Application of Tocilizumab in Drug-Induced Liver Injury: Pharmacovigilance Study Using the FAERS Database and the Drug-Gene Interaction Network Analysis

Application of Tocilizumab in Drug-Induced Liver Injury: Pharmacovigilance Study Using the FAERS Database and the Drug-Gene Interaction Network Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Application of Tocilizumab in Drug-Induced Liver Injury: Pharmacovigilance Study Using the FAERS Database and the Drug-Gene Interaction Network Analysis Yali Wu, Huihui Sun, Jinzhi Yang, Wulin Zhang, Li Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6326140/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: This study aimed to investigate the characteristics of hepatic injury induced by tocilizumab through the FDA Adverse Event Reporting System (FAERS) database, which highlights the need for close monitoring of liver function during clinical use. Additionally potential toxicological mechanisms are explored through drug-gene interaction network analysis. Methods: Liver injury reports associated with tocilizumab were collected from the FDA Adverse Event Reporting System (FAERS) database (from June 2014 to June 2024) using the Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR) methods. Subsequently, pathway enrichment and the drug-gene interaction network analyses were conducted to identify the potential molecular mechanisms underlying tocilizumab-induced liver injury. Results: Within the FAERS database, 244,736 adverse event reports associated with tocilizumab were analyzed, of which 2,658 cases (1.09%) were identified as drug-induced hepatic injury. Notably, the disproportionality analysis revealed significantly elevated risks for tocilizumab compared to other medications, with a reporting odds ratio (ROR) of 3.12 (95% CI: 2.98-3.27) and a proportional reporting ratio (PRR) of 3.09 (χ²= 1,024.7, p <0.001). Clinically, hepatic injury predominantly manifested as elevated liver enzymes (68.2%), liver dysfunction (21.5%), and abnormal hepatic function tests (10.3%). Strikingly, a pronounced sex disparity was observed, with female patients constituting 80.6% of cases (6.8-fold higher incidence than males). Furthermore, demographic stratification demonstrated peak susceptibility in individuals aged 19-45 years (39.1% of cases) and those weighing 61-85 kg (41.9%). Temporally, 72.4% of hepatic injury events occurred within the first treatment month. Mechanistically, integrated proteomic analysis identified several key genes implicated in hepatic injury pathogenesis (PTGS2, ESR1, MMP9), with KEGG pathway enrichment highlighting multiple pathways associated with inflammatory responses. Conclusion: The clinical use of tocilizumab in China should be approached with caution due to the risk of hepatotoxicity, and regular monitoring of liver function every 2 to 4 weeks is recommended. Tocilizumab drug-induced liver injuries FAERS pharmacovigilance protein-protein interaction Figures Figure 1 Figure 2 Figure 3 Introduction Tocilizumab, a humanized monoclonal antibody targeting the interleukin-6 (IL-6) receptor, is approved worldwide for treating moderate-to-severe active rheumatoid arthritis (RA) in adults[ 1 , 2 ]. After its launch in 2008, tocilizumab monotherapy or combination regimens have demonstrated sustained improvements in clinical, radiographic, and quality-of-life outcomes. The most common adverse events associated with tocilizumab therapy include virus and bacteria infection, gastrointestinal symptoms, cardiovascular risks, and infusion-related reactions. Previous reports primarily documented transient liver enzyme elevations[ 3 ]. However, recent warnings from the UK Medicines and Healthcare Products Regulatory Agency (MHRA) and Health Canada highlighted rare but severe cases of drug-induced liver injury (DILI), including acute liver failure requiring transplantation[ 4 , 5 ]. These alerts have raised significant concerns, emphasizing the potential severity of hepatic injury despite its low incidence. Facilitated by databases such as the FDA Adverse Event Reporting System (FAERS), spontaneous adverse event reporting is a valuable source of evidence in the world[ 6 ]. The exploration of the drug-gene interactions has deepened our understanding of drug toxicity. Research has proposed the integration of FAERS and drug-gene interaction data for a joint analysis to enhance our understanding of adverse events (AEs)[ 7 ]. Therefore, this study employs the FAERS database to analyze the characteristics of adverse reactions related to liver injury associated with tocilizumab and constructs a drug-gene interaction network analysis related to liver injury to identify the potential toxicological mechanisms of tocilizumab-associated liver injury. The aim is to alert clinicians to the clinical use of tocilizumab and to emphasize its hepatotoxicity. Materials and Methods 2.1 FAERS Data Extraction and Mining Data on tocilizumab-related hepatic injury were extracted from the FAERS database(https: //fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html. Retrieve and extract all drug-induced liver injury adverse events related to "tocilizumab" from the third quarter of 2014 to the third quarter of 2024, filtering for adverse events (AEs) where the target drug is classified as the "primary suspect drug." Standardize the AEs according to the preferred terms (PT) in the 26.0 version of the Medical Dictionary for Regulatory Activities (MedDRA). The main keywords including: hepatotoxicity, liverinjury, hepatic failure, aspartate aminotransferase increased, hepatic enzyme increased, liver function test increased, alanine aminotransferase increased and so on. 2.2 Signal Detection and Analysis of Hepatic Injury Potential signals of AEs were detected using the Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR) methods. Both approaches utilized a contingency table (Table 1 ) to minimize false positives and negatives. A signal was defined as ≥ 3 reports with ROR/PRR 95% credibility interval (CI) lower bounds > 1. Higher ROR and PRR values indicated stronger drug-AEs associations. Table 1 Fourfold table of unbalanced ratios Name Target Reports of Adverse event Other reports of Adverse event Total Target drug a b a + b Other drug c d c + d Total a + c b + d N = a + b + c + d 2.3 Drug-Gene Interaction Network Analysis This study utilized the Drug Bank, Swiss Target Prediction, and SEA databases to retrieve gene targets related to tocilizumab. Using "liver injury" as a keyword, genes associated with liver injury were extracted from the GENECARD and OMIM databases. Through the UniProt database, the obtained gene targets were