Safety Profile and Hepatotoxicity of Anaplastic Lymphoma Kinase Tyrosine Kinase Inhibitors: A Disproportionality Analysis Based on FDA Adverse Event Reporting System Database
1
Key Laboratory of Environmental Medicine Engineering, Ministry of Education of China, School of Public Health, Southeast University, Nanjing 210009, China
2
Jiangsu Institute for Sports and Health (JISH), Nanjing 211100, China
*
Author to whom correspondence should be addressed.
Toxics 2025, 13(3), 210; https://doi.org/10.3390/toxics13030210
Submission received: 17 February 2025
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Revised: 10 March 2025
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Accepted: 13 March 2025
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Published: 14 March 2025
Abstract
:Anaplastic lymphoma kinase tyrosine kinase inhibitors (ALK-TKIs) have become first-line therapies for advanced non-small cell lung cancer (NSCLC) with ALK rearrangements. This study investigates ALK-TKI-associated adverse events (AEs), focusing on identifying hepatotoxicity signals and previously undocumented safety concerns. Using disproportionality analysis of 56,864 reports from the FDA Adverse Event Reporting System (FAERS) database, we systematically classified AEs via the Medical Dictionary for Regulatory Activities (MedDRA). At the System Organ Class (SOC) level, crizotinib exhibited a significantly stronger signal for eye disorders, ceritinib was uniquely linked to gastrointestinal disorders, and loratinib was predominantly associated with metabolism and nutrition disorders. Several AEs previously undocumented in drug labels were identified, including pericardial effusion, elevated C-reactive protein, hemolytic anemia, hemoptysis, and decreased hemoglobin. Furthermore, crizotinib, ceritinib, and alectinib were significantly associated with hepatotoxicity, marked by elevated alanine aminotransferase, aspartate aminotransferase, and hepatic enzyme levels. These findings highlight the need for vigilant monitoring of unlabeled AEs and potential label updates, particularly for hepatotoxicity risks associated with crizotinib, ceritinib, and alectinib.
1. Introduction
Lung cancer remains a global health challenge, ranking among the most frequently diagnosed malignancies and representing the leading cause of cancer-related mortality worldwide. According to epidemiological data, approximately 2 million new cases and 1.76 million deaths are reported annually [1], with non-small cell lung cancer (NSCLC) being the predominant histological subtype accounting for 85% of all diagnoses [2]. While platinum-based chemotherapy has been the mainstay treatment [3], the development of molecularly targeted therapies, particularly tyrosine kinase inhibitors (TKIs), has revolutionized treatment paradigms and significantly improved patient outcomes following the discovery of actionable driver mutations [4].
Anaplastic lymphoma kinase (ALK) rearrangements were initially identified in NSCLC patients in 2007, paving the way for ALK-TKI therapies [5]. Approximately 3–7% (more than 60,000 cases annually) of NSCLC patients involve ALK gene rearrangements, more frequently found in adenocarcinoma cases and non-smokers or light smokers [5,6,7]. Since crizotinib’s approval in 2011, successive generations of ALK-TKIs have been developed and were found to transform clinical outcomes: second-generation ceritinib (2014), alectinib (2015), brigatinib (2017), and third-generation lorlatinib (2018) [8]. The National Comprehensive Cancer Network (NCCN) now recommends ALK-TKIs as a first-line therapy for ALK-positive advanced NSCLC [9]. Nevertheless, their clinical utility is tempered by safety concerns, with prevalent adverse events (AEs) spanning gastrointestinal disturbances, hepatotoxicity, visual abnormalities, and pulmonary complications [10]. In the context of the limited accessibility to comprehensive safety data, it is of the utmost importance to conduct a thorough analysis of the overall safety profile associated with ALK-TKIs.
Notably, drug-induced liver injury (DILI), characterized by asymptomatic transaminase elevation or overt hepatic dysfunction, constitutes a critical safety issue, accounting for >50% of acute liver failure cases in Western populations [11]. While hepatotoxicity has been reported in clinical trials [12,13,14,15,16], a comprehensive real-world comparison of ALK-TKI-associated hepatic safety profiles remains conspicuously absent. To our understanding, no prior real-world study has exhaustively delineated and compared the AEs linked to hepatotoxicity induced by ALK-TKIs.
The FDA Adverse Event Reporting System (FAERS), a post-marketing surveillance database aggregating spontaneous AE reports, provides an invaluable resource for pharmacovigilance research. To address existing knowledge gaps, this study conducts a disproportionality analysis of FAERS data to (1) systematically characterize hepatotoxicity signals associated with ALK-TKIs, and (2) identify previously unlabeled AEs requiring clinical attention. Our findings aim to optimize risk-benefit assessments and improve evidence-based monitoring strategies for ALK-TKI therapy.
2. Materials and Methods
2.1. Data Source
The FAERS database, a publicly accessible repository of post-marketing safety reports, was selected for this retrospective pharmacovigilance study owing to its comprehensive real-world data on adverse drug events. Data spanning from the first quarter of 2011 to the fourth quarter of 2023 were retrieved in the American Standard Code for Information Interchange (ASCII) format. The drug entities were identified using both generic and brand names, with the inclusion criteria restricted to agents approved by the FDA within the study timeframe to ensure temporal relevance.
2.2. Data Processing
The datasets employed in this study encompassed Drug Information (DRUG), Demographic Information (DEMO), Patient Outcomes (OUTC), Indications (INDI), Adverse Reactions (REAC), and Therapy Start and End Dates (THER). The datasets were merged using the PRIMARYID key (Figure 1) and deduplicated following FDA recommendations. (1) We selected the latest FDA_DT as the time identifier when the PRIMARYID was the same. (2) To eliminate duplicate reports submitted by various individuals and institutions, we prioritized the higher PRIMARYID when both FDA_DT and CASEID were the same. The AEs were systematically coded using Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms (PTs) and mapped to the corresponding System Organ Classes (SOCs) [17]. The clinical characteristics of patients experiencing AEs, including age, gender, reporter type, geographic origin, and patient outcomes were extracted to provide a more comprehensive understanding of the safety profile of the five drugs.
For hepatotoxicity analysis, prior to initiating the drug search, the REAC dataset was standardized by consolidating all liver injury-related preferred terms to the unified designation “DILI.” Table S1 presents the lexicon of liver injury preferred terms derived from the Medical Dictionary for Regulatory Activities (MedDRA), adapted from previous studies investigating antifungal agents or elexacaftor/tezacaftor/ivacaftor and DILI [18,19].
2.3. Statistical Analysis
In pharmacovigilance research, disproportionality analysis serves as a key tool to assess potential associations between specific AEs and particular drugs [20]. A 2 × 2 contingency table (Table S2) compared the observed AE frequencies in ALK-TKIs recipients against the expected background rates in the full FAERS dataset. The disproportionality analysis method for AE signal detection includes the Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) method. The thresholds for signal detection followed the established criteria (Table S3). The ROR and PRR methodologies are highly efficient in identifying the relative risks pertaining to specific drug-event pairs. However, the ROR and PRR methods show high sensitivity but low specificity and are prone to false positives. The MGPS method can pull the disproportionality scores toward the null, thereby reducing the impact of spuriously high ROR values and thus detecting AE signals more reliably. The BCPNN method is a measure of the strength of the drug–AEs association. This integrative approach balances sensitivity and specificity through cross-validation. All analyses were performed in the R (v4.3.2) software.
3. Results
3.1. Basic Information
The data of crizotinib, ceritinib, alectinib, brigatinib, and loratinib reported in the FAERS database were extracted, spanning the period from the first quarter of 2011 to the fourth quarter of 2023. After excluding duplicates, the number of PS reports for each ALK-TKIs was as follows: crizotinib 19,857, ceritinib 8946, alectinib 12,141, brigatinib 7468, and lorlatinib 8452.
