Exploring the Association Between Upadacitinib and Venous Thromboembolism: An Integrated Analysis of Adverse Drug Events and Network Toxicology Mechanisms

Exploring the Association Between Upadacitinib and Venous Thromboembolism: An Integrated Analysis of Adverse Drug Events and Network Toxicology Mechanisms | 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 Article Exploring the Association Between Upadacitinib and Venous Thromboembolism: An Integrated Analysis of Adverse Drug Events and Network Toxicology Mechanisms Xingyu Liao, Guoliang Luo, Min Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7223810/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 To investigate the association between upadacitinib and venous thromboembolism (VTE) and explore potential toxicological mechanisms by integrating adverse drug events (ADEs) data with network toxicology analyses. Methods A retrospective analysis of upadacitinib-related ADEs reported in the FAERS database from January 2004 to March 2025 was conducted. Signal detection methods included Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS). Potential molecular mechanisms were explored through network toxicology. Predicted targets of upadacitinib were obtained from TOXRIC, STITCH, and SwissTargetPrediction, while VTE-related targets were retrieved from GeneCards. Protein-protein interaction (PPI) networks were constructed using STRING and visualized with Cytoscape. Functional enrichment analyses (GO and KEGG) were performed using DAVID. Molecular docking was conducted via CB-Dock2. Results A total of 712 reports linked upadacitinib to VTE, showing a significant positive signal (ROR [95% CI]: 1.65 [1.54–1.77]; IC025: 0.72 [0.61]). The median time to VTE onset was 122 days. Network toxicology identified six core targets: STAT3, NFKB1, GSK3B, HIF1A, HSP90AB1, and CCND1. Enriched pathways included Human Cytomegalovirus infection, HIF-1 signaling, Ras signaling, Neurotrophin signaling, and PI3K-Akt signaling. Molecular docking revealed strong binding affinities between upadacitinib and these targets. Conclusion upadacitinib may be associated with an increased risk of VTE, mediated by multiple signaling pathways and key toxicological targets. These findings provide mechanistic insights into upadacitinib-induced VTE and support the need for enhanced pharmacovigilance in clinical settings. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Drug discovery Health sciences/Medical research Food and Drug Administration Adverse Event Reporting System pharmacovigilance network toxicology upadacitinib inflammatory diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Upadacitinib is a selective Janus kinase 1 (JAK1) inhibitor used to treat immune-mediated inflammatory disorders, including rheumatoid arthritis (RA), psoriatic arthritis (PsA), atopic dermatitis, axial spondyloarthritis, moderate to severe ulcerative colitis (UC) and Crohn’s disease (CD) 1 , 2 . Compared to other biologic therapies, JAK inhibitors such as upadacitinib offer advantages including oral administration and rapid onset of action, and have shown high efficacy in inducing and maintaining remission in UC and CD 3 – 5 . However, concerns have emerged regarding a possible thrombotic risk associated with upadacitinib, prompted by an increasing number of reports of venous thromboembolism (VTE) 6 , 7 . VTE, primarily presenting as deep vein thrombosis (DVT), is a serious vascular complication linked to significant morbidity and mortality 8 . Although case reports suggest a potential link between JAK inhibitors and VTE, there remains a lack of comprehensive investigation into the adverse event profiles and toxicological mechanisms underlying upadacitinib-induced VTE. To address this gap, the present study examines the association between upadacitinib and VTE by integrating data from adverse drug events (ADEs) reports with network toxicology analysis. This integrative approach aims to elucidate potential mechanistic pathways and toxicological targets, providing a theoretical foundation for improved safety assessment and supporting the application of precision medicine in upadacitinib therapy. 2 Method 2.1 Data source The FAERS database was used as the data source between January 2004 and March 2025. FAERS is an open database maintained by the FDA that collects ADE reports submitted by healthcare professionals, consumers, and manufacturers from across the world. 