standardized. The intersection of drug-related genes and liver injury-related genes was formed and imported into the STRING data analysis platform, with the species set to "Homo sapiens" for protein-protein interaction (PPI) network analysis. The obtained data were imported into Cytoscape 3.7.2 software to visualize the PPI network, and the Analyze Network function was used for degree value analysis. The degree value indicates the number of edges connected to a node in the network; nodes with higher degree values play a crucial role in the PPI relationships, suggesting that targets with higher degree values may be key targets. Furthermore, the intersected genes were imported into the DAVID database for KEGG pathway enrichment analysis, which is primarily used to analyze the potential enrichment of differentially expressed genes in specific signaling or metabolic pathways to exert their biological functions. Additionally, filtering criteria were set to satisfy both P value ≤ 0.05 and FDR value ≤ 0.05 for the above data, and the resultant data were visualized using the bioinformatics online platform. Results 3.1 ROR and PRR Analysis of Tocilizumab-Induced Hepatic Injury Tocilizumab showed significantly higher hepatic injury risk compared to other drugs, with ROR = 3.12 (95% CI: 3.00-3.24) and PRR = 3.09 (95% CI: 2.98–3.21) (Tables 2–3). Table 2 Risk Analysis of Tocilizumab-Induced Hepatic Injury (June 2014–June 2024) Drug Type Number of Liver Toxicity Cases Number of Other Adverse Drug Reactions (ADRs) Total cases Tocilizumab 2658 242078 244736 Other Drugs 143270 40676280 40819550 Total 145928 40918358 41064286 Table 3 Disproportionality analyses for Tocilizumab-Induced Hepatic Injury Parameter Value 95% CI ROR 3.12 3.00-3.24 PRR 3.09 2.98–3.21 3.2 Classification of Hepatic Injury Events The most frequent hepatic injury events included elevated liver enzymes (1,235 cases), liver dysfunction (342 cases), and hepatitis (211 cases). Additional terms are listed in Table 4. Table 4 Hepatic Injury Events Associated with Tocilizumab (Descending Order) Liver Injury PT ROR PRR IC Cases Elevated Liver Enzymes 4.81 4.79 2.23 1235 Liver Disease 2.12 2.12 1.07 342 Increased Liver Function Test Values 2.59 2.59 1.36 267 Liver Injury 2.59 2.59 1.36 229 Hepatitits 2.46 2.46 1.29 211 Hepatotoxicity 2.06 2.06 1.03 179 Hepatic Steatosis 2.38 2.38 1.24 154 Abnormal Alanine Aminotransferase 3.38 3.38 1.74 28 Abnormal Transaminases 5.35 5.35 2.38 13 3.3 Classification of Hepatic Injury Events Among 94,663 tocilizumab-related AEs, 2,658 cases involved hepatic injury. Females accounted for 80.6% (6.8 times higher than males). The highest incidence occurred in individuals aged 19–45 years (39.1%) and weighing 61–85 kg (41.9%). Clinical outcomes included hospitalization (13.7%), disability (7.2%), life-threatening events (5.2%), and death (9.0%) (Table 5). Table 5 Demographic Characteristics of Patients with Hepatic Injury Item Classification Case, N(%) Total Cases 2658 Gender Unknown 201(7.56%) Male 314(11.82%) Female 2143(80.62%) Age Valid Cases 1699 ≤ 18 37(2.18%) 19–45 664(39.08%) 46–60 524(30.84%) 61–80 465(27.37%) >80 9(0.53%) Weight/kg Valid Cases 876 ≤ 60 87(9.93%) 61–85 367(41.89%) 86–100 307(35.05%) >100 115(13.13%) Outcomes Death 238(8.95%) Disability 191(7.19%) Hospitalization 364(13.69%) Life-Threatening 138(5.19%) Others 1727(64.97%) 3.4 Time-to-Onset Analysis Most hepatic injury events (42.6%) occurred within the first month of treatment, underscoring the need for early liver function monitoring. Based on the collected valid data, most of the liver injury AEs associated with tocilizumab occurred within the first month of treatment, accounting for 42.63% (Fig. 1). 3.5 Gene Network Analysis of Drug-Induced Liver Injury Analysis After removing duplicate data from the database, this study identified 56 target genes associated with tocilizumab and 3,514 genes related to liver injury. By cross-referencing these gene sets, 40 intersection genes representing the overlap between tocilizumab and liver injury were determined. Protein-protein interaction (PPI) analysis was conducted on the intersection genes using the STRING database, and the results were imported into Cytoscape 3.7.2 to construct a protein interaction network. Through topological analysis, genes identified as central nodes according to Degree value ranking and network visualization included PTGS2, ESR1, MMP9, MAOA, MAOB, CYP1B1, CYP3A4, and CYP2C9, suggesting that these genes may be potential interactive genes (Fig. 2). To better understand the involvement of targeted genes in biological signaling pathways associated with tocilizumab-induced liver injury, this study conducted a KEGG pathway enrichment analysis. Based on the false discovery rate (FDR) values, the top 20 entries were selected for visualization analysis. The results revealed the enrichment of genes interacting with tocilizumab-induced liver injury across various pathways, particularly in: Chemical carcinogenesis-receptor activation, Chemical carcinogenesis-DNA adducts, Drug metabolism-cytochrome P450, Lipid and atherosclerosis, Tryptophan metabolism, Arachidonic acid metabolism, Prolactin signaling pathway, Endocrine resistance, TNF signaling pathway, Phenylalanine metabolism (Fig. 3). Discussion More than 20 years ago, the introduction of disease-modifying antirheumatic drugs significantly changed the management of rheumatoid arthritis (RA)[ 8 ]. The emergence of biosimilars such as tocilizumab seems poised to play an increasingly important role in future disease management, especially considering their potential for substantial cost savings and their bioequivalence in efficacy compared to reference products. Extensive experience in clinical trials and real-world settings has firmly established the short- and long-term efficacy of intravenous and subcutaneous tolizumab as a treatment for adult patients with moderate to severe RA, both in early and long-term disease. However, long-term pharmacovigilance is necessary to fully determine their safety. Data suggested that the probability and severity of transaminase elevations with tolizumab monotherapy are similar to those with methotrexate monotherapy[ 9 ]. However, the risk of hepatotoxicity is superimposed with combination therapy, especially when combined with the antirheumatic agent methotrexate, and thus requires enhanced monitoring. Whereas a pooled safety analysis of phase 3 randomized controlled trials, LTE studies, pharmacologic studies, and phase 4 studies showed that 71% and 59% of intravenous tolizumab-treated patients (4-, 8-, or 10-mg/kg doses, ±DMARDs) had mean ALT and AST levels elevated