---
title: "Entrectinib safety signals in NSCLC: Pharmacovigilance analysis of FAERS and JADER"
id: "plos-one-15-safety-profile-of-entrectinib-in-nsclc-multi-source-pharmacovigilance-analysis"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-15-safety-profile-of-entrectinib-in-nsclc-multi-source-pharmacovigilance-analysis"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358331"
published_at: "2026-09-15T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Entrectinib safety signals in NSCLC: Pharmacovigilance analysis of FAERS and JADER
## Provenance & Clinical Metadata
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- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358331)
- **Published At:** 2026-09-15T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This multi-source pharmacovigilance study analyzed entrectinib-associated adverse event (AE) reports for non-small cell lung cancer (NSCLC) from FAERS (2020Q1–2025Q4; 520 patients; 1,573 PT events) and JADER (2020Q1–2025Q3; 254 patients; 393 PT events). - Four disproportionality methods were applied, with **reporting odds ratio (ROR)** as the primary signal criterion; MedDRA v28.1 coding was used. - At the system organ class (SOC) level, reproducible positive signals across both databases included **nervous system disorders**, **cardiac disorders**, and **renal and urinary disorders** (FAERS/JADER RORs: 4.48/4.76; 3.20/5.43; 2.04/3.92 respectively). - At the preferred-term (PT) level, 23 signals were detected in both databases; 58 PTs were unique to FAERS and 8 to JADER. Shared PT signals included dizziness, taste disorder, renal impairment, blood creatinine increased, cardiac failure, cognitive disorder, ataxia, syncope, myocarditis, QT prolonged, and hyperuricaemia with reported RORs listed per database in the source. - Subgroup analyses indicated exploratory age- and sex-related reporting heterogeneity: more renal and mobility-related reports in older patients and a female predominance for ataxia reports in FAERS. - Time-to-onset analysis in FAERS (203/520 reports with valid onset data) found a median onset of 13 days; 70.94% of evaluable reports had onset within 30 days and Weibull modeling suggested an early-failure pattern. - The authors emphasize these findings are hypothesis-generating signal detections and do not establish incidence or causality; molecular fusion status was not available for verification. - Data and derived analytic tables from the study are provided as supporting information; raw FAERS and JADER files are publicly available per the source.
## Clinical Analysis & Structured Key Points
Safety profile of entrectinib in NSCLC: Multi-source pharmacovigilance analysis using FAERS and JADER | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Peer Review Reader Comments Figures Figures Abstract Background Entrectinib is effective for ROS1-positive non-small cell lung cancer (NSCLC), but its postmarketing safety profile remains incompletely characterized outside clinical trials. We conducted a dual-database pharmacovigilance study to characterize real-world reporting patterns, identify cross-database replicated adverse event signals, explore age- and sex-related reporting heterogeneity, and evaluate time-to-onset patterns. Methods Reports were retrieved from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS; 2020Q1–2025Q4; 520 patients; 1,573 preferred-term [PT] events) and the Japanese Adverse Drug Event Report database (JADER; 2020Q1–2025Q3; 254 patients; 393 PT events). Molecular fusion status was not available for verification. Four disproportionality methods were applied, with reporting odds ratio (ROR) as the primary signal criterion. Results At the system organ class level, shared positive signals involved nervous system disorders (FAERS/JADER ROR: 4.48/4.76), cardiac disorders (3.20/5.43), and renal and urinary disorders (2.04/3.92). At the PT level, 23 signals were detected in both databases, whereas 58 PTs were unique to FAERS and 8 to JADER. Representative shared PT signals included dizziness (12.94/14.33), taste disorder (38.03/13.28), renal impairment (6.89/8.18), blood creatinine increased (8.31/24.99), cardiac failure (7.57/8.90), cognitive disorder (18.04/78.29), ataxia (51.73/351.45), syncope (8.87/89.45), myocarditis (3.47/5.91), electrocardiogram QT prolonged (4.01/5.35), and hyperuricaemia (7.36/59.63). Subgroup analyses suggested exploratory age- and sex-related reporting heterogeneity, including relatively more renal and mobility-related reports in older patients and female predominance for ataxia in FAERS. In the FAERS-based time-to-onset analysis, 203 of 520 reports (39.0%) had valid onset data. The median time to onset was 13 days, 70.94% of evaluable reports had onset dates within 30 days, and Weibull analysis suggested an early-failure pattern. Conclusions Entrectinib-associated reports in NSCLC showed reproducible neurologic, cardiac, renal, and laboratory-related disproportional reporting signals across FAERS and JADER. These