Among patients who reportedly experienced AEs, the majority were female, with most cases involving individuals aged 45–65 years old. The United States was identified as the primary reporting country. In terms of reporters, physicians were reported to be the main source, especially for ceritinib (40.73%) and brigatinib (40.72%), while consumer reports were the highest in alectinib (46.59%). In terms of outcome of AEs, crizotinib (26.35%), ceritinib (28.57%), and lorlatinib (25.04%) were predominantly associated with death as the reported outcome. In contrast, alectinib (22.21%) and brigatinib (26.83%) were mainly linked to hospitalization—initial or prolonged. Notably, most AE reports were submitted within the first 2 months of medication use. For more details, see Table 1 and Figure S1.
3.2. Distribution of AE Signals in SOCs
The AEs associated with the five ALK-TKIs were categorized into 21 SOCs according to MedDRA. Disproportionality analysis was performed using the ROR and PRR to identify significant AE signals across multiple SOCs. The analysis revealed distinctive SOCs-level safety profiles for each ALK-TKI, with statistically significant signals summarized below and detailed in Table 2. Crizotinib exhibited prominent signals in neoplasms benign, malignant and unspecified (incl cysts and polyps) (ROR = 3.46, 95% CI = 3.29–3.63, PRR = 3.24, χ2 = 2766.34) and eye disorders (ROR = 2.38, 95% CI = 2.23–2.55, PRR = 2.32, χ2 = 676.52). Ceritinib demonstrated notable signals for gastrointestinal disorders (ROR = 2.33, 95% CI = 2.20–2.46, PRR = 2.09, χ2 = 940.32), neoplasms benign, malignant, and unspecified (incl cysts and polyps) (ROR = 7.22, 95% CI = 6.82–7.64, PRR = 6.14, χ2 = 6534.7), metabolism and nutrition disorders (ROR = 2.1, 95% CI = 1.89–2.33, PRR = 2.05, χ2 = 200.02), hepatobiliary disorders (ROR = 3.68, 95% CI = 3.25–4.18, PRR = 3.61, χ2 = 472.62). Alectinib showed a pronounced signal for hepatobiliary disorders (ROR = 3.30, 95% CI = 2.96–3.69, PRR = 3.24, χ2 = 498.68). Brigatinib was predominantly associated with neoplasms benign, malignant, and unspecified (incl cysts and polyps) (ROR = 5.40, 95% CI = 5.07–5.76, PRR = 4.75, χ2 = 3374.71). Lorlatinib demonstrated a dominant signal for metabolism and nutrition disorders (ROR = 2.58, 95% CI = 2.34–2.85, PRR = 2.51, χ2 = 378.10).
3.3. Analysis of AE Signals at the PT Level
The study analyzed AE signals at the PT level using four disproportionality algorithms. After excluding death, malignancy-related AEs, and COVID-19, a total of 454 statistically significant AE signals were identified across the five ALK-TKIs: crizotinib (104 signals), ceritinib (89 signals), alectinib (94 signals), brigatinib (52 signals), and lorlatinib (115 signals). The top 30 AE signals for each ALK-TKI were summarized and compared with those listed in their respective drug labels (Table 3, Table 4, Table 5, Table 6 and Table 7). Key findings are listed below.
Crizotinib: The most frequently reported AEs were vomiting, visual impairment, and constipation. Notably, notable signals were observed for pleural effusion, pericardial effusion and hypoxia, which are not currently mentioned on the drug label. Ceritinib: Diarrhea, nausea, and vomiting were the most commonly reported AEs. Strong but unlisted signals were observed for pleural effusion, elevated C-reactive protein, hypokalemia, and central nervous system lesions. Alectinib: The most frequent AEs were constipation, myalgia, and oedema. Significant signals included pleural effusion, pulmonary oedema, hemolysis, hemolytic anemia, pericardial effusion, fluid retention, and diverticulitis, which are not yet documented in the drug label. Brigatinib: The most prominent AE was blood creatine phosphokinase increased, followed by metastases to the central nervous system. Notable AE signals not mentioned in the label included pleural effusion, hemoptysis, central nervous system lesion, cerebral hemorrhage, pneumonia bacterial, and cerebral edema. Lorlatinib: The most frequently reported AE was blood cholesterol increase, followed by hallucination and weight increase. Significant signals not yet listed in the label included pleural effusion, hemoglobin decreased, low-density lipoprotein increased, pericardial effusion, electrocardiogram QT prolonged, fluid retention, and brain oedema. These findings underscore the importance of tailored monitoring based on the unique AE profiles of each ALK-TKI.
3.4. Hepatotoxicity Analysis of ALK-TKIs
A Venn diagram of AE signals was plotted for the five drugs, and all drugs were associated with DILI AEs (Figure S2 and Table S4). A total of 1978 reports of DILI were identified in FAERS, with 696 reports of crizotinib, 451 reports of ceritinib, 528 reports of alectinib, 178 reports of brigatinib, and 125 reports of lorlatinib. Demographic and clinical characteristics of DILI cases associated with ALK-TKIs were analyzed based on the FAERS database. Brigatinib users were generally older (60.74 ± 12.95 years), whereas ceritinib (53.78 ± 17.83) and lorlatinib (53.01 ± 16.30) were reported in younger populations. Females predominated in all groups except for lorlatinib. Geographic distribution indicated that the United States was the primary reporting country for crizotinib (40.23%) and alectinib (30.68%) DILI cases. In terms of reporters, physicians were reported to be the main source, especially for crizotinib (59.63%) and alectinib (57.77%). DILI-related mortality was elevated with crizotinib (16.95%) and lorlatinib (16.00%) but lower for alectinib (4.73%). Most cases lacked medication timing data, though brigatinib had more early-onset reports (≤28 days: 14.05%). These findings highlight the need for tailored monitoring based on drug-specific risk profiles (Table S5).
After assessing the association between each ALK inhibitor and DILI by using the four algorithms, we found that crizotinib, ceritinib, and alectinib were significantly associated with increased DILI reports. Signal analysis was performed for ALK-TKIs-associated DILI. The results showed that the top three reported AEs were alanine aminotransferase (ALT) increased (ROR = 5.65, 95% CI = 4.95–6.47, PRR = 5.63, χ2 = 819.84, IC = 2.49, IC025 = 0.82, EBGM = 5.61, EBGM05 = 5.02), aspartate aminotransferase (AST) increased (ROR = 6.52, 95% CI = 5.68–7.48, PRR = 6.49, χ2 = 953.29, IC = 2.69, IC025 = 1.03, EBGM = 6.47, EBGM05 = 5.76), and hepatic enzyme increased (ROR = 3.72, 95% CI = 3.2–4.32, PRR = 3.71, χ2 = 340.12, IC = 1.89, IC025 = 0.22, EBGM = 3.7, EBGM05 = 3.27) (Table 8 and Table S6).
4. Discussion
This large-scale pharmacovigilance study systematically characterized the safety profiles of ALK-TKIs using the FAERS database from 2011 to 2023. Through analyzing 56,864 AE reports and conducting disproportionality analysis, we identified both established and previously unreported safety signals across the five drugs and highlighted significant hepatotoxicity risks associated with crizotinib, ceritinib, and alectinib.