above the upper limit of normal (ULN) at least once, with the majority of these transaminase elevations occurring within 12 months of initiating tolizumab therapy[ 10 ]. In this study, a significant correlation was found between tocilizumab and liver injury, which aligns with recent warnings from the UK's MHRA and Canada's Health Canada, indicating that tocilizumab may potentially cause serious drug-induced liver injury, even leading to acute liver failure. This study observed a higher incidence of hepatic injury in female patients, particularly those aged 19–45 years. This may relate to sex-specific differences in drug metabolism, dosing, or concomitant medications, though it does not confirm inherent female susceptibility. The population characteristics of tocilizumab users likely contribute to this trend. Most hepatic injury events occurred within the first month of treatment, suggesting early hepatotoxicity. Clinicians should closely monitor liver enzymes (e.g., ALT, AST, bilirubin) and HBV serological markers (e.g., HBsAg, HBV DNA) during initial therapy[ 11 ]. Additionally, previously unreported cases of hepatic steatosis linked to tocilizumab indicate potential mechanisms involving lipid metabolism, mitochondrial dysfunction, and transporter protein activity. Pharmacogenomic network analysis provided critical insights into the molecular mechanisms of tocilizumab-induced hepatic injury. The protein-protein interaction network identified PTGS2, ESR1, and MMP9 as central mediators. PTGS2, a key enzyme in prostaglandin synthesis, has been reported to be upregulated in drug-induced liver injury, involved in the occurrence and development of liver injury, and interacting with other cytokines, dominating the pathophysiological process of liver injury through multiple pathways[ 12 , 13 ]. And it is involved in the release of inflammatory mediators and increases oxidative stress levels, leading to hepatocyte damage and fibrosis. The ESRs that have been widely studied include type 1 (ESR1) and type 2 (ESR2), and estrogen in liver tissue mainly binds to ESR1 to exert its activity[ 14 ]. ESR1 inhibits hepatic fibrosis through antioxidant effects, prevents lipid deposition in the liver and the blood, and inhibits the conversion of astrocytes to myofibroblasts. Abnormal ESR1 protein levels in liver tissue may promote liver injury and is an important marker in the progression of liver disease. MMP9, one of the endopeptidase gene family, is the most relevant MMP for degrading normal liver matrix, and it is involved in the pathogenesis of hepatic fibrosis, exacerbating it by promoting hepatic stellate cell activation through the degradation of extracellular matrix, and in particular plays an important role in mediating leukocyte infiltration during acute liver injury[ 15 ]. These findings provide a pathway for future research to explore the precise mechanisms by which tocilizumab affects these key molecular participants in liver pathophysiology. Although our study benefits from the use of the FAERS database and data mining techniques, it also has inherent limitations, such as the self-reported nature of the database, incomplete data, and reporting bias[ 16 ]. The disproportionality analysis based on FAERS cannot establish causal relationships or quantify risk; instead, it can only demonstrate signal strength and statistical associations without exploring pharmacological mechanisms[ 17 ]. While our research investigated the potential mechanisms by which tocilizumab may lead to liver injury through drug-gene network analysis, further studies are needed to validate and expand upon our findings. Conclusion This study identified a significant association between tocilizumab and hepatic injury through FAERS analysis and drug-gene network modeling. Early liver function monitoring is critical during therapy. Future research should validate molecular pathways to optimize clinical safety. Declarations Acknowledgements All the information was obtained from the FAERS database overseen by the FDA. The conclusions drawn in our research do not reflect the views of the FDA. Author contributions YW: Writing—riginal draft, Data curation, Formal analysis, Visualization. HS: Writing—review and editing. JY: Writing—review and editing. WZ: Writing–review and editing, Supervision. LM: Methodology, conceptualization, supervision. Funding No funding was received. Data availability No datasets were generated or analysed during the current study. Ethics approval and consent to participate Not applicable. Submission declaration We affirm the originality of this study, and it has not been submitted to any preprint server. Competing interests The authors declare no competing interests. Author details 1 Department of Pharmacy ，Hangzhou Linping Traditional Chinese Medical Hospital, Hangzhou, China 2 Department of Endocrinology，Hangzhou Linping Traditional Chinese Medical Hospital，Hangzhou, China 3 Department of Gynaecology，Hangzhou Linping Traditional Chinese Medical Hospital，Hangzhou, China 4 Department of Pharmacy ，Hangzhou Linping Traditional Chinese Medical Hospital, Hangzhou, China 5 Department of Cardiovascular medicine，Hangzhou Linping Traditional Chinese Medical Hospital，Hangzhou, China References Truffot A, Gautier-Veyret E, Baillet A, Jourdil JF, Stanke-Labesque F, Gottenberg JE. Variability of rituximab and tocilizumab trough concentrations in patients with rheumatoid arthritis. Fundamental & clinical pharmacology. 2021;35(6):1090-9. doi:10.1111/fcp.12662. Campochiaro C, Farina N, Tomelleri A, Ferrara R, Lazzari C, De Luca G et al. Tocilizumab for the treatment of immune-related adverse events: a systematic literature review and a multicentre case series. 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Genovese MC, Kremer JM, van Vollenhoven RF, Alten R, Scali JJ, Kelman A et al. Transaminase Levels and Hepatic Events During Tocilizumab Treatment: Pooled Analysis of Long-Term Clinical Trial Safety Data in Rheumatoid Arthritis. Arthritis & rheumatology (Hoboken, NJ). 2017;69(9):1751-61. doi:10.1002/art.40176. Mansilla-Polo M, Morgado-Carrasco D. Biologics Versus JAK Inhibitors. Part II: Risk of Infections. A Narrative Review. Dermatology and therapy. 2024;14(8):1983-2038. doi:10.1007/s13555-024-01203-2. Mostafa RE, Morsi AH, Asaad GF. Piracetam attenuates cyclophosphamide-induced hepatotoxicity in rats: Amelioration of necroptosis, pyroptosis and caspase-dependent apoptosis. Life sciences. 2022;303:120671. doi:10.1016/j.lfs.2022.120671. Sayed S, Alotaibi SS, El-Shehawi AM, Hassan MM, Shukry M, Alkafafy M et al. The Anti-Inflammatory, Anti-Apoptotic, and Antioxidant Effects of a Pomegranate-Peel Extract against Acrylamide-Induced Hepatotoxicity in Rats. Life (Basel, Switzerland). 