findings may help prioritize early safety monitoring but should be interpreted as hypothesis-generating signals rather than evidence of incidence or causality. Citation: Yang J, Liu J, Zhao G, Li H, Sun G (2026) Safety profile of entrectinib in NSCLC: Multi-source pharmacovigilance analysis using FAERS and JADER. PLoS One 21(9): e0358331. https://doi.org/10.1371/journal.pone.0358331 Editor: Qi Chen, University of Kansas Medical Center, UNITED STATES OF AMERICA Received: June 2, 2026; Accepted: August 31, 2026; Published: September 15, 2026 Copyright: © 2026 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The raw FAERS and JADER data used in this study are publicly available. FAERS quarterly data files can be accessed via the U.S. Food and Drug Administration ( https://www.fda.gov/drugs/drug-approvals-and-databases/fda-adverse-event-reporting-system-faers-database ), and JADER data files are available from the Pharmaceuticals and Medical Devices Agency of Japan ( https://www.info.pmda.go.jp/fukusayoudb/CsvDownload.jsp ). The de-duplicated, event-level analytic datasets generated for this study are provided as Supporting Information ( S1 Dataset and S2 Dataset ). The underlying derived signal-detection tables are provided as S1 – S8 Tables . Funding: G.S. received funding from the cultivating scientific research project of the Second Hospital of Dalian Medical University (grant number YJ20250033). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. 1 Introduction Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85%–90% of all cases [ 1 ]. ROS1 fusion occurs in approximately 1%–2% of NSCLC cases and defines a clinically actionable molecular subset [ 2 , 3 ]. The development of entrectinib, a potent ROS1-targeted tyrosine kinase inhibitor, has expanded therapeutic options for ROS1-positive NSCLC and improved disease control, particularly in patients with intracranial involvement [ 3 , 4 ]. This agent has demonstrated clinically meaningful activity in both treatment-naïve and previously treated patients with advanced disease [ 4 ]. Despite these advances, the safety profile of ROS1 inhibitors, particularly entrectinib, warrants further investigation [ 3 , 4 ]. Clinical trials provide important safety data [ 5 ]; however, they are typically conducted in selected patient populations and may not fully capture the spectrum of adverse events (AEs) observed in real-world practice [ 6 ]. To address this gap, we analyzed pharmacovigilance data from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report (JADER) database [ 7 , 8 ]. These systems provide complementary postmarketing data from distinct reporting settings and may facilitate the identification of safety signals that are not readily captured in clinical trials [ 9 ]. Building upon these considerations, we conducted a multi-source pharmacovigilance study to move beyond descriptive signal listing from a single spontaneous reporting system [ 10 ]. By integrating complementary data from FAERS (2020Q1–2025Q4) and JADER (2020Q1–2025Q3), we aimed to characterize entrectinib-associated adverse events at both the system organ class (SOC) and preferred term (PT) levels using four disproportionality methods—reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS). We further examined time-to-onset patterns using a Weibull distribution model and explored age- and sex-related heterogeneity through stratified subgroup analyses. Our aim was to identify reproducible pharmacovigilance signals associated with entrectinib, distinguish them from database-specific reporting fluctuations, and provide hypothesis-generating evidence to support earlier and more structured safety monitoring in routine practice [ 11 ]. 2 Methods 2.1 Data source and collection The FAERS is one of the largest spontaneous adverse event reporting systems worldwide [ 12 ]. It collects and stores adverse event (AE) reports submitted by healthcare professionals, patients, and manufacturers. As a key resource for postmarketing pharmacovigilance, FAERS supports drug safety monitoring in routine clinical practice and provides safety data across diverse populations and settings. The database comprises multiple tables, including demographics (DEMO), drug information (DRUG), adverse reactions (REAC), patient outcomes (OUTC), report sources (RPSR), therapy start and end dates (THER), and indications (INDI); these tables are linked using the unique CASEID identifier [ 13 ]. The JADER database is maintained by the Pharmaceuticals and Medical Devices Agency (PMDA) of Japan [ 8 ]. It collects adverse drug event reports submitted by pharmaceutical companies and medical institutions and serves as an important resource for postmarketing drug safety surveillance in Japan. JADER consists of four datasets: demographics (DEMO), drug information (DRUG), adverse reactions (REAC), and patient history (HIST) [ 14 ]. These datasets were linked using the unique case identifier provided in JADER to construct unified case-level records before subsequent filtering