2.2 Data cleaning Before analysis, duplicate data with identical gender, age, reporting country, adverse events, drug names, and start and end dates were removed. 2.2 Data processing Upadacitinib was selected as the study object. The generic name, brand name, and abbreviation of the drug from the FDA, EMA, and relevant clinical guidelines were used as search keywords to identify the relevant ADE reports. Only ADE reports wherein upadacitinib was considered the primary suspect were included. The preferred terms for VTE adverse events were established using MedDRA (version 25.0) and relevant literature, as detailed in Supplementary Table 1. 2.4 Disproportionality analysis Four algorithms were used in the pharmacovigilance research: ROR, PRR, BCPNN, and MGPS, to identify signals related to adverse drug reactions (detailed formulas and positive signal criteria are provided in Supplementary Table 2). The algorithm values reflect the strength of the association between the drug and corresponding ADE signals 9 . A signal was considered positive only when it met the criteria of two algorithms simultaneously. All statistical analyses were conducted using R software (version 4.1.2) and Excel 2019. 2.5 Network toxicological analysis The predicted targets of upadacitinib were obtained from the TOXRIC, STITCH, and SwissTargetPrediction databases, whereas VTE-related targets were collected from GeneCards. The intersection of these two sets of targets gave the potential toxicity targets. A protein–protein interaction (PPI) network was constructed using STRING and Cytoscape to identify the key targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the DAVID database, and the results were selected based on p-values and corrected p-values (q-values), both less than 0.05. 2.6 Molecular docking Molecular docking was performed to analyze the binding modes and affinities between upadacitinib and the key target proteins. CB-Dock2 was used to preprocess the small-molecule ligands and the key target proteins. The docking site with the lowest Vina score was selected as the optimal binding mode. The binding energies were calculated, and the binding mode was visualized in 3D. The 2D docking images were generated using Discovery Studio to illustrate the key interactions such as hydrogen bonds and hydrophobic interactions. 3 Results 3.1 General characteristics Between January 2004 and March 2025, a total of 191,310 adverse event reports related to JAK inhibitors were identified in the FAERS database, including 2,227 cases of VTE, of which 712 were associated with upadacitinib. The majority of these upadacitinib-related VTE cases occurred in females (414 cases, 58.1%), while males accounted for 245 cases (34.4%). The highest age-specific incidence was observed in the 18–65 age group (40.0%), followed by those aged ≥ 85 years (21.9%). Among reported outcomes, hospitalization occurred in 45.5% of cases, life-threatening events in 5.9%, and death in 4.2%. Overall, 98.5% of the events were classified as serious. Reporting trends showed a steady increase over time, peaking in 2021 with 245 reports (34.4%). Most reports originated from consumers (48.3%) and physicians (35.0%), with the United States (60.5%) and Germany (6.2%) being the leading countries of origin (Supplementary Table 1). 3.2 Disproportionality analysis The correlation and signal values of upadacitinib with VTE-related ADEs are presented in Fig. 1 and Table 2. Upadacitinib was associated with positive signals for VTE (ROR(95% CI): 1.65 (1.54–1.77), IC(IC025): 0.72 (0.61)). The VTE-related adverse events that exhibited positive signals include: Pulmonary embolism (ROR(95% CI): 1.36 (1.22–1.51), IC(IC025): 0.44 (0.28)), Pulmonary thrombosis (ROR(95% CI): 7.94 (6.92–9.12), PRR: 7.93 (1228.84), EBGM(EBGM05): 7.79 (6.94), IC(IC025): 2.96 (2.76)), Venous Occlusion (ROR(95% CI): 2.63 (1.49–4.65), PRR: 2.63 (12.08), IC(IC025): 1.39 (0.59)), Portal Vein Thrombosis (ROR(95% CI): 1.94 (1.17–3.23), IC(IC025): 0.96 (0.23)), Venous Thrombosis Limb (ROR(95% CI): 4.33 (2.89–6.47), PRR: 4.33 (60.66), EBGM(EBGM05): 4.29 (3.06), IC(IC025): 2.1 (1.52)), Retinal Vein Occlusion (ROR(95% CI): 3.54 (2.37–5.29), PRR: 3.54 (43.35), EBGM(EBGM05): 3.52 (2.51), IC(IC025): 1.81 (1.23)), Thrombosed Varicose Vein (EBGM(EBGM05): 14.49 (4.44), IC(IC025): 3.86 (2.14)), and Portal Vein Embolism (EBGM(EBGM05): 17.13 (3.2), IC(IC025): 4.1 (1.97)). 