Distinct demographic patterns were observed among patients experiencing AEs. Our demographic analysis revealed that the majority of AE reports were from females, which aligns with the findings of an evaluation of gender differences in adverse drug reactions across various therapeutic interventions, potentially reflecting the influence of sex hormones on transporter proteins, receptors, and enzymes involved in drug metabolism [21,22]. Age distribution further highlighted risks in older populations, concordant with the NSCLC epidemiology and age-related declines in hepatic/renal function affecting drug clearance [23,24]. The geographical analysis demonstrated disparities, with the majority of AE reports originating from the United States and Japan contributing to 58.82% of the total reports. The results were likely influenced by regional variations in drug approval timelines and prescription patterns. Identifying these differences in AEs could reduce the experience of AEs for patients and could be conducive to the development of personalized medicine.
Importantly, our findings on eye disorders, gastrointestinal disorders, hepatobiliary disorders, and metabolism and nutrition disorders are consistent with prior clinical trials and meta-analysis, thus reinforcing the validity of our methodology [12,13,14,15,25]. For instance, crizotinib was identified as the sole agent with a significant signal for ocular toxicity, which aligns with clinical trial data indicating that approximately 62% of patients experienced visual disturbances, such as photopsia, diplopia, and photophobia [21]. Preclinical studies have suggested that crizotinib-induced ocular toxicity may result from the modulation of phototransduction pathways in retinal ganglion cells, as demonstrated in murine models that exhibited altered light sensitivity thresholds [25]. Similarly, the high reporting rate of ceritinib-associated hepatotoxicity observed in our study corresponds with pooled analyses reporting elevated ALT levels in more than 50% of patients [26]. Notably, ceritinib and alectinib both showed elevated hepatobiliary disorder signals, likely attributable to ceritinib’s dual inhibition of insulin and IGF-1 receptors, which can disrupt hepatic metabolic homeostasis [27]. Conversely, lorlatinib demonstrated a more favorable hepatotoxicity profile but showed strong associations with metabolic disturbances, particularly hypercholesterolemia which was a recognized class effect. Emerging evidence has linked these lipid abnormalities to accelerated atherosclerosis, necessitating proactive and vigilant monitoring of lipids profiles in high-risk populations [28]. These findings confirm that disproportionality analysis effectively identifies clinically meaningful signals, and our results align with real-world clinical observations.
In addition to confirming known AEs, our analysis identified several previously unreported AEs that are not currently included in drug labels. Notably, pleural and pericardial effusions emerged as class-wide signals across all ALK-TKIs. While these effusions may reflect progression in NSCLC patients, they could also result from drug-specific mechanisms such as crizotinib-induced endothelial leakage [29]. Among ceritinib-treated patients, hypokalemia has been observed, likely attributable to pharmacodynamic interactions with diuretics commonly prescribed for comorbid conditions [30]. Additionally, our study confirmed a previously underreported association between alectinib and pulmonary edema, corroborating data from the Japanese Adverse Drug Event Report (JADER) database, which documented pulmonary edema in 6 out of 157 cases of alectinib-associated lung toxicity [29]. Furthermore, our study corroborates previous case reports documenting hemolysis and hemolytic anemia as rare but significant AEs associated with alectinib [31]. While no prior studies have reported certain AEs identified in our analysis, where their clinical significance is supported by both high reporting frequencies and robust Bayesian statistical signals, suggesting a potential class effect of ALK-TKIs.
Additionally, this analysis demonstrated consistent hepatotoxicity signals across all five ALK-TKIs, aligning with a meta-analysis reporting elevated hepatotoxicity risks in NSCLC patients treated with ALK inhibitors (pooled incidence: 26% for ALT elevation and 23.2% for AST elevation) [32]. However, the DILI reporting rates in FAERS differed from those observed in registration trials. For instance, ceritinib-associated ALT elevation was reported in 42% of patients (Grade 1–2: 22%) in the ASCEND-5 trial [15], a proportion higher than the FAERS-derived reporting rate. This divergence is likely attributable to the underreporting of asymptomatic laboratory abnormalities in real-world pharmacovigilance systems, where mild or transient elevations may not trigger clinical action or documentation in real-world settings. These findings underscore the complementary roles of clinical trials (systematically capturing graded laboratory abnormalities) and pharmacovigilance databases (detecting severe or unmonitored AEs) in comprehensive risk characterization [12,15,32].
The demographic patterns in DILI reports mirrored NSCLC epidemiology and pharmacovigilance trends. The older age of brigatinib users aligns with age-related declines in hepatic metabolic capacity [24], whereas younger cohorts receiving ceritinib and lorlatinib may reflect preferential prescribing patterns for these agents in non-smokers with ALK rearrangements [6]. Female predominance in DILI reports parallels gender disparities in NSCLC incidence and AE reporting behaviors observed in pharmacovigilance studies [22]. Geographic clustering, such as U.S.-dominant crizotinib reports, correlates with regional variations in drug approval timelines and clinical adoption rates [10].
Disproportionality analysis confirmed robust hepatotoxicity signals for crizotinib, ceritinib, and alectinib, corroborating previous clinical trial evidence while advancing real-world evidence through quantitative risk ranking [13,14,15,33]. Ceritinib exhibited particularly elevated hepatotoxicity risk, potentially attributable to its broad-spectrum kinase inhibition profile that may impair hepatic regenerative mechanisms. Conversely, brigatinib and lorlatinib demonstrated more favorable hepatic safety profiles, suggesting their potential suitability for patients with pre-existing liver dysfunction. Nevertheless, the precise mechanisms underlying ALK-TKIs-associated hepatotoxicity remain incompletely understood, highlighting the need for further research to elucidate these pathways.
The clinical implications of our findings are significant, as they provide actionable insights for guiding the selection and monitoring of ALK-TKIs. The identification of previously unreported AEs underscores the need for heightened vigilance among healthcare providers, particularly when prescribing these agents to manage patients with comorbid conditions or predisposing risk factors. Prior to initiating ALK-TKI therapy, clinicians should conduct comprehensive baseline assessments to tailor ALK-TKI selection based on individual patient comorbidities. For instance, lorlatinib should be avoided in patients with pre-existing hyperlipidemia due to its propensity to exacerbate lipid abnormalities. Proactive surveillance and early intervention are vital during therapy, so as to mitigate AE progression and prevent severe complications. In addition, this study systematically compiled the currently labeled warnings, recommended dose adjustments for hepatic impairment, and liver monitoring guidelines for each ALK-TKI (Table S7). By comparing labeled warnings, dose adjustment protocols, and monitoring requirements across ALK-TKIs, we delineated distinct hepatotoxicity risk profiles and management strategies among these agents. These findings, integrated with real-world evidence from the FAERS database, provide actionable insights to guide personalized treatment selection based on individual patient profiles, particularly hepatic function status. By integrating personalized treatment strategies with rigorous AE monitoring, the risk-benefit profile of ALK-TKIs can be optimized for individual patients [26].
The limitations of the study should be considered when interpreting the results. Firstly, the inherent constraints of the FAERS database, as a spontaneous reporting system, introduce potential biases such as underreporting, selective reporting, and confounding due to underlying disease progression. Secondly, disproportionality metrics indicate association rather than causation. While our study established a statistically significant association between ALK-TKIs and DILI within the FAERS database, these findings do not imply causation. To validate these findings clinically, further research is warranted across three key domains: (1) Prospective cohort studies to assess causality and quantify incidence rates in real-world populations through longitudinal monitoring of ALK-TKI recipients; (2) pharmacogenomic analyses leveraging next-generation sequencing to identify genetic predispositions (e.g., HLA haplotypes, CYP450 polymorphisms) associated with specific AEs; (3) mechanistic investigations integrating in vitro models (e.g., patient-derived organoids), multi-omics profiling, and biomarker discovery to elucidate the pathophysiological pathways underlying pericardial effusion or hemolytic anemia. Findings from such investigations will help refine risk stratification and aid the development of targeted preventive measures. Additionally, the Weber effect may lead to inflated AE reporting rates for newer drugs. To contextualize the potential impact of the Weber effect, this study compiled the first approval dates of each ALK-TKI in the United States, European Union, Japan, and China, as well as in other countries (Table S8). While this study did not apply statistical adjustments (e.g., time-weighted models) to mitigate the Weber effect—potentially resulting in overestimated safety signals for newer drugs—the inclusion of approval timelines provides critical context for interpreting these findings. Specifically, readers are cautioned against overinterpreting safety signals for recently approved agents (e.g., lorlatinib, approved in 2018). Future studies should incorporate time-stratified analyses or advanced statistical approaches (e.g., negative binomial regression with temporal weighting) to account for reporting biases associated with the Weber effect.
