is needed to establish standardized guidelines for selecting appropriate search and aggregation strategies that best align with each pharmacovigilance research objective. Future studies should focus on developing real-time data processing techniques to standardize drug names, terms for adverse drug reaction and indications to ensure consistent terminology. This enhances knowledge circulation and ensures a more effective and timely flow of information, thereby facilitating research efforts using the FAERS dataset.

In this study, we found that chronic liver disease was associated with T-DM1 in terms of long-term and profound toxicities, as well as with acute-onset hepatotoxicity, and transient liver function deterioration. This pharmacovigilance study presented a comprehensive comparative analysis of ADRs associated with T-DM1 using the FAERS database, considering the background incidence of ADRs in patients with BC. The robust methodology involved the deduplication of the initial dataset, followed by careful curation to identify valid BC cases, resulting in a refined cohort of 174,848 cases. Within this cohort, 2519 cases associated with T-DM1 as the primary suspected drug were identified. Demographic analysis of this cohort revealed a predominance of female patients, which is consistent with the typical demographic pattern observed in BC. Disproportionality analysis identified a potential safety signal for T-DM1 related to the incidence of hepatobiliary disorders. Conditions such as hepatic cirrhosis, portal hypertension, and nodular regenerative hyperplasia were particularly notable for their high odds ratios, underscoring the need for special consideration of these ADRs in clinical practice.

Specifically, estimating the time to onset of liver-related complications, including hepatic cirrhosis, provides valuable insights that can inform clinical decision making. By understanding the potential timelines for the development of such serious conditions, clinicians could be informed for optimize treatment plans and surveillance strategies to balance efficacy and safety. Early detection and intervention for liver toxicities can prevent progression to more severe outcomes, such as cirrhosis or even hepatic failure. Therefore, studies aimed at identifying early indicators or predictors of liver damage are vital. It not only aids in safeguarding patient health during long-term therapy but also enhances the overall management of treatment-related adverse effects, thereby improving both the quality of life and survival outcomes of breast cancer patients. Therefore, studies assessed the onset time of long-term hepatobiliary disorders, enabling clinicians to provide more informed preemptive care and potentially prevent irreversible complications.

These findings highlighted the importance of increased clinical vigilance when prescribing T-DM1 for long-term use. Additionally, the time-to-onset analysis provided insightful data on the temporal patterns of ADRs. The median onset time of hepatobiliary disorders with T-DM1 was 41 days but extended to over 300 days for prolonged disorders. This pattern indicates that the duration of T-DM1 therapy in long-term prescriptions should be carefully considered. Determining the balance between the therapeutic benefits and potential risks of T-DM1 can contribute to refining the treatment guidelines and optimizing patient safety. These results emphasize the need for further data collection and active discussions to identify a safe and effective treatment window that will ultimately help identify the optimal duration of T-DM1 therapy, where therapeutic benefits are carefully weighed against adverse reactions that are likely to occur.

This study had some limitations. First, T-DM1 targets patients with HER2 BC; however, information regarding specific molecular indicators is not available in the FAERS data. Therefore, cases in our subgroup analysis were restricted to a broader category, BC, as the recorded drug indication, compromising comparability between T-DM1 and other drug groups. Second, spontaneous notifications have inherent limitations, including reporting bias and underreporting. In the FAERS database, reports that considered confounding factors, such as baseline disease affecting patients and related patient characteristics, were either limited or missing. To minimize this limitation, we conducted subgroup analyses of cases with indications for breast cancer, implying a comparison of T-DM1 with the medications explicitly used for the treatment of T-DM1. Disproportionality analysis by therapeutic area may also help reduce confounding by indication21,22. After filtering for BC indications, the proportion of females among the cases was balanced between the T-DM1 and reference groups, contrasting with the substantial difference observed in the entire dataset between the two groups, and the proportion of reports from physicians and pharmacists was elevated. However, further sophisticated analyses are needed to consider the specific indications for the drug of interest and various confounders.

Although T-DM1 remains a crucial therapeutic option for BC, this study highlights the importance of monitoring for hepatobiliary disorders especially for long-term use. These findings should guide clinicians in balancing the therapeutic benefits of T-DM1 against its potential risks and ensure informed decision-making in BC treatment. Specifically, this research seeks to contribute to the ongoing effort to optimize cancer treatment by ensuring that therapeutic decisions are grounded in a thorough and up-to-date understanding of both the efficacy and safety of critical drugs, such as T-DM1.

Methods

Study design and data source

This study aimed to assess the risk of hepatobiliary disorders associated with T-DM1 in a post-marketing setting, with a specific focus on investigating the long-term effects over an extended treatment period. This study utilized the FAERS dataset, a spontaneous report database compiled and provided by the Food and FDA, to monitor the safety of medications and therapeutic biological agents23. FAERS data are publicly released in the form of quarterly data extraction (QDE) files via the FDA website24. These files were organized into seven tables: demographics (DEMO), drug (DRUG), reaction (REAC), outcome (OUTC), indications for use (INDI), therapy dates (THER), and reporting sources (RPSR). The duration of data collection for the study was determined based on the initial authorization of T-DM1 in February 2013 in the U.S.25. Data files from January 1, 2013, to December 31, 2022, were collected and managed using Microsoft SQL Server on a Windows Server 2016 data center operating on the Amazon Relational Database Service.