3.3 The time of adverse reaction onset Based on the START_DT and EVENT_DT data fields, the median time to VTE onset was 122 days (interquartile range: 40–399.75 days). As shown in Fig. 2 , adverse events occurred most frequently after 360 days of treatment, accounting for approximately 27.8% of the total. Figure 3 illustrates the cumulative incidence of VTE-related adverse events that demonstrated significant signals. 3.4 PPI Network Analysis of Upadacitinib-Related VTE Toxicity Targets To investigate the toxicological mechanisms of upadacitinib-related VTE, we analyzed the two most common VTE-related adverse events: embolism venous and venous thrombosis limb. A total of 225 predicted targets of upadacitinib were identified, and their intersection with VTE-related genes from the GeneCards database was used to define the key toxicity-related targets. The PPI network was constructed using STRING and visualized in Cytoscape, revealing the following key targets: STAT3, NFKB1, GSK3B, HIF1A, HSP90AB1, and CCND1. 3.5 GO and KEGG Enrichment Analysis of Upadacitinib-Related VTE Toxicity Targets To explore the biological mechanisms of the toxic targets of upadacitinib-induced VTE, GO and KEGG enrichment analyses were performed using the DAVID database. GO enrichment revealed significant involvement of these targets in key biological processes, cellular components, and molecular functions, including oxidative stress response, regulation of lipid metabolism, membrane integrity, and nuclear receptor activity (Fig. 5 ) KEGG pathway analysis identified 138 significantly enriched pathways (P < 0.05), many of which are associated with hypoxic stress, inflammation, and endothelial dysfunction. Notably enriched pathways included Human cytomegalovirus infection, HIF-1 signaling, Ras signaling, Neurotrophin signaling, and PI3K-Akt signaling. (Fig. 5 ) 3.6 Construction of Pathway-Target Network and Identification of Key Toxicity Targets To clarify the distribution of candidate targets across relevant pathways, a VTE pathway–target network was constructed. Figure 6 A displays the overlap of multiple key targets among GO terms, while Fig. 6 B highlights their aggregation within thrombosis-associated KEGG pathways, including the PI3K-Akt and HIF-1 signaling pathways. Topological analysis identified six targets with a degree value of 9: STAT3, NFKB1, GSK3B, HIF1A, HSP90AB1, and CCND1. These targets are associated with key KEGG pathways and may serve as crucial toxicological targets of upadacitinib. (Fig. 7 ) 3.7 Molecular Docking of Upadacitinib with Key Toxicity Targets To assess the interaction between upadacitinib and key toxicological targets, molecular docking was performed to analyze the affinity of upadacitinib for these targets. The binding energies of upadacitinib with the target proteins were as follows: STAT3 (-6.71 kcal/mol), NFKB1 (-6.101 kcal/mol), GSK3B (-8.244 kcal/mol), HIF1A (-6.949 kcal/mol), HSP90AB1 (-7.216 kcal/mol), and CCND1 (-6.789 kcal/mol). The docking results for the key targets GSK3B and HSP90AB1, which exhibited the most significant binding activity, were visualized in 3D and 2D using CB-Dock2 and Discovery Studio. As shown in Fig. 8 , upadacitinib forms stable interactions with the active site of GSK3B through multiple hydrogen bonds and hydrophobic contacts. Key interacting residues include GLU12, LYS94, and ARG96. In Fig. 9 , the binding conformation of HSP90AB1 reveals interactions within the ATP-binding pocket, supported by stable hydrogen bonds and electrostatic interactions involving ASP311, ARG337, and PRO336. 4 Discussion 4.1Analysis of ADE Signals JAK inhibitors, particularly upadacitinib, are increasingly used in the treatment of various inflammatory diseases 10 . While clinical trials have demonstrated favorable short-term safety profiles for biologics, their limited duration (≤ 12 months) and stringent exclusion criteria often preclude the detection of rare or delayed adverse events such as VTE. Post-marketing surveillance using large-scale datasets is therefore essential to assess long-term risks 11 . In this study, 712 VTE cases associated with upadacitinib were identified in the FAERS database. Females accounted for 58.1% of these cases, with the highest prevalence (32.4%) observed in women