and analysis. We conducted a multi-source pharmacovigilance analysis using FAERS data from 2020Q1 to 2025Q4 and JADER data from 2020Q1 to 2025Q3. The FAERS quarterly data files were downloaded on March 15, 2026, and the JADER data files were downloaded on March 15, 2026. In FAERS, reports were retrieved using the generic name entrectinib and the brand name Rozlytrek, with entrectinib designated as the primary suspect drug and the indication restricted to non-small cell lung cancer (NSCLC). In JADER, reports were retrieved using the Japanese drug name “エヌトレクチニブ”, with entrectinib likewise designated as the primary suspect drug and the indication restricted to NSCLC cases. For FAERS, duplicate reports were addressed according to the FDA-recommended deduplication procedure, which prioritizes the most recent report version for each case on the basis of the unique CASEID and FDA_DT field, that is, the date on which the report was received by the FDA, to ensure retention of only the latest and most complete record [ 15 ]. No duplicate FAERS reports were ultimately retained after screening. This process yielded 520 eligible patients and 1,573 PT events. For JADER, duplicate records were removed before database linkage, resulting in 254 unique patients. After matching, a total of 393 PT events were identified. All AEs were coded according to the Medical Dictionary for Regulatory Activities (MedDRA), Version 28.1. AEs were mapped to PTs and corresponding SOCs for subsequent analyses. Because spontaneous reporting systems include both toxicity-related terms and disease-course-related terms, PTs such as “disease progression” were retained in the initial signal-detection output when they met the statistical criteria, but were categorized as disease-course-related reporting terms rather than toxicity-specific AE signals for clinical interpretation. These terms were therefore not considered part of the core toxicity profile of entrectinib. A schematic overview of the study workflow is presented in Fig 1 . Download: PNG larger image TIFF original image Fig 1. Data processing flowchart for entrectinib reports in FAERS and JADER. Panel A shows the data processing workflow for FAERS; panel B shows the data processing workflow for JADER. Abbreviations: FAERS, FDA Adverse Event Reporting System; JADER, Japanese Adverse Drug Event Report database; DEMO, demographics; DRUG, drug information; REAC, adverse reactions; PT, preferred term. https://doi.org/10.1371/journal.pone.0358331.g001 Because this study used publicly available and de-identified pharmacovigilance data from FAERS and JADER, it did not involve direct contact with human participants or access to identifiable individual-level patient information. Therefore, approval from an institutional ethics review committee and informed consent were not required. 2.2 Statistical analysis Four established disproportionality methods—reporting odds ratio (ROR) [ 15 ], proportional reporting ratio (PRR) [ 16 ], Bayesian confidence propagation neural network (BCPNN) [ 17 ], and multi-item gamma Poisson shrinker (MGPS) [ 18 ] —were applied for signal detection. ROR was selected as the primary criterion for defining positive signals because it is widely used in spontaneous reporting system analyses, is straightforward to interpret, and allows comparison with previous pharmacovigilance studies [ 19 ]. PRR, BCPNN, and MGPS were used as complementary methods to evaluate the robustness of the detected signals under different disproportionality assumptions. Because these algorithms differ in statistical principles, shrinkage behavior, and sensitivity to sparse counts, complete overlap across methods was not expected. Therefore, signals meeting the ROR criterion were considered the primary signal set, whereas signals additionally supported by PRR, BCPNN, and/or MGPS were regarded as more robust. Conversely, PTs detected by only one method, particularly those based on sparse counts, were interpreted cautiously as exploratory and hypothesis-generating rather than definitive safety findings. For each database, disproportionality analyses were performed within the NSCLC reporting background rather than against the entire spontaneous reporting database [ 20 ]. The target group consisted of reports in which entrectinib was recorded as the primary suspect drug for NSCLC-related indications. The reference set consisted of NSCLC-related reports involving all other drugs after excluding entrectinib-related reports. Therefore, the estimated RORs reflected disproportionate reporting of PTs for entrectinib relative to other drug reports within the same disease-reporting background, rather than relative to the entire FAERS or JADER database. For each PT, a two-by-two contingency table was constructed separately in FAERS and JADER. In this event-level disproportionality analysis, “a” represented the number of target PT events in the entrectinib-NSCLC target group, “b” represented other PT events in the target group, “c” represented target PT