5. Conclusions
In conclusion, this large-scale pharmacovigilance database analysis comprehensively analyzed AE profiles of five ALK-TKIs used in NSCLC, revealing both established and previously unrecognized safety signals. In clinical settings, these signals need to be monitored and product labels may be updated to inform patients and clinicians of the AEs. Meanwhile, as noted in the reports, the five ALK-TKIs examined in this study were strongly associated with DILI. The study established a hepatotoxicity gradient among ALK-TKIs, thereby guiding agent selection for patients with hepatic comorbidities. Further studies are required to comprehend the mechanisms underlying these unlabeled AEs and to establish a causal relationship between the drugs and DILI, thus supporting the development of standardized monitoring guidelines.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/toxics13030210/s1, Figure S1: Distribution of age(a), weight(b), sex(c), country of reporter(d), the type of reporter(e), and outcome(f) in reports for five drugs; Figure S2: Wayne plots of adverse event signals for the five drugs; Table S1: Dictionary of preferred terms for liver injury; Table S2: Contingency table; Table S3: ROR, PRR, BCPNN, and EBGM methods, formulas, and thresholds; Table S4: Common adverse events signals for five drugs; Table S5: Basic information for DILI of crizotinib, ceritinib, alectinib, brigatinib, lorlatinib; Table S6: Signal analysis of DILI in ALK-TKIs; Table S7: Label warnings and hepatic safety guidelines for ALK-TKIs; Table S8: Global approval timeline of ALK-TKIs.
Author Contributions
Conceptualization, Y.P. and J.Z.; methodology, Y.Y.; software, Y.Y.; validation, Y.Y. and S.T.; formal analysis, Y.Y.; investigation, Y.Y.; resources, Y.Y.; data curation, Y.Y.; writing—original draft preparation, Y.Y; writing—review and editing, S.T. and J.Z.; visualization, Y.Y.; supervision, Y.P. and J.Z.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Natural Science Foundation of Jiangsu Province, Major Project, grant number BK20222008.
Institutional Review Board Statement
Ethical approval was not required for this study as all data reported to the FDA were anonymous.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data supporting reported results can be made available upon request.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
The flow diagram showing the analysis process of the study. “×” indicates the removal of duplicate reports.
Figure 1.
The flow diagram showing the analysis process of the study. “×” indicates the removal of duplicate reports.
Table 1.
Basic information for crizotinib, ceritinib, alectinib, brigatinib, and lorlatinib.
| Characteristics | Crizotinib | Ceritinib | Alectinib | Brigatinib | Lorlatinib |
|---|---|---|---|---|---|
| Total number | 19,857 | 8946 | 12,141 | 7468 | 8452 |
| Age (years), mean ± SD | 60.13 ± 16.56 | 55.84 ± 14.96 | 60.23 ± 14.72 | 61.03 ± 14.93 | 55.84 ± 17.96 |
| Sex | |||||
| Female | 10,748 (54.13) | 4639 (51.86) | 6977 (57.47) | 4221 (56.52) | 4370 (51.70) |
| Male | 7770 (39.13) | 3646 (40.76) | 4235 (34.88) | 2750 (36.82) | 3306 (39.12) |
| Unknown | 1339 (6.74) | 661 (7.39) | 929 (7.65) | 497 (6.66) | 776 (9.18) |
| Reporter country | |||||
| United states | 11,814 (59.50) | 1832 (20.48) | 6749 (55.59) | 3984 (53.35) | 2695 (31.89) |
| Japan | 1670 (8.41) | 1357 (15.17) | 974 (8.02) | 1294 (17.33) | 1079 (12.77) |
| China | 527 (2.65) | 638 (7.13) | 572 (4.71) | 48 (0.64) | 269 (3.18) |
| Other | 5846 (29.44) | 5119 (57.22) | 3846 (31.68) | 2142 (28.68) | 4409 (52.17) |
| Reporter | |||||
| Physician | 7450 (37.52) | 3644 (40.73) | 4193 (34.54) | 3041 (40.72) | 3253 (38.49) |
| Consumer | 5587 (28.14) | 3037 (33.95) | 5657 (46.59) | 2629 (35.20) | 3377 (39.96) |
| Pharmacist | 2332 (11.74) | 350 (3.91) | 951 (7.83) | 521 (6.89) | 563 (6.66) |
| Other health professionals | 4394 (22.13) | 1491 (16.67) | 1238 (10.20) | 1272 (17.03) | 1136 (13.44) |
| Unknown | 94 (0.47) | 424 (4.74) | 102 (0.84) | 5 (0.07) | 123 (1.46) |
| Outcome | |||||
| Death | 5232 (26.35) | 2556 (28.57) | 1336 (11.00) | 1379 (18.47) | 2116 (25.04) |
| Hospitalization—initial or prolonged | 4200 (21.15) | 2152 (24.06) | 2697 (22.21) | 2004 (26.83) | 1784 (21.11) |
| Life threatening | 348 (1.75) | 242 (2.71) | 236 (1.94) | 72 (0.96) | 142 (1.68) |
| Disability | 86 (0.43) | 87 (0.97) | 128 (1.05) | 34 (0.46) | 93 (1.10) |
| Other serious outcomes | 3896 (19.62) | 3067 (34.28) | 4488 (36.97) | 3183 (42.62) | 3120 (36.91) |
| Unknown | 6095 (30.69) | 842 (9.41) | 3256 (26.82) | 796 (10.66) | 1197 (14.16) |
| Adverse event occurrence time-medication date (days) | |||||
| 0–7 | 1570 (7.91) | 1294 (14.46) | 111 (0.91) | 531 (7.11) | 222 (2.63) |
| 7–28 | 1600 (8.06) | 1179 (13.18) | 351 (2.89) | 330 (4.42) | 408 (4.83) |
| 28–60 | 857 (4.32) | 642 (7.18) | 249 (2.05) | 153 (2.05) | 352 (4.16) |
| ≥60 | 2072 (10.43) | 1707 (19.08) | 452 (3.72) | 470 (6.29) | 738 (8.73) |
| Unknown | 13,758 (69.29) | 4124 (46.10) | 9612 (79.17) | 5984 (80.13) | 6448 (76.29) |
SD standard deviation.
Table 2.