aged 45–65 years. This pattern may be attributed to a combination of hormonal factors and the underlying thrombotic risk associated with upadacitinib. During the perimenopausal and postmenopausal periods, levels of estrone (E1) and estriol (E3) increase, promoting excessive fibrin clot formation and impairing fibrinolytic activity, thereby elevating the risk of VTE 12 . Furthermore, the use of oral contraceptives and hormone replacement therapy (HRT) has been independently linked to increased VTE incidence 13 . Roach et al 14 reported a sixfold increased VTE risk among women over 50 using oral contraceptives, and a fourfold increase with oral HRT use. In addition to hormonal influences, the higher prevalence of autoimmune diseases in women may contribute to the observed sex disparity. Over 85% of individuals with multiple autoimmune conditions are female. The incidence of systemic lupus erythematosus (SLE) is 7–9 times higher in women than in men, while rheumatoid arthritis (RA) is 2–3 times more prevalent in women 15 – 17 . Previous studies have reported mixed findings regarding the association between upadacitinib and VTE. Yates et al 18 noted that while individual studies suggested a potential link between JAK inhibitors and VTE, a meta-analysis did not find a statistically significant association. Other studies 19 , 20 identified a possible correlation between upadacitinib and VTE but observed no significant difference in incidence when compared with adalimumab or methotrexate. Importantly, VTE risk appears to be higher in patients with pre-existing cardiovascular conditions or a history of thromboembolism. Yang et al 21 reported that upadacitinib at a dosage of 30 mg once daily showed a trend toward increased VTE risk compared to placebo. Additionally, patients who developed VTE during treatment tended to have more active disease at the time of the event, compared to those in clinical remission while receiving upadacitinib 15 mg or 30 mg.There are divergent views regarding the role of upadacitinib in inducing VTE, which may stem from several contributing factors. First, the FAERS database encompasses a large and diverse population with long-term pharmacovigilance, increasing the likelihood of capturing adverse events in high-risk individuals. Second, VTE is a chronic and often latent pathological condition, making it difficult to detect in short-term clinical trials. Third, it is important to emphasize that a positive signal in the FAERS database indicates a potential association, not a confirmed causal relationship. Signal detection merely suggests that the observed event warrants further investigation. Notably, this study found that 98.5% of upadacitinib-associated VTE cases were classified as serious, with high rates of hospitalization and life-threatening outcomes, underscoring the clinical severity of these events. Therefore, it is imperative that clinicians closely monitor patients with known cardiovascular risk factors—such as age over 65, chronic smoking, prolonged anticoagulant use, or a prior history of VTE—to minimize the likelihood of serious adverse reactions 22 . 4.2 Toxicological Mechanism Analysis of Upadacitinib-Induced VTE This study further investigated the potential toxicological mechanisms underlying upadacitinib-induced VTE. PPI network analysis identified several key targets implicated in VTE pathogenesis, including STAT3, NFKB1, GSK3B, HIF1A, HSP90AB1, and CCND1. These targets are involved in multiple critical signaling pathways, notably the Human Cytomegalovirus (HCMV) infection, HIF-1 signaling, Ras signaling, Neurotrophin signaling, and PI3K-Akt signaling pathways. Moreover, HCMV has been associated with acute VTE, ACS, and other thrombotic events 23 . The upregulation of HIF1A was induced by HCMV infection, which is regulated by the PI3K/Akt signaling pathway 24 , 25 . The expression of the lac Z gene, a reporter for HIF1A, is driven by the minimal CMV immediate-early promoter 26 . Notably, HIF1A enhances the expression of coagulation factors, such as TF, and integrins on the surface of endothelial cells, including αVβ3 and αVβ5, thereby promoting thrombus formation. Concurrently, HIF1A inhibits the conversion of plasminogen to plasmin, which contributes to thrombus formation 27 – 30 . Additionally, after thrombus formation, reduced local blood flow exacerbates hypoxia, which