events in the reference set, and “d” represented other PT events in the reference set [ 21 ]. The event-level contingency table structure and PT event totals are shown in S1 Table . A PT was considered a positive signal when the number of reported events was at least 3 (a ≥ 3) and the lower bound of the 95% confidence interval (CI) for the ROR exceeded 1. Detailed formulas and signal-detection criteria for ROR, PRR, BCPNN, and MGPS are provided in S2 Table . Because disproportionality analysis is intended for pharmacovigilance signal detection rather than confirmatory hypothesis testing, no formal multiplicity adjustment was applied; therefore, signals detected in only one database, by only one method, or based on sparse counts were interpreted as exploratory and hypothesis-generating [ 22 ]. Particular caution was applied when interpreting low-frequency signals with extremely large RORs and wide confidence intervals. Although such signals were retained in the complete supplementary signal lists for transparency, PTs with sparse counts, especially those based on only 3–5 reports, were not emphasized as definitive safety findings in the main interpretation. These signals were considered statistically unstable and potentially sensitive to reporting variation, coding practices, and random fluctuation. Descriptive analyses were first performed to summarize the characteristics of entrectinib-associated adverse event (AE) reports. Subgroup analyses were conducted according to sex and age categories as recorded in the source databases [ 23 , 24 ]. In FAERS, age was categorized as 1 with a lower 95% confidence limit above 1 [ 24 ]. All data processing, statistical analyses, and figure generation were performed using R software (version 4.5.2). 3 Results 3.1 Descriptive analysis A total of 520 eligible patients from FAERS and 254 unique patients from JADER were included in the descriptive analysis, and baseline characteristics are presented in Tables 1 and 2 . In both databases, female patients accounted for a greater proportion than male patients, representing 48.7% and 57.9% of the study population in FAERS and JADER, respectively. Sex data were missing more frequently in FAERS than in JADER (10.4% vs. 4.7%). Download: PNG larger image TIFF original image Table 1. Baseline characteristics of entrectinib-associated adverse event reports in the FAERS database. https://doi.org/10.1371/journal.pone.0358331.t001 Download: PNG larger image TIFF original image Table 2. Baseline characteristics of entrectinib-associated adverse event reports in the JADER database. https://doi.org/10.1371/journal.pone.0358331.t002 Body weight data were incomplete in both databases, particularly in FAERS. Missing body weight data were recorded in 68.3% of FAERS patients and 46.9% of JADER patients. Among patients with available data, the 50–100 kg category represented the largest subgroup in both databases, accounting for 24.8% in FAERS and 37.0% in JADER. The age distribution differed modestly between the two databases. In FAERS, patients aged ≥65 years accounted for 39.2% of the cohort, slightly exceeding the proportion of those aged <65 years (37.1%), whereas 23.7% of patients had missing age data. In JADER, patients aged <70 years constituted the majority (57.5%), whereas those aged ≥70 years accounted for 41.3%; only 1.2% of patients had missing age data. Outcome profiles also differed between the two databases. In FAERS, the most frequently reported outcome was “other” (64.8%), followed by hospitalization (16.5%) and death (14.8%). Life-threatening events and disability were infrequently reported, accounting for 3.1% and 0.8%, respectively. In JADER, recovered and partially recovered outcomes were the most common, accounting for 37.4% and 29.8%, respectively. Not recovered outcomes accounted for 12.7%, death for 6.4%, and sequelae for 0.3%. Physicians were the predominant reporters in both databases, contributing 71.5% of reports in FAERS and 79.9% in JADER. Pharmacists accounted for 8.1% of reports in FAERS and 18.5% in JADER, whereas consumers and other healthcare professionals accounted for smaller proportions overall. Overall, physician-reported cases constituted the primary source of entrectinib-related adverse event reporting in both databases. The annual reporting trends are shown in Figs 2A and 2B . In FAERS, the number of eligible reports increased from 19 in 2020–58 in 2021 and 129 in 2022, remained elevated in 2023 (101), peaked in 2024 (161), and declined in 2025 (52). In JADER, the corresponding numbers were 29 in 2020, 72 in 2021, 84 in 2022, 23 in 2023, 34 in 2024, and 12 in 2025Q3. Taken together, the yearly distributions describe reporting activity over time in the two databases, with an early postmarketing increase followed by subsequent fluctuations. These trends should be interpreted as reporting patterns rather than changes in the true incidence of adverse events. Download: PNG larger image TIFF origi
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