Signal strength at the SOC level of AEs for ALK-TKIs.
| Drug | SOC | N | ROR (95% CI) | PRR (χ2) | IC (IC025) | EBGM (EBGMO5) |
|---|---|---|---|---|---|---|
| crizotinib | neoplasms benign, malignant, and unspecified (incl cysts and polyps) | 1738 | 3.46 (3.29–3.63) ★ | 3.24 (2766.34) ★ | 1.70 (0.03) ★ | 3.24 (3.11) ★ |
| eye disorders | 883 | 2.38 (2.23–2.55) ★ | 2.32 (676.52) ★ | 1.21 (−0.45) | 2.32 (2.19) ★ | |
| ceritinib | gastrointestinal disorders | 1557 | 2.33 (2.2–2.46) ★ | 2.09 (940.32) ★ | 1.06 (−0.60) | 2.09 (2.00) ★ |
| neoplasms benign, malignant, and unspecified (incl cysts and polyps) | 1543 | 7.22 (6.82–7.64) ★ | 6.14 (6534.7) ★ | 2.62 (0.95) ★ | 6.14 (5.86) ★ | |
| metabolism and nutrition disorders | 369 | 2.1 (1.89–2.33) ★ | 2.05 (200.02) ★ | 1.04 (−0.63) | 2.05 (1.88) | |
| hepatobiliary disorders | 252 | 3.68 (3.25–4.18) ★ | 3.61 (472.62) ★ | 1.85 (0.18) ★ | 3.60 (3.24) ★ | |
| alectinib | hepatobiliary disorders | 319 | 3.30 (2.96–3.69) ★ | 3.24 (498.68) ★ | 1.70 (0.03) ★ | 3.24 (2.95) ★ |
| brigatinib | neoplasms benign, malignant, and unspecified (incl cysts and polyps) | 1106 | 5.40 (5.07–5.76) ★ | 4.75 (3374.71) ★ | 2.25 (0.58) ★ | 4.74 (4.5) ★ |
| lorlatinib | metabolism and nutrition disorders | 410 | 2.58 (2.34–2.85) ★ | 2.51 (378.10) ★ | 1.32 (−0.34) | 2.50 (2.31) ★ |
★ indicates statistical significance. SOC, System Organ Class in MedDRA; ROR, Reporting Odds Ratio; PRR, Proportional Reporting Ratio; IC, Information Component; EBGM, Empirical Bayesian Geometric Mean.
Table 3.
The top 30 signals in the AE reports of crizotinib.
| PT | N | ROR (95% CI) | PRR (χ2) | IC (IC025) | EBGM (EBGMO5) |
|---|---|---|---|---|---|
| vomiting | 448 | 3.25 (2.96–3.57) | 3.20 (683.06) | 1.68 (0.01) | 3.20 (2.96) |
| visual impairment | 292 | 7.30 (6.51–8.20) | 7.21 (1561.41) | 2.85 (1.18) | 7.20 (6.53) |
| constipation | 283 | 4.11 (3.66–4.63) | 4.07 (656.68) | 2.02 (0.36) | 4.07 (3.68) |
| oedema peripheral | 262 | 7.47 (6.61–8.44) | 7.38 (1444.58) | 2.88 (1.21) | 7.37 (6.65) |
| pleural effusion a | 157 | 8.23 (7.03–9.63) | 8.17 (986.21) | 3.03 (1.36) | 8.15 (7.15) |
| dysgeusia | 153 | 7.04 (6.00–8.25) | 6.99 (784.19) | 2.80 (1.14) | 6.97 (6.10) |
| dysphagia | 125 | 4.38 (3.67–5.22) | 4.36 (323.69) | 2.12 (0.46) | 4.36 (3.76) |
| interstitial lung disease | 118 | 7.75 (6.46–9.29) | 7.71 (687.39) | 2.94 (1.28) | 7.69 (6.61) |
| oedema | 116 | 6.84 (5.70–8.22) | 6.81 (574.13) | 2.76 (1.10) | 6.80 (5.83) |
| photopsia | 115 | 61.48 (51.08–73.99) | 61.13 (6656.83) | 5.90 (4.24) | 59.84 (51.25) |
| pulmonary embolism | 109 | 3.66 (3.03–4.42) | 3.64 (209.18) | 1.86 (0.20) | 3.64 (3.11) |
| renal impairment | 100 | 3.62 (2.98–4.41) | 3.61 (188.57) | 1.85 (0.18) | 3.61 (3.06) |
| pneumonitis | 94 | 11.35 (9.26–13.9) | 11.30 (879.35) | 3.49 (1.83) | 11.26 (9.50) |
| hepatic enzyme increase | 88 | 4.21 (3.42–5.20) | 4.20 (214.47) | 2.07 (0.40) | 4.20 (3.52) |
| alanine aminotransferase increase | 84 | 4.85 (3.92–6.02) | 4.84 (255.5) | 2.27 (0.61) | 4.83 (4.04) |
| blood creatinine increased | 75 | 3.69 (2.94–4.64) | 3.68 (146.67) | 1.88 (0.21) | 3.68 (3.04) |
| respiratory failure | 72 | 3.43 (2.72–4.33) | 3.43 (123.65) | 1.78 (0.11) | 3.42 (2.82) |
| aspartate aminotransferase increase | 71 | 4.95 (3.92–6.26) | 4.94 (222.94) | 2.30 (0.64) | 4.93 (4.06) |
| bradycardia | 70 | 4.52 (3.57–5.72) | 4.51 (190.99) | 2.17 (0.50) | 4.50 (3.70) |
| vitreous floaters | 63 | 20.72 (16.16–26.55) | 20.65 (1169.71) | 4.36 (2.69) | 20.51 (16.66) |
| oesophagitis | 60 | 19.01 (14.74–24.51) | 18.95 (1013.72) | 4.24 (2.57) | 18.83 (15.22) |
| pulmonary oedema | 59 | 4.34 (3.36–5.61) | 4.33 (151.21) | 2.11 (0.45) | 4.33 (3.50) |
| hepatic function abnormal | 57 | 5.08 (3.92–6.59) | 5.07 (185.97) | 2.34 (0.67) | 5.06 (4.07) |
| liver function test abnormal | 55 | 6.85 (5.26–8.93) | 6.83 (273.36) | 2.77 (1.10) | 6.82 (5.46) |
| pericardial effusion a | 54 | 7.69 (5.88–10.04) | 7.67 (312.4) | 2.94 (1.27) | 7.65 (6.12) |
| electrocardiogram qt prolonged | 52 | 4.77 (3.63–6.26) | 4.76 (154.21) | 2.25 (0.58) | 4.75 (3.78) |
| transaminases increased | 44 | 6.45 (4.80–8.68) | 6.44 (201.88) | 2.68 (1.02) | 6.43 (5.02) |
| hypoxia a | 42 | 4.03 (2.97–5.45) | 4.02 (95.18) | 2.01 (0.34) | 4.02 (3.12) |
| liver function test increased | 39 | 5.45 (3.98–7.46) | 5.44 (141.12) | 2.44 (0.78) | 5.43 (4.18) |
| diplopia | 37 | 4.87 (3.53–6.73) | 4.86 (113.38) | 2.28 (0.61) | 4.86 (3.71) |
a indicates a PT not yet mentioned in the drug instruction. PT, Preferred Term in MedDRA; ROR, Reporting Odds Ratio; PRR, Proportional Reporting Ratio; IC, Information Component; EBGM, Empirical Bayesian Geometric Mean.
Table 4.