re-activates the HIF pathway and creates a vicious cycle 31 . STAT3 also plays a crucial role in stabilizing HIF-1α, which in turn induces VTE through the regulation of VEGF 32 . Inhibition of STAT3 suppresses both HIF-1 and VEGFexpression 33 . Elevated VEGF levels increase vascular permeability, contributing to endothelial leakage, reduced blood flow velocity, and subsequent thrombusformation 34 , 35 . The activity of reactive oxygen species (ROS) generated by non-phagocytic NAD(P)H oxidase is regulated by the Ras signaling pathway via angiotensin II, which affects nitric oxide (NO) bioavialability 36 . Persistent activation of Ras in endothelial cells can cause vascular remodeling, a process modulated through the PI3K signaling pathway 37 . Additionally, HIF1A is stabilized by the PI3K/Akt pathway, promoting the expression of angiogenic factors such as VEGF 38 , 39 . The eNOS–PI3K/Akt–HIF-1α–VEGF signaling axis is further regulated by HSP90AB1, which enhances eNOS phosphorylation and NO production while supporting HIF-1α stability and VEGF secretion 40 , 41 . Molecular docking results from this study indicate that upadacitinib binds to the ATP-binding domain of HSP90AB1, potentially inhibiting its ATPase activity. This disruption may impair HSP90AB1’s chaperone functions, which are essential for stabilizing and activating the eNOS, PI3K/Akt, and HIF-1α complexes, thereby inducing VTE toxicity. NO, a crucial signaling molecule in the vascular system, is primarily produced by eNOS (endothelial nitric oxide synthase) 42 . Impaired NO bioavailability is associated with endothelial and platelet dysfunction 43 . NO suppresses the exocytosis of Weibel-Palade bodies, which are involved in vascular inflammation and thrombosis 44 . Stagliano et al 45 demonstrated that the NOS inhibitor L-NAME enhances platelet deposition and promotes thrombosis, while Freedman et al 46 reported increased platelet aggregation in eNOS-deficient mice. The neurotrophin signaling pathway comprising molecules such as brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF), interacts with high-affinity receptors (TrkA/B/C) and the low-affinity receptor p75NTR. These receptors are expressed not only in the nervous system but also in endothelial cells, platelets, and immune cells 47 , 48 . In inflammatory environments, neurotrophin signaling regulates endothelial barrier function, platelet activation, and pro-inflammatory cytokine release through pathways including PI3K-Akt, MAPK, PLCγ, and NF-κB 49 . Studies suggest that low BDNF levels may alter fibrin fiber formation. Upon binding to the truncated TrkB receptor (TrkB-T1), BDNF activates the Rac1–PKC–PI3K/Akt–STAT3 pathway, leading to platelet aggregation 50 , 51 . NFKB1, a central component of the NF-κB signaling pathway, plays a critical role in platelet activation. Upon activation, it promotes the release of pro-thrombotic granules such as P-selectin, sCD40L, and IL-1β, facilitating platelet aggregation 52 . Antiplatelet agents, including aspirin and ticagrelor, have been shown to suppress platelet reactivity by inhibiting the NF-κB pathway 53 . GSK3β, a known negative regulator of platelet function, inhibits platelet activation through Akt-dependent phosphorylation 54 , 55 . Molecular docking results from this study indicate that upadacitinib binds stably within the active pocket of GSK3B via hydrogen bonding and hydrophobic interactions. This interaction may suppress GSK3B’s inhibitory effect on platelets, thereby enhancing platelet activation and aggregation 56 . Such enhanced platelet reactivity may represent a key mechanism by which upadacitinib contributes to VTE toxicity. Additionally, Cyclin D1 promotes vascular smooth muscle cell proliferation and vascular remodeling by forming a complex with CDK4/6, further increasing the risk of thrombosis 57 , 58 . Collectively, these findings suggest that upadacitinib may induce VTE through the combined effects of multiple signaling pathways and molecular targets. This study has several limitations. First, the FAERS database, as a spontaneous reporting system, is subject to inherent biases, including underreporting, duplicate records, incomplete data, and potential reporting errors, which may affect result accuracy. Second, FAERS-based pharmacovigilance studies are descriptive and cannot establish a causal relationship between upadacitinib ADEs. Although the signals detected may indicate a potential association, confounding factors and the absence of patient-level baseline information prevent definitive risk assessment. Lastly, target prediction in network toxicology relies heavily on algorithm-driven databases, which lack experimental validation. Therefore, the regulatory effects of upadacitinib on the predicted targets remain uncertain and warrant further empirical investigation. 