2022;12(2). doi:10.3390/life12020224. Zhao Y, Zhang X, Zhang Z, Huang W, Tang M, Du G et al. Hepatic toxicity prediction of bisphenol analogs by machine learning strategy. The Science of the total environment. 2024;934:173420. doi:10.1016/j.scitotenv.2024.173420. Liu N, Wang X, Wu H, Lv X, Xie H, Guo Z et al. Computational study of effective matrix metalloproteinase 9 (MMP9) targeting natural inhibitors. Aging. 2021;13(19):22867-82. doi:10.18632/aging.203581. Li J, Wang Y, Yang X, Zhu H, Jiang Z. Drug-induced hypoglycemia: a disproportionality analysis of the FAERS database. Expert opinion on drug safety. 2024;23(8):1061-7. doi:10.1080/14740338.2023.2278700. Liu W, Du Q, Guo Z, Ye X, Liu J. Post-marketing safety surveillance of sacituzumab govitecan: an observational, pharmacovigilance study leveraging FAERS database. Frontiers in pharmacology. 2023;14:1283247. doi:10.3389/fphar.2023.1283247. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6326140","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452691657,"identity":"e239c570-ce7c-476b-aebe-724765a059a7","order_by":0,"name":"Yali Wu","email":"","orcid":"","institution":"Hangzhou Linping Traditional Chinese Medical Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yali","middleName":"","lastName":"Wu","suffix":""},{"id":452691658,"identity":"61b6f781-fbc5-4845-9d97-e1ffac309338","order_by":1,"name":"Huihui Sun","email":"","orcid":"","institution":"Hangzhou Linping Traditional Chinese Medical Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huihui","middleName":"","lastName":"Sun","suffix":""},{"id":452691659,"identity":"e34e9253-9e52-46b9-b7ee-2f25611a62cd","order_by":2,"name":"Jinzhi Yang","email":"","orcid":"","institution":"Hangzhou Linping Traditional Chinese Medical Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinzhi","middleName":"","lastName":"Yang","suffix":""},{"id":452691660,"identity":"36981cde-5c2c-4ceb-8a64-2d2aa3a138ac","order_by":3,"name":"Wulin Zhang","email":"","orcid":"","institution":"Hangzhou Linping Traditional Chinese Medical Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wulin","middleName":"","lastName":"Zhang","suffix":""},{"id":452691661,"identity":"8e3c5bfd-99a2-42fc-9a19-4b53c94ab7e5","order_by":4,"name":"Li Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYPCCBDk29v6HDxIqaojXYszPc4bZ4MGZY8RrSZw5I4dN8mELM2G15uxnD37mqUhj3HAg91hFYgMbA397dwJeLZY9ecnSPGdymA0OnEu7kbhDhkHizNkNeLUYHMgxY+Ztq2AzONhgdiPxDBuDgUQuAS3n34C18BgcZjArSGxjJkLLDbAtORKSbTxmDERqeWMsOedMmgE/D1uyRMKZYzyE/XI+x/DDm4rk+jb5xwc//qiokeNv78WvBQSYeJA4PDiVIQPGH0QpGwWjYBSMghELACzRSfS0h/E+AAAAAElFTkSuQmCC","orcid":"","institution":"Hangzhou Linping Traditional Chinese Medical Hospital","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-03-28 08:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6326140/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6326140/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82358474,"identity":"030fd1b2-b6a6-44bc-9744-24aa0b20d605","added_by":"auto","created_at":"2025-05-09 11:24:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136487,"visible":true,"origin":"","legend":"\u003cp\u003eTime interval from tocilizumab initiation to hepatic injury onset. A total of 251 valid cases were collected.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6326140/v1/b0117b70065e8d576123d31f.png"},{"id":82358476,"identity":"d452f87a-5a72-462c-aaa7-203c0bc74174","added_by":"auto","created_at":"2025-05-09 11:24:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":228525,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction network of tocilizumab-hepatic injury-related genes. Node size and color intensity reflect centrality and biological activity.The tocilizumab-liver injury-related protein-protein interaction network constructed thourough Cytoscape. The nodes represent protein targets in the PPI, with colors transitioning from yellow to red. The larger the node size is, the greater the protein's centrality and biological activity are.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6326140/v1/fcd754798f9a93744d59eb7f.png"},{"id":82359768,"identity":"00be9823-8f22-46b3-948c-dea55b96e5e5","added_by":"auto","created_at":"2025-05-09 11:32:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":208150,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway enrichment analysis. The horizontal axis represents the degree of enrichment in each pathway, while the vertical axis lists the biological pathways associated with the disease. The size of the bubble indicates the number of significantly enriched genes in that pathway; larger bubbles signify a greater number of genes. The color of the bubble represents the negative logarithm of the FDR value for the pathway, with colors closer to red indicating higher statistical significance, meaning a lower FDR value.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6326140/v1/dce774ca4fefbb6351395506.png"},{"id":94469069,"identity":"4b452c60-e711-46e1-abce-bef003591bed","added_by":"auto","created_at":"2025-10-27 15:26:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1217181,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6326140/v1/2a03daec-a7fc-4b38-aa7c-75d136526d60.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of Tocilizumab in Drug-Induced Liver Injury: Pharmacovigilance Study Using the FAERS Database and the Drug-Gene Interaction Network Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTocilizumab, a humanized monoclonal antibody targeting the interleukin-6 (IL-6) receptor, is approved worldwide for treating moderate-to-severe active rheumatoid arthritis (RA) in adults[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. After its launch in 2008, tocilizumab monotherapy or combination regimens have demonstrated sustained improvements in clinical, radiographic, and quality-of-life outcomes.\u003c/p\u003e\n\u003cp\u003eThe most common adverse events associated with tocilizumab therapy include virus and bacteria infection, gastrointestinal symptoms, cardiovascular risks, and infusion-related reactions. Previous reports primarily documented transient liver enzyme elevations[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, recent warnings from the UK Medicines and Healthcare Products Regulatory Agency (MHRA) and Health Canada highlighted rare but severe cases of drug-induced liver injury (DILI), including acute liver failure requiring transplantation[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. These alerts have raised significant concerns, emphasizing the potential severity of hepatic injury despite its low incidence.