The top 30 signals in the AE reports of ceritinib.
| PT | N | ROR (95% CI) | PRR (χ2) | IC (IC025) | EBGM (EBGMO5) |
|---|---|---|---|---|---|
| diarrhea | 422 | 4.54 (4.12–5.01) | 4.37 (1108.54) | 2.13 (0.46) | 4.37 (4.03) |
| nausea | 350 | 3.30 (2.97–3.68) | 3.21 (539.71) | 1.68 (0.02) | 3.21 (2.94) |
| vomiting | 254 | 4.20 (3.71–4.76) | 4.11 (600.80) | 2.04 (0.37) | 4.10 (3.70) |
| metastases to central nervous system | 173 | 107.08 (91.99–124.65) | 105.03 (17,486.21) | 6.69 (5.02) | 103.03 (90.73) |
| decreased appetite | 126 | 3.63 (3.05–4.33) | 3.60 (236.80) | 1.85 (0.18) | 3.59 (3.10) |
| alanine aminotransferase increase | 72 | 9.70 (7.69–12.24) | 9.63 (556.55) | 3.27 (1.60) | 9.62 (7.92) |
| aspartate aminotransferase increase | 64 | 10.51 (8.22–13.45) | 10.44 (545.90) | 3.38 (1.72) | 10.43 (8.49) |
| blood creatinine increase | 64 | 7.25 (5.67–9.28) | 7.21 (342.15) | 2.85 (1.18) | 7.20 (5.86) |
| general physical health deterioration | 60 | 3.90 (3.02–5.02) | 3.88 (128.29) | 1.95 (0.29) | 3.88 (3.13) |
| hepatic function abnormal | 56 | 11.28 (8.67–14.68) | 11.22 (520.37) | 3.48 (1.82) | 11.20 (8.98) |
| metastases to liver | 55 | 21.81 (16.72–28.44) | 21.68 (1080.78) | 4.43 (2.77) | 21.59 (17.29) |
| pleural effusion a | 52 | 6.37 (4.85–8.37) | 6.34 (233.88) | 2.66 (1.00) | 6.33 (5.04) |
| pericardial effusion | 47 | 15.36 (11.53–20.47) | 15.28 (625.89) | 3.93 (2.26) | 15.24 (11.99) |
| pericarditis | 46 | 19.79 (14.81–26.46) | 19.70 (813.58) | 4.29 (2.63) | 19.63 (15.40) |
| renal impairment | 45 | 3.59 (2.67–4.81) | 3.57 (83.42) | 1.84 (0.17) | 3.57 (2.79) |
| metastases to bone | 44 | 19.20 (14.27–25.83) | 19.11 (752.59) | 4.25 (2.58) | 19.04 (14.86) |
| blood alkaline phosphatase increase | 41 | 14.71 (10.82–20.00) | 14.65 (520.11) | 3.87 (2.20) | 14.61 (11.30) |
| hepatic enzyme increase | 40 | 4.17 (3.06–5.69) | 4.15 (95.85) | 2.05 (0.39) | 4.15 (3.20) |
| liver disorder | 39 | 6.54 (4.78–8.96) | 6.52 (182.15) | 2.70 (1.04) | 6.51 (5.01) |
| hyperglycemia | 38 | 8.29 (6.03–11.40) | 8.26 (242.23) | 3.04 (1.38) | 8.25 (6.32) |
| pneumonitis | 31 | 7.88 (5.54–11.22) | 7.86 (185.42) | 2.97 (1.31) | 7.85 (5.84) |
| gamma-glutamyltransferase increase | 30 | 11.19 (7.81–16.02) | 11.15 (276.82) | 3.48 (1.81) | 11.13 (8.25) |
| metastases to meninges | 28 | 95.64 (65.78–139.06) | 95.35 (2568.40) | 6.55 (4.88) | 93.70 (68.51) |
| transaminases increase | 28 | 9.07 (6.26–13.15) | 9.05 (200.14) | 3.18 (1.51) | 9.03 (6.62) |
| c-reactive protein increase a | 26 | 4.35 (2.96–6.40) | 4.34 (66.89) | 2.12 (0.45) | 4.34 (3.14) |
| drug resistance | 26 | 6.75 (4.59–9.93) | 6.74 (126.91) | 2.75 (1.08) | 6.73 (4.88) |
| hypokalemia a | 26 | 4.21 (2.86–6.18) | 4.20 (63.29) | 2.07 (0.40) | 4.19 (3.04) |
| central nervous system lesion a | 24 | 11.03 (7.39–16.47) | 11.00 (217.85) | 3.46 (1.79) | 10.98 (7.85) |
| electrocardiogram qt prolonged | 23 | 4.64 (3.08–6.99) | 4.63 (65.52) | 2.21 (0.54) | 4.63 (3.29) |
| gastrointestinal toxicity | 21 | 34.22 (22.27–52.58) | 34.14 (671.29) | 5.08 (3.42) | 33.93 (23.68) |
a indicates a PT not yet mentioned in the drug instruction. PT, Preferred Term in MedDRA; ROR, Reporting Odds Ratio; PRR, Proportional Reporting Ratio; IC, Information Component; EBGM, Empirical Bayesian Geometric Mean.
Table 5.
The top 30 signals in the AE reports of alectinib.
| PT | N | ROR (95% CI) | PRR (χ2) | IC (IC025) | EBGM (EBGMO5) |
|---|---|---|---|---|---|
| constipation | 285 | 6.71 (5.96–7.55) | 6.57 (1349.54) | 2.71 (1.05) | 6.56 (5.95) |
| myalgia | 159 | 5.24 (4.48–6.13) | 5.19 (538.19) | 2.37 (0.71) | 5.18 (4.55) |
| oedema | 95 | 10.38 (8.48–12.71) | 10.31 (796.76) | 3.36 (1.70) | 10.28 (8.68) |
| oedema peripheral | 93 | 5.47 (4.46–6.71) | 5.44 (336.85) | 2.44 (0.78) | 5.43 (4.58) |
| metastases to central nervous system | 89 | 39.37 (31.92–48.55) | 39.09 (3267.58) | 5.27 (3.61) | 38.67 (32.45) |
| pleural effusion a | 80 | 7.44 (5.97–9.27) | 7.40 (441.96) | 2.88 (1.22) | 7.38 (6.14) |
| pneumonitis | 75 | 13.68 (10.89–17.17) | 13.60 (872.46) | 3.76 (2.09) | 13.55 (11.20) |
| blood bilirubin increase | 73 | 17.10 (13.58–21.54) | 17.01 (1094.85) | 4.08 (2.42) | 16.93 (13.96) |
| bradycardia | 73 | 7.81 (6.21–9.84) | 7.77 (430.16) | 2.96 (1.29) | 7.76 (6.40) |
| blood creatine phosphokinase increase | 69 | 17.42 (13.74–22.08) | 17.33 (1056.70) | 4.11 (2.44) | 17.25 (14.14) |
| blood creatinine increase | 57 | 4.76 (3.67–6.18) | 4.74 (168.25) | 2.24 (0.58) | 4.74 (3.81) |
| pulmonary oedema a | 57 | 7.30 (5.62–9.47) | 7.27 (307.74) | 2.86 (1.19) | 7.26 (5.84) |
| hepatic function abnormal | 53 | 7.73 (5.90–10.13) | 7.70 (308.70) | 2.94 (1.28) | 7.69 (6.13) |
| interstitial lung disease | 51 | 5.44 (4.13–7.16) | 5.42 (183.65) | 2.44 (0.77) | 5.41 (4.30) |
| aspartate aminotransferase increase | 50 | 6.08 (4.60–8.03) | 6.06 (210.88) | 2.60 (0.93) | 6.05 (4.79) |
| hepatic enzyme increase | 50 | 3.78 (2.86–4.99) | 3.77 (101.69) | 1.91 (0.25) | 3.77 (2.98) |
| hemolysis a | 49 | 37.11 (27.99–49.20) | 36.96 (1697.01) | 5.19 (3.53) | 36.59 (28.90) |
| photosensitivity reaction | 49 | 14.85 (11.21–19.68) | 14.80 (627.98) | 3.88 (2.22) | 14.74 (11.65) |
| hemolytic anemia a | 43 | 31.55 (23.35–42.62) | 31.44 (1256.36) | 4.96 (3.30) | 31.17 (24.24) |
| pericardial effusion a | 43 | 10.51 (7.79–14.18) | 10.47 (367.53) | 3.38 (1.72) | 10.45 (8.13) |
| fluid retention a | 41 | 3.94 (2.90–5.35) | 3.93 (89.52) | 1.97 (0.31) | 3.93 (3.04) |
| liver disorder | 41 | 5.05 (3.71–6.86) | 5.04 (132.48) | 2.33 (0.66) | 5.03 (3.89) |
| alanine aminotransferase increase | 39 | 3.86 (2.82–5.29) | 3.85 (82.25) | 1.94 (0.28) | 3.85 (2.96) |
| lung disorder | 37 | 3.78 (2.74–5.22) | 3.77 (75.35) | 1.91 (0.25) | 3.77 (2.88) |
| liver function test increase | 31 | 5.50 (3.86–7.82) | 5.48 (113.54) | 2.45 (0.79) | 5.48 (4.08) |
| drug resistance | 29 | 5.33 (3.70–7.67) | 5.32 (101.58) | 2.41 (0.74) | 5.31 (3.92) |
| hyperbilirubinemia | 28 | 15.56 (10.73–22.57) | 15.53 (379.02) | 3.95 (2.28) | 15.47 (11.33) |
| transaminases increase | 27 | 6.45 (4.42–9.41) | 6.43 (123.76) | 2.68 (1.02) | 6.43 (4.68) |
| metastases to bone | 26 | 8.30 (5.65–12.20) | 8.29 (166.24) | 3.05 (1.38) | 8.27 (5.99) |
| diverticulitis a | 24 | 3.80 (2.54–5.67) | 3.79 (49.35) | 1.92 (0.26) | 3.79 (2.71) |
a indicates a PT not yet mentioned in the drug instruction. PT, Preferred Term in MedDRA; ROR, Reporting Odds Ratio; PRR, Proportional Reporting Ratio; IC, Information Component; EBGM, Empirical Bayesian Geometric Mean.