5 Conclusion This study identified a potential association between upadacitinib and VTE using pharmacovigilance analysis, underscoring the importance of thorough VTE risk assessment before initiating treatment. Furthermore, the underlying mechanisms of upadacitinib-induced VTE were preliminarily investigated through network toxicology and molecular docking, revealing key targets and pathways involved in thrombogenesis. These findings contribute to the growing body of evidence supporting post-marketing drug safety surveillance and highlight the need for continued evaluation of under-researched medications. Declarations Conflicts of Interest: The authors declare no conflicts of interest. Funding: This research was funded by the Joint Innovation Fund between the Chengdu Municipal Health and Wellness Commission and Chengdu University of Traditional Chinese Medicine, China, grant numbers: WXLH202402020; National Natural Science Foundation of China, grant numbers: 82274529; Hospital capability enhancement project of Hospital of Chengdu University of Traditional Chinese Medicine, grant numbers: 20-B05. Author Contribution Conceptualization, X.L. and G.L.; methodology, X.L.; software, G.L.; writing—review and editing, X.L.; supervision, M.C.; All authors have read and agreed to the published version of the manuscript. Acknowledgement We acknowledge the use of pathway information from KEGG: Kyoto Encyclopedia of Genes and Genomes (www.kegg.jp/kegg/kegg1.html), developed by Kanehisa Laboratories. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. References Honap, S. et al. 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Supplementary Files TableS1.docx TableS2.docx TableS4.docx TableS3.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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16:05:58","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1416758,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/86480c843edb68c106be3116.png"},{"id":93253037,"identity":"031ccc9d-0508-4081-a5c5-daad6ceb532e","added_by":"auto","created_at":"2025-10-10 16:05:58","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":168069,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/7a68e37744c690f908e07695.png"},{"id":93253033,"identity":"e8874fb5-c38c-423f-88b7-2cb7df71026f","added_by":"auto","created_at":"2025-10-10 16:05:57","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190214,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/469860038a612c648a7f9d22.png"},{"id":93253840,"identity":"5fe5d100-78f4-4eac-89a3-1198889f70a1","added_by":"auto","created_at":"2025-10-10 16:13:58","extension":"xml","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121604,"visible":true,"origin":"","legend":"","description":"","filename":"d9231f84b0c54eb29b9d0dab2a2c7ca81structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/5dc9b6dc284ec6da30721c5c.xml"},{"id":93253041,"identity":"ddd48c3e-40ee-495d-b59a-aaa6168b7dff","added_by":"auto","created_at":"2025-10-10 16:05:58","extension":"html","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134128,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/2c71169f8761c84a049d5d20.html"},{"id":93252999,"identity":"298fbefe-a491-4f79-af26-e53fc58bb556","added_by":"auto","created_at":"2025-10-10 16:05:57","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":535823,"visible":true,"origin":"","legend":"\u003cp\u003ePositive ADE signals of Upadacitinib using EBGM05, IC025, PRR, and ROR methods at the PT level.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/c9b0138663162dce98395346.jpg"},{"id":93253824,"identity":"8b40b06a-d45e-4c17-8c87-4a73fd5aa0fb","added_by":"auto","created_at":"2025-10-10 16:13:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":228021,"visible":true,"origin":"","legend":"\u003cp\u003eTime to event onset distribution for Upadacitinib-related VTE.