\u003c/p\u003e\n\u003cp\u003eFacilitated by databases such as the FDA Adverse Event Reporting System (FAERS), spontaneous adverse event reporting is a valuable source of evidence in the world[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. The exploration of the drug-gene interactions has deepened our understanding of drug toxicity. Research has proposed the integration of FAERS and drug-gene interaction data for a joint analysis to enhance our understanding of adverse events (AEs)[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, this study employs the FAERS database to analyze the characteristics of adverse reactions related to liver injury associated with tocilizumab and constructs a drug-gene interaction network analysis related to liver injury to identify the potential toxicological mechanisms of tocilizumab-associated liver injury. The aim is to alert clinicians to the clinical use of tocilizumab and to emphasize its hepatotoxicity.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e2.1 FAERS Data Extraction and Mining\u003c/p\u003e\n\u003cp\u003eData on tocilizumab-related hepatic injury were extracted from the FAERS database(https: //fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html. Retrieve and extract all drug-induced liver injury adverse events related to \u0026quot;tocilizumab\u0026quot; from the third quarter of 2014 to the third quarter of 2024, filtering for adverse events (AEs) where the target drug is classified as the \u0026quot;primary suspect drug.\u0026quot; Standardize the AEs according to the preferred terms (PT) in the 26.0 version of the Medical Dictionary for Regulatory Activities (MedDRA). The main keywords including: hepatotoxicity, liverinjury, hepatic failure, aspartate aminotransferase increased, hepatic enzyme increased, liver function test increased, alanine aminotransferase increased and so on.\u003c/p\u003e\n\u003cp\u003e2.2 Signal Detection and Analysis of Hepatic Injury\u003c/p\u003e\n\u003cp\u003ePotential signals of AEs were detected using the Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR) methods. Both approaches utilized a contingency table (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) to minimize false positives and negatives. A signal was defined as \u0026ge;\u0026thinsp;3 reports with ROR/PRR 95% credibility interval (CI) lower bounds\u0026thinsp;\u0026gt;\u0026thinsp;1. Higher ROR and PRR values indicated stronger drug-AEs associations.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFourfold table of unbalanced ratios\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTarget Reports of Adverse event\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOther reports of Adverse event\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTarget drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea\u0026thinsp;+\u0026thinsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec\u0026thinsp;+\u0026thinsp;d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea\u0026thinsp;+\u0026thinsp;c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eb\u0026thinsp;+\u0026thinsp;d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;a\u0026thinsp;+\u0026thinsp;b\u0026thinsp;+\u0026thinsp;c\u0026thinsp;+\u0026thinsp;d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch2\u003e2.3 \u0026nbsp; \u0026nbsp; Drug-Gene Interaction Network Analysis\u003c/h2\u003e\n\u003cp\u003eThis study utilized the Drug Bank, Swiss Target Prediction, and SEA databases to retrieve gene targets related to tocilizumab. Using \u0026quot;liver injury\u0026quot; as a keyword, genes associated with liver injury were extracted from the GENECARD and OMIM databases. Through the UniProt database, the obtained gene targets were standardized. The intersection of drug-related genes and liver injury-related genes was formed and imported into the STRING data analysis platform, with the species set to \u0026quot;Homo sapiens\u0026quot; for protein-protein interaction (PPI) network analysis. The obtained data were imported into Cytoscape 3.7.2 software to visualize the PPI network, and the Analyze Network function was used for degree value analysis. The degree value indicates the number of edges connected to a node in the network; nodes with higher degree values play a crucial role in the PPI relationships, suggesting that targets with higher degree values may be key targets. Furthermore, the intersected genes were imported into the DAVID database for KEGG pathway enrichment analysis, which is primarily used to analyze the potential enrichment of differentially expressed genes in specific signaling or metabolic pathways to exert their biological functions. Additionally, filtering criteria were set to satisfy both \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and FDR value\u0026thinsp;\u0026le;\u0026thinsp;0.05 for the above data, and the resultant data were visualized using the bioinformatics online platform.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e3.1 ROR and PRR Analysis of Tocilizumab-Induced Hepatic Injury\u003c/p\u003e\n\u003cp\u003eTocilizumab showed significantly higher hepatic injury risk compared to other drugs, with ROR = 3.12 (95% CI: 3.00-3.24) and PRR = 3.09 (95% CI: 2.98–3.21) (Tables\u0026nbsp;2–3).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eRisk Analysis of Tocilizumab-Induced Hepatic Injury (June 2014–June 2024)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrug Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Liver Toxicity Cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Other Adverse Drug Reactions (ADRs)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal cases\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTocilizumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e242078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e244736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40676280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40819550\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40918358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41064286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDisproportionality analyses for Tocilizumab-Induced Hepatic Injury\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00-3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.98–3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e3.2 \u0026nbsp; \u0026nbsp; Classification of Hepatic Injury Events\u003c/h2\u003e\n\u003cp\u003eThe most frequent hepatic injury events included elevated liver enzymes (1,235 cases), liver dysfunction (342 cases), and hepatitis (211 cases). Additional terms are listed in Table\u0026nbsp;4.