Table 6.
The top 30 signals in the AE reports of brigatinib.
| PT | N | ROR (95% CI) | PRR (χ2) | IC (IC025) | EBGM (EBGMO5) |
|---|---|---|---|---|---|
| blood creatine phosphokinase increase | 141 | 59.73 (50.51–70.63) | 58.62 (7895.64) | 5.86 (4.19) | 57.95 (50.37) |
| metastases to central nervous system | 141 | 102.48 (86.61–121.26) | 100.56 (13,627.45) | 6.62 (4.96) | 98.60 (85.65) |
| blood pressure increase | 71 | 3.26 (2.58–4.12) | 3.24 (110.05) | 1.69 (0.03) | 3.24 (2.66) |
| pleural effusion a | 46 | 7.10 (5.31–9.49) | 7.06 (239.23) | 2.82 (1.15) | 7.05 (5.53) |
| pneumonitis | 40 | 11.63 (8.52–15.87) | 11.57 (385.56) | 3.53 (1.86) | 11.55 (8.90) |
| amylase increase | 39 | 85.67 (62.37–117.66) | 85.22 (3191.92) | 6.39 (4.72) | 83.81 (64.27) |
| renal impairment | 37 | 3.37 (2.44–4.66) | 3.36 (61.47) | 1.75 (0.08) | 3.36 (2.56) |
| lipase increase | 31 | 42.03 (29.49–59.90) | 41.86 (1226.26) | 5.38 (3.71) | 41.52 (30.87) |
| aspartate aminotransferase increase | 30 | 5.91 (4.12–8.45) | 5.89 (121.60) | 2.56 (0.89) | 5.88 (4.35) |
| lung disorder | 29 | 4.79 (3.33–6.90) | 4.78 (86.63) | 2.26 (0.59) | 4.77 (3.52) |
| metastases to liver | 29 | 13.91 (9.65–20.04) | 13.86 (345.08) | 3.79 (2.12) | 13.82 (10.18) |
| interstitial lung disease | 28 | 4.79 (3.30–6.94) | 4.77 (83.45) | 2.25 (0.59) | 4.77 (3.49) |
| photosensitivity reaction | 28 | 14.01 (9.66–20.31) | 13.96 (336.03) | 3.80 (2.13) | 13.92 (10.20) |
| pulmonary toxicity | 26 | 29.05 (19.74–42.74) | 28.95 (697.69) | 4.85 (3.18) | 28.79 (20.84) |
| alanine aminotransferase increase | 24 | 3.85 (2.58–5.74) | 3.84 (50.37) | 1.94 (0.27) | 3.84 (2.74) |
| hemoptysis a | 20 | 6.24 (4.02–9.68) | 6.22 (87.62) | 2.64 (0.97) | 6.22 (4.31) |
| hepatic function abnormal | 20 | 4.65 (3.00–7.22) | 4.64 (57.14) | 2.21 (0.55) | 4.64 (3.21) |
| pericardial effusion a | 19 | 7.53 (4.80–11.81) | 7.51 (107.08) | 2.91 (1.24) | 7.50 (5.14) |
| metastases to bone | 16 | 8.04 (4.92–13.13) | 8.02 (98.24) | 3.00 (1.34) | 8.01 (5.31) |
| central nervous system lesion a | 14 | 9.24 (5.47–15.61) | 9.22 (102.46) | 3.20 (1.54) | 9.21 (5.93) |
| hepatotoxicity | 14 | 4.91 (2.91–8.30) | 4.91 (43.52) | 2.29 (0.63) | 4.90 (3.16) |
| liver function test increase | 14 | 3.89 (2.30–6.58) | 3.89 (30.01) | 1.96 (0.29) | 3.88 (2.50) |
| hyperglycemia | 13 | 3.51 (2.04–6.05) | 3.51 (23.31) | 1.81 (0.14) | 3.51 (2.22) |
| cerebral hemorrhage a | 12 | 3.28 (1.86–5.77) | 3.27 (18.92) | 1.71 (0.04) | 3.27 (2.04) |
| metastases to lung | 12 | 9.03 (5.12–15.91) | 9.01 (85.36) | 3.17 (1.50) | 9.00 (5.60) |
| taste disorder | 12 | 3.58 (2.03–6.32) | 3.58 (22.31) | 1.84 (0.17) | 3.58 (2.23) |
| pneumonia bacterial a | 11 | 9.01 (4.98–16.28) | 9.00 (78.05) | 3.17 (1.50) | 8.98 (5.47) |
| brain oedema a | 10 | 7.74 (4.16–14.40) | 7.73 (58.55) | 2.95 (1.28) | 7.72 (4.59) |
| metastases to meninges | 10 | 38.58 (20.70–71.91) | 38.53 (362.79) | 5.26 (3.59) | 38.24 (22.72) |
| hypercholesterolemia | 7 | 8.61 (4.10–18.08) | 8.60 (46.96) | 3.10 (1.44) | 8.59 (4.62) |
a indicates a PT not yet mentioned in the drug instruction. PT, Preferred Term in MedDRA; ROR, Reporting Odds Ratio; PRR, Proportional Reporting Ratio; IC, Information Component; EBGM, Empirical Bayesian Geometric Mean.
Table 7.