Data preprocessing

Case deduplication and code mapping

For data preprocessing, case deduplication was performed prior to statistical analysis in accordance with FDA recommendations26. Within the FAERS database, multiple DEMO table records can correspond to one case report owing to follow-up reports that may include updated information. After merging data from different periods using a case identifier, the most recent records were selected. Redundancies were removed based on a combination of the following six fields: event date, age, sex, adverse events, set of administered drugs, and reporting country27. Suspected adverse reaction data in FAERS is categorized using the preferred term (PT) level from the MedDRA®28. MedDRA® is a widely used terminology for classifying adverse drug events (ADEs) developed by the International Council for Harmonization. It has a hierarchical structure with five levels: SOC, high-level group term, high-level term, PT, and Lower-Level Term (LLT)29. For disproportionality analysis, these PTs were matched with their corresponding MedDRA® SOC levels according to a predefined match between the PT and primary SOC in MedDRA® V25.128.

Case definitions and data extraction

Cases treated with T-DM1 were identified in the DRUG table by searching for “ADO-trastuzumab emtansine,” “trastuzumab emtansine,” and “Kadcyla” within both the ‘drugname’ and ‘prod_ai’ fields. Drugs in the FAERS database were reported using four classifications that specified the role of the drug in relation to the reported ADRs: primary suspect drug, secondary suspect drug, concomitant drug, and interacting drug. In this study, only cases labeled as primary suspected drugs based on the ‘role_code’ field were considered.

In our study, the PTs for the hepatic disorder class were defined according to the hierarchy of the MedDRA® dictionary and adjusted using the drug label. Every PT directly classified as a hepatobiliary disorder at the SOC level was included. Additionally, we included ADR PTs that are relevant to hepatic injury and already monitored as indicators of hepatotoxicity on the T-DM1 drug label but classified under other SOCs in MedDRA®. These include terms such as “Alanine aminotransferase abnormal,” “Alanine aminotransferase decreased,” “Alanine aminotransferase increased,” “Alanine aminotransferase,” “Hepatic enzyme abnormal,” “Hepatic enzyme increased,” “Liver function test abnormal,” “Liver function test decreased,” and “Liver function test increased.”8 (Supplementary Table S2) and categorized the PTs associated with chronic responses based on reviews by the two clinicians (Supplementary Table S3).

To adjust for confounding by clinical indications, we defined a subgroup of patients with indication information for breast cancer. BC cases were identified using explicitly recorded information stored in the INDI table, corresponding to the 47 PTs listed in Supplementary Table S1. Consistent with the methodologies delineated in the literature29,30, FAERS data records where the PTs in the REAC and INDI tables matched were considered erroneous owing to inconsistency in reporting and were excluded. To investigate the time to onset of ADRs after T-DM1 exposure, the start dates of drug administration in the THER table and the onset dates of ADRs in the DEMO table were standardized and identically formatted as yyyy-mm-dd. Cases in which the start date of drug administration preceded the onset date of ADRs were excluded.

Statistical analysis

We summarized the characteristics of the reported cases, including age (years), sex, country of reporting, and reporter status. We defined the reference group as the set of all other drugs in the T-DM1 database to compare the reported cases of ADR related to T-DM1 with the background incidence of ADR in the full database31,32,33. To evaluate the signals of disproportionate reporting of hepatobiliary disorders associated with T-DM1-treated cases in the FAERS database, we performed a disproportionality analysis using a case/non-case method. The ROR with its 95% CI, Bayesian IC with the lower limit and upper limit 95% Crl (IC025-IC925, repectively), and the chi-squared (χ2) test were calculated at both the SOC and PT levels21,34,35,36,37 (Supplementary material 1). To reduce the bias of a single algorithm, ROR, IC, and χ2 statistics were considered to detect the signals. When the number of cases was > 3, the lower bound of the 95% CI of the ROR was > 1, IC025 > 0, and the p-value for the χ2 test was < 0.05, the corresponding ADE was considered a potential safety signal21,31. The time-to-onset of ADRs related to T-DM1 was calculated as [onset date of ADRs] − [start date of T-DM1 administration] + 0.538,39. At the SOC and PT levels, with an available sample size of at least 10, we presented box plots of the onset time of each ADR and performed time-to-onset analyses. To represent the failure time rate distribution against time and account for extreme onset time values, we used the Weibull model to estimate the scale and shape parameters39,40,41. The scale parameter indicates the number of days within which an ADR mostly occurred after the T-DM1 start date. The shape parameter of the Weibull distribution indicates whether the failure rate increases (> 1), remains constant (= 1), or decreases (< 1) over time. We analyzed subsets related to prolonged and chronic hepatobiliary disorders and suspected prolonged and chronic hepatobiliary disorders. In addition, we performed subgroup analyses of cases with indications for BC in all analyses. All statistical analyses were performed using the R software (version 4.2.1; R Foundation for Statistical Computing, Vienna, Austria).

Ethics approval and consent to participant

The Institutional Review Board of Sungkyunkwan University waived approval as FAERS is an open-source anonymized database (IRB No. SKKU-2023-11-037). The annotated and examined datasets are available upon reasonable request from the corresponding authors.