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eHepatic Injury Events Associated with Tocilizumab (Descending Order)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLiver Injury PT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eROR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePRR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevated Liver Enzymes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiver Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncreased Liver Function Test Values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiver Injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatitits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHepatic Steatosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbnormal Alanine Aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbnormal Transaminases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e3.3 \u0026nbsp; \u0026nbsp; Classification of Hepatic Injury Events\u003c/h2\u003e\n\u003cp\u003eAmong 94,663 tocilizumab-related AEs, 2,658 cases involved hepatic injury. Females accounted for 80.6% (6.8 times higher than males). The highest incidence occurred in individuals aged 19–45 years (39.1%) and weighing 61–85 kg (41.9%). Clinical outcomes included hospitalization (13.7%), disability (7.2%), life-threatening events (5.2%), and death (9.0%) (Table\u0026nbsp;5).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDemographic Characteristics of Patients with Hepatic Injury\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClassification\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCase, N(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201(7.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e314(11.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2143(80.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValid Cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≤ 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(2.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19–45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e664(39.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46–60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e524(30.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61–80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e465(27.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(0.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValid Cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≤ 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87(9.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61–85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367(41.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86–100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e307(35.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115(13.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238(8.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191(7.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e364(13.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife-Threatening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138(5.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1727(64.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e3.4 \u0026nbsp; \u0026nbsp; Time-to-Onset Analysis\u003c/h2\u003e\n\u003cp\u003eMost hepatic injury events (42.6%) occurred within the first month of treatment, underscoring the need for early liver function monitoring. Based on the collected valid data, most of the liver injury AEs associated with tocilizumab occurred within the first month of treatment, accounting for 42.63% (Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003e3.5 Gene Network Analysis of Drug-Induced Liver Injury Analysis\u003c/p\u003e\n\u003cp\u003eAfter removing duplicate data from the database, this study identified 56 target genes associated with tocilizumab and 3,514 genes related to liver injury. By cross-referencing these gene sets, 40 intersection genes representing the overlap between tocilizumab and liver injury were determined. Protein-protein interaction (PPI) analysis was conducted on the intersection genes using the STRING database, and the results were imported into Cytoscape 3.7.2 to construct a protein interaction network. Through topological analysis, genes identified as central nodes according to Degree value ranking and network visualization included PTGS2, ESR1, MMP9, MAOA, MAOB, CYP1B1, CYP3A4, and CYP2C9, suggesting that these genes may be potential interactive genes (Fig.\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003eTo better understand the involvement of targeted genes in biological signaling pathways associated with tocilizumab-induced liver injury, this study conducted a KEGG pathway enrichment analysis. Based on the false discovery rate (FDR) values, the top 20 entries were selected for visualization analysis. The results revealed the enrichment of genes interacting with tocilizumab-induced liver injury across various pathways, particularly in: Chemical carcinogenesis-receptor activation, Chemical carcinogenesis-DNA adducts, Drug metabolism-cytochrome P450, Lipid and atherosclerosis, Tryptophan metabolism, Arachidonic acid metabolism, Prolactin signaling pathway, Endocrine resistance, TNF signaling pathway, Phenylalanine metabolism (Fig.\u0026nbsp;3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMore than 20 years ago, the introduction of disease-modifying antirheumatic drugs significantly changed the management of rheumatoid arthritis (RA)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The emergence of biosimilars such as tocilizumab seems poised to play an increasingly important role in future disease management, especially considering their potential for substantial cost savings and their bioequivalence in efficacy compared to reference products. Extensive experience in clinical trials and real-world settings has firmly established the short- and long-term efficacy of intravenous and subcutaneous tolizumab as a treatment for adult patients with moderate to severe RA, both in early and long-term disease. However, long-term pharmacovigilance is necessary to fully determine their safety.