The top 30 signals in the AE reports of lorlatinib.
| PT | N | ROR (95% CI) | PRR (χ2) | IC (IC025) | EBGM (EBGMO5) |
|---|---|---|---|---|---|
| blood cholesterol increase | 128 | 23.81 (19.99–28.37) | 23.47 (2737.05) | 4.54 (2.88) | 23.32 (20.14) |
| hallucination | 111 | 12.10 (10.03–14.60) | 11.95 (1111.76) | 3.58 (1.91) | 11.92 (10.19) |
| weight increase | 109 | 3.46 (2.86–4.18) | 3.43 (187.72) | 1.78 (0.11) | 3.42 (2.92) |
| oedema peripheral | 103 | 9.21 (7.58–11.19) | 9.11 (743.13) | 3.18 (1.52) | 9.09 (7.73) |
| oedema | 96 | 16.03 (13.11–19.62) | 15.86 (1332.06) | 3.98 (2.32) | 15.80 (13.35) |
| hypercholesterolemia | 92 | 100.00 (81.20–123.15) | 98.92 (8680.41) | 6.59 (4.92) | 96.31 (80.91) |
| cognitive disorder | 90 | 14.19 (11.52–17.47) | 14.05 (1087.52) | 3.81 (2.14) | 14.00 (11.76) |
| pleural effusion a | 70 | 10.02 (7.92–12.68) | 9.94 (562.02) | 3.31 (1.64) | 9.92 (8.14) |
| blood triglycerides increase | 66 | 45.30 (35.05–57.80) | 44.95 (2801.84) | 5.47 (3.81) | 44.41 (36.22) |
| hyperlipidemia | 66 | 75.75 (59.31–96.75) | 75.17 (4731.61) | 6.20 (4.54) | 73.65 (60.01) |
| neuropathy peripheral | 59 | 4.35 (3.37–5.62) | 4.33 (151.10) | 2.11 (0.45) | 4.32 (3.49) |
| hypertriglyceridemia | 55 | 80.17 (61.32–104.81) | 79.65 (4179.53) | 6.28 (4.62) | 77.95 (62.29) |
| pulmonary embolism | 45 | 5.09 (3.79–6.82) | 5.06 (146.69) | 2.34 (0.67) | 5.06 (3.96) |
| speech disorder | 44 | 6.93 (5.15–9.32) | 6.90 (221.56) | 2.78 (1.12) | 6.89 (5.37) |
| hemoglobin decrease a | 43 | 3.20 (2.37–4.32) | 3.19 (64.82) | 1.67 (0.01) | 3.19 (2.48) |
| nervous system disorder | 42 | 17.73 (13.08–24.03) | 17.65 (656.56) | 4.13 (2.47) | 17.57 (13.62) |
| low-density lipoprotein increase a | 39 | 46.76 (34.07–64.18) | 46.55 (1716.39) | 5.52 (3.86) | 45.97 (35.27) |
| cardiac failure | 34 | 3.39 (2.42–4.74) | 3.38 (56.91) | 1.75 (0.09) | 3.38 (2.55) |
| hallucination, auditory | 34 | 20.17 (14.39–28.27) | 20.09 (613.54) | 4.32 (2.65) | 19.99 (15.07) |
| pericardial effusion a | 34 | 12.10 (8.64–16.96) | 12.06 (343.71) | 3.59 (1.92) | 12.02 (9.06) |
| psychotic disorder | 33 | 11.73 (8.33–16.53) | 11.69 (321.75) | 3.54 (1.88) | 11.66 (8.75) |
| mental disorder | 32 | 5.53 (3.91–7.83) | 5.51 (118.09) | 2.46 (0.79) | 5.51 (4.12) |
| hallucination, visual | 30 | 11.32 (7.91–16.21) | 11.29 (280.47) | 3.49 (1.83) | 11.25 (8.33) |
| dyslipidemia | 29 | 41.16 (28.53–59.39) | 41.03 (1119.78) | 5.34 (3.68) | 40.57 (29.85) |
| pneumonitis | 29 | 7.42 (5.15–10.69) | 7.40 (160.19) | 2.88 (1.22) | 7.38 (5.44) |
| amnesia | 27 | 3.80 (2.61–5.55) | 3.79 (55.51) | 1.92 (0.26) | 3.79 (2.76) |
| electrocardiogram qt prolonged a | 26 | 5.55 (3.78–8.16) | 5.54 (96.64) | 2.47 (0.80) | 5.53 (4.01) |
| delirium | 25 | 6.20 (4.18–9.18) | 6.18 (108.42) | 2.63 (0.96) | 6.17 (4.44) |
| fluid retention a | 24 | 3.59 (2.41–5.36) | 3.59 (44.73) | 1.84 (0.17) | 3.58 (2.56) |
| brain oedema a | 21 | 14.76 (9.61–22.66) | 14.72 (267.52) | 3.87 (2.21) | 14.67 (10.24) |
a indicates a PT not yet mentioned in the drug instruction. PT, Preferred Term in MedDRA; ROR, Reporting Odds Ratio; PRR, Proportional Reporting Ratio; IC, Information Component; EBGM, Empirical Bayesian Geometric Mean.
Table 8.
Signal strength of five drug-related DILI.
| DRUG | N | DILI reports | DILI Proportion (%) | ROR (95% CI) | PRR (χ2) | IC (IC025) | EBGM (EBGMO5) |
|---|---|---|---|---|---|---|---|
| crizotinib | 19,857 | 696 | 3.51 | 3.54 (3.28–3.82) ★ | 3.45 (1220.66) ★ | 1.78 (0.12) ★ | 3.45 (3.23) ★ |
| ceritinib | 8946 | 451 | 5.04 | 5.25 (4.77–5.77) ★ | 5.03 (1470.92) ★ | 2.33 (0.66) ★ | 5.03 (4.65) ★ |
| alectinib | 12,141 | 528 | 4.35 | 4.49 (4.12–4.90) ★ | 4.34 (1369.30) ★ | 2.12 (0.45) ★ | 4.34 (4.03) ★ |
| brigatinib | 7468 | 178 | 2.38 | 2.40 (2.07–2.78) ★ | 2.37 (141.76) ★ | 1.24 (−0.42) | 2.37 (2.09) ★ |
| lorlatinib | 8452 | 125 | 1.48 | 1.49 (1.25–1.77) ★ | 1.48 (19.61) | 0.56 (−1.10) | 1.48 (1.28) |
★ indicates statistical significance. ROR, Reporting Odds Ratio; PRR, Proportional Reporting Ratio; IC, Information Component; EBGM, Empirical Bayesian Geometric Mean.
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Yang, Y.; Tan, S.; Pu, Y.; Zhang, J. Safety Profile and Hepatotoxicity of Anaplastic Lymphoma Kinase Tyrosine Kinase Inhibitors: A Disproportionality Analysis Based on FDA Adverse Event Reporting System Database. Toxics 2025, 13, 210. https://doi.org/10.3390/toxics13030210
AMA Style
Yang Y, Tan S, Pu Y, Zhang J. Safety Profile and Hepatotoxicity of Anaplastic Lymphoma Kinase Tyrosine Kinase Inhibitors: A Disproportionality Analysis Based on FDA Adverse Event Reporting System Database. Toxics. 2025; 13(3):210. https://doi.org/10.3390/toxics13030210
Chicago/Turabian StyleYang, Yun, Shiyi Tan, Yuepu Pu, and Juan Zhang. 2025. "Safety Profile and Hepatotoxicity of Anaplastic Lymphoma Kinase Tyrosine Kinase Inhibitors: A Disproportionality Analysis Based on FDA Adverse Event Reporting System Database" Toxics 13, no. 3: 210. https://doi.org/10.3390/toxics13030210
APA StyleYang, Y., Tan, S., Pu, Y., & Zhang, J. (2025). Safety Profile and Hepatotoxicity of Anaplastic Lymphoma Kinase Tyrosine Kinase Inhibitors: A Disproportionality Analysis Based on FDA Adverse Event Reporting System Database. Toxics, 13(3), 210. https://doi.org/10.3390/toxics13030210
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