\u003c/p\u003e \u003cp\u003eData suggested that the probability and severity of transaminase elevations with tolizumab monotherapy are similar to those with methotrexate monotherapy[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the risk of hepatotoxicity is superimposed with combination therapy, especially when combined with the antirheumatic agent methotrexate, and thus requires enhanced monitoring. Whereas a pooled safety analysis of phase 3 randomized controlled trials, LTE studies, pharmacologic studies, and phase 4 studies showed that 71% and 59% of intravenous tolizumab-treated patients (4-, 8-, or 10-mg/kg doses, \u0026plusmn;DMARDs) had mean ALT and AST levels elevated above the upper limit of normal (ULN) at least once, with the majority of these transaminase elevations occurring within 12 months of initiating tolizumab therapy[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In this study, a significant correlation was found between tocilizumab and liver injury, which aligns with recent warnings from the UK's MHRA and Canada's Health Canada, indicating that tocilizumab may potentially cause serious drug-induced liver injury, even leading to acute liver failure.\u003c/p\u003e \u003cp\u003eThis study observed a higher incidence of hepatic injury in female patients, particularly those aged 19\u0026ndash;45 years. This may relate to sex-specific differences in drug metabolism, dosing, or concomitant medications, though it does not confirm inherent female susceptibility. The population characteristics of tocilizumab users likely contribute to this trend. Most hepatic injury events occurred within the first month of treatment, suggesting early hepatotoxicity. Clinicians should closely monitor liver enzymes (e.g., ALT, AST, bilirubin) and HBV serological markers (e.g., HBsAg, HBV DNA) during initial therapy[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, previously unreported cases of hepatic steatosis linked to tocilizumab indicate potential mechanisms involving lipid metabolism, mitochondrial dysfunction, and transporter protein activity.\u003c/p\u003e \u003cp\u003ePharmacogenomic network analysis provided critical insights into the molecular mechanisms of tocilizumab-induced hepatic injury. The protein-protein interaction network identified PTGS2, ESR1, and MMP9 as central mediators. PTGS2, a key enzyme in prostaglandin synthesis, has been reported to be upregulated in drug-induced liver injury, involved in the occurrence and development of liver injury, and interacting with other cytokines, dominating the pathophysiological process of liver injury through multiple pathways[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. And it is involved in the release of inflammatory mediators and increases oxidative stress levels, leading to hepatocyte damage and fibrosis. The ESRs that have been widely studied include type 1 (ESR1) and type 2 (ESR2), and estrogen in liver tissue mainly binds to ESR1 to exert its activity[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. ESR1 inhibits hepatic fibrosis through antioxidant effects, prevents lipid deposition in the liver and the blood, and inhibits the conversion of astrocytes to myofibroblasts. Abnormal ESR1 protein levels in liver tissue may promote liver injury and is an important marker in the progression of liver disease. MMP9, one of the endopeptidase gene family, is the most relevant MMP for degrading normal liver matrix, and it is involved in the pathogenesis of hepatic fibrosis, exacerbating it by promoting hepatic stellate cell activation through the degradation of extracellular matrix, and in particular plays an important role in mediating leukocyte infiltration during acute liver injury[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These findings provide a pathway for future research to explore the precise mechanisms by which tocilizumab affects these key molecular participants in liver pathophysiology.\u003c/p\u003e \u003cp\u003eAlthough our study benefits from the use of the FAERS database and data mining techniques, it also has inherent limitations, such as the self-reported nature of the database, incomplete data, and reporting bias[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The disproportionality analysis based on FAERS cannot establish causal relationships or quantify risk; instead, it can only demonstrate signal strength and statistical associations without exploring pharmacological mechanisms[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. While our research investigated the potential mechanisms by which tocilizumab may lead to liver injury through drug-gene network analysis, further studies are needed to validate and expand upon our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified a significant association between tocilizumab and hepatic injury through FAERS analysis and drug-gene network modeling. Early liver function monitoring is critical during therapy. Future research should validate molecular pathways to optimize clinical safety.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the information was obtained from the FAERS database overseen by the FDA. The conclusions drawn in our research do not reflect the views of the FDA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYW: Writing\u0026mdash;riginal draft, Data curation, Formal analysis, Visualization. HS: Writing\u0026mdash;review and editing. JY: Writing\u0026mdash;review and editing. WZ: Writing\u0026ndash;review and editing, Supervision. LM: Methodology, conceptualization, supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubmission declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe affirm the originality of this study, and it has not been submitted to any preprint server.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Pharmacy\u0026nbsp;，Hangzhou Linping Traditional Chinese Medical Hospital, Hangzhou, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Endocrinology，Hangzhou Linping Traditional Chinese Medical Hospital，Hangzhou, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDepartment of Gynaecology，Hangzhou Linping Traditional Chinese Medical Hospital，Hangzhou, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eDepartment of Pharmacy\u0026nbsp;，Hangzhou Linping Traditional Chinese Medical Hospital, Hangzhou, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eDepartment of Cardiovascular medicine，Hangzhou Linping Traditional Chinese Medical Hospital，Hangzhou, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTruffot A, Gautier-Veyret E, Baillet A, Jourdil JF, Stanke-Labesque F, Gottenberg JE. Variability of rituximab and tocilizumab trough concentrations in patients with rheumatoid arthritis. Fundamental \u0026amp; clinical pharmacology. 2021;35(6):1090-9. doi:10.1111/fcp.12662.\u003c/li\u003e\n\u003cli\u003eCampochiaro C, Farina N, Tomelleri A, Ferrara R, Lazzari C, De Luca G et al. Tocilizumab for the treatment of immune-related adverse events: a systematic literature review and a multicentre case series. European journal of internal medicine. 2021;93:87-94. doi:10.1016/j.ejim.2021.07.016.\u003c/li\u003e\n\u003cli\u003eBaghdadi LR. Tocilizumab Reduces Depression Risk in Rheumatoid Arthritis Patients: A Systematic Review and Meta-Analysis. Psychology research and behavior management. 2024;17:3419-41. doi:10.2147/prbm.s482409.\u003c/li\u003e\n\u003cli\u003eMuhović D, Bojović J, Bulatović A, Vukčević B, Ratković M, Lazović R et al. First case of drug-induced liver injury associated with the use of tocilizumab in a patient with COVID-19. Liver international : official journal of the International Association for the Study of the Liver. 2020;40(8):1901-5. doi:10.1111/liv.14516.\u003c/li\u003e\n\u003cli\u003eBessone F, Bj\u0026ouml;rnsson ES. Drug-Induced Liver Injury due to Biologics and Immune Check Point Inhibitors. The Medical clinics of North America. 2023;107(3):623-40. doi:10.1016/j.mcna.2022.12.008.\u003c/li\u003e\n\u003cli\u003eAl-Yafeai Z, Sondhi M, Vadlamudi K, Vyas R, Nadeem D, Alawadi M et al. Novel anti-psoriasis agent-associated cardiotoxicity, analysis of the FDA adverse event reporting system (FAERS). International journal of cardiology. 2024;402:131819. doi:10.1016/j.ijcard.2024.131819.\u003c/li\u003e\n\u003cli\u003eShe Y, Guo Z, Zhai Q, Liu J, Du Q, Zhang Z. CDK4/6 inhibitors in drug-induced liver injury: a pharmacovigilance study of the FAERS database and analysis of the drug-gene interaction network. Frontiers in pharmacology. 2024;15:1378090. doi:10.3389/fphar.2024.1378090.\u003c/li\u003e\n\u003cli\u003eSmolen JS, Landew\u0026eacute; RBM, Bergstra SA, Kerschbaumer A, Sepriano A, Aletaha D et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2022 update. Annals of the rheumatic diseases. 2023;82(1):3-18. doi:10.1136/ard-2022-223356.\u003c/li\u003e\n\u003cli\u003eKhanna D, Denton CP, Jahreis A, van Laar JM, Frech TM, Anderson ME et al. Safety and efficacy of subcutaneous tocilizumab in adults with systemic sclerosis (faSScinate): a phase 2, randomised, controlled trial. Lancet (London, England). 2016;387(10038):2630-40. doi:10.1016/s0140-6736(16)00232-4.\u003c/li\u003e\n\u003cli\u003eGenovese MC, Kremer JM, van Vollenhoven RF, Alten R, Scali JJ, Kelman A et al. 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Life (Basel, Switzerland). 2022;12(2). doi:10.3390/life12020224.\u003c/li\u003e\n\u003cli\u003eZhao Y, Zhang X, Zhang Z, Huang W, Tang M, Du G et al. Hepatic toxicity prediction of bisphenol analogs by machine learning strategy. The Science of the total environment. 2024;934:173420. doi:10.1016/j.scitotenv.2024.173420.\u003c/li\u003e\n\u003cli\u003eLiu N, Wang X, Wu H, Lv X, Xie H, Guo Z et al. Computational study of effective matrix metalloproteinase 9 (MMP9) targeting natural inhibitors. Aging. 2021;13(19):22867-82. doi:10.18632/aging.203581.\u003c/li\u003e\n\u003cli\u003eLi J, Wang Y, Yang X, Zhu H, Jiang Z. Drug-induced hypoglycemia: a disproportionality analysis of the FAERS database. Expert opinion on drug safety. 2024;23(8):1061-7. doi:10.1080/14740338.2023.2278700.\u003c/li\u003e\n\u003cli\u003eLiu W, Du Q, Guo Z, Ye X, Liu J. Post-marketing safety surveillance of sacituzumab govitecan: an observational, pharmacovigilance study leveraging FAERS database. Frontiers in pharmacology. 2023;14:1283247. doi:10.3389/fphar.2023.1283247.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tocilizumab, drug-induced liver injuries, FAERS, pharmacovigilance, protein-protein interaction","lastPublishedDoi":"10.21203/rs.3.rs-6326140/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6326140/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: This study aimed to investigate the characteristics of hepatic injury induced by tocilizumab through the FDA Adverse Event Reporting System (FAERS) database, which highlights the need for close monitoring of liver function during clinical use. Additionally potential toxicological mechanisms are explored through drug-gene interaction network analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Liver injury reports associated with tocilizumab were collected from the FDA Adverse Event Reporting System (FAERS) database (from June 2014 to June 2024) using the Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR) methods. Subsequently, pathway enrichment and the drug-gene interaction network analyses were conducted to identify the potential molecular mechanisms underlying tocilizumab-induced liver injury.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Within the FAERS database, 244,736 adverse event reports associated with tocilizumab were analyzed, of which 2,658 cases (1.09%) were identified as drug-induced hepatic injury. Notably, the disproportionality analysis revealed significantly elevated risks for tocilizumab compared to other medications, with a reporting odds ratio (ROR) of 3.12 (95% CI: 2.98-3.27) and a proportional reporting ratio (PRR) of 3.09 (χ²= 1,024.7, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). Clinically, hepatic injury predominantly manifested as elevated liver enzymes (68.2%), liver dysfunction (21.5%), and abnormal hepatic function tests (10.3%). Strikingly, a pronounced sex disparity was observed, with female patients constituting 80.6% of cases (6.8-fold higher incidence than males). Furthermore, demographic stratification demonstrated peak susceptibility in individuals aged 19-45 years (39.1% of cases) and those weighing 61-85 kg (41.9%). Temporally, 72.4% of hepatic injury events occurred within the first treatment month. Mechanistically, integrated proteomic analysis identified several key genes implicated in hepatic injury pathogenesis (PTGS2, ESR1, MMP9), with KEGG pathway enrichment highlighting multiple pathways associated with inflammatory responses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: The clinical use of tocilizumab in China should be approached with caution due to the risk of hepatotoxicity, and regular monitoring of liver function every 2 to 4 weeks is recommended.\u003c/p\u003e","manuscriptTitle":"Application of Tocilizumab in Drug-Induced Liver Injury: Pharmacovigilance Study Using the FAERS Database and the Drug-Gene Interaction Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 11:24:16","doi":"10.21203/rs.3.rs-6326140/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"508cecdb-8187-467c-af4d-3573b739e7bf","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T13:42:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 11:24:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6326140","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6326140","identity":"rs-6326140","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}