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/4e8da7191490da6fa667967c.jpg"},{"id":93253006,"identity":"cf8ae6d3-d6a0-47dd-85a9-c17bf0d843d6","added_by":"auto","created_at":"2025-10-10 16:05:57","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":394097,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative time to event onset for positive signals of PT.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/6ed3e24f67978e9db3eb1c48.jpg"},{"id":93253827,"identity":"6bf3bf0b-e3fb-4a83-a89e-67273b954128","added_by":"auto","created_at":"2025-10-10 16:13:57","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3079215,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network map of common targets of Upadacitinib and VTE\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/10b71fdf94a2c32833e5bd20.jpg"},{"id":93253002,"identity":"285f16e6-78f5-437a-a127-2c21c28deff5","added_by":"auto","created_at":"2025-10-10 16:05:57","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":453276,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG enrichment analysis of biological processes (A), cellular components (B), molecular functions (C), and pathways (D) associated with Upadacitinib-induced VTE.(The pathway was redrawn based on information from KEGG: Kyoto Encyclopedia of Genes and Genomes (www.kegg.jp/kegg/kegg1.html). © Kanehisa Laboratories.)\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/86b19d37e44c08a1ffd4b767.jpg"},{"id":93253011,"identity":"78ff8a6c-8e7a-401c-bc66-22914305d627","added_by":"auto","created_at":"2025-10-10 16:05:57","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1905342,"visible":true,"origin":"","legend":"\u003cp\u003eChord diagram showing the association between key targets and related pathways in Upadacitinib-induced VTE. (A) Intersection of key genes across multiple GO terms. (B) Clustering of targets within KEGG pathways.( The pathway was redrawn based on information from KEGG: Kyoto Encyclopedia of Genes and Genomes (www.kegg.jp/kegg/kegg1.html). © Kanehisa Laboratories.)\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/d9172acc1ee69c12f6ab8f08.jpg"},{"id":93253016,"identity":"cf8b80f4-c34c-4130-bb99-f8a1dac9fcdf","added_by":"auto","created_at":"2025-10-10 16:05:57","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4968102,"visible":true,"origin":"","legend":"\u003cp\u003eKey targets associated with Upadacitinib-induced VTE\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/b9261be4d7a15ea9b1ab9487.jpg"},{"id":93253018,"identity":"0245272a-c745-419e-a266-8109930c08ab","added_by":"auto","created_at":"2025-10-10 16:05:57","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":431532,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of molecular docking patterns between Upadacitinib and GSK3B. (A) 3D structure of GSK3B with Upadacitinib binding site. (B) Molecular docking interaction of Upadacitinib with GSK3B showing hydrogen bond and hydrophobic interactions. (C) Detailed view of the binding mode of Upadacitinib with GSK3B showing specific interacting residues.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/fa239b2e941f612568f62a74.jpg"},{"id":93253009,"identity":"4ab14450-7248-4d60-adec-8e61c0d89273","added_by":"auto","created_at":"2025-10-10 16:05:57","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":496415,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of molecular docking patterns between Upadacitinib and HSP90AB1. (A) 3D structure of HSP90AB1 with Upadacitinib binding site. (B) Molecular docking interaction of Upadacitinib with HSP90AB1 showing hydrogen bond and hydrophobic interactions. (C) Detailed view of the binding mode of Upadacitinib with HSP90AB1 showing specific interacting residues.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/b71e8905b9a4de9f73a6d253.jpg"},{"id":106749304,"identity":"440bcb95-0077-47cc-a482-6c833b738e63","added_by":"auto","created_at":"2026-04-13 06:28:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13355237,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/8fcaab73-bc2d-4e09-8221-167b912a4138.pdf"},{"id":93252997,"identity":"552127fd-e4d0-4e3c-a52b-7b19c083fb31","added_by":"auto","created_at":"2025-10-10 16:05:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20218,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/6f497fe95b72b868922dad9d.docx"},{"id":93253004,"identity":"b517cef7-53b9-46a4-b668-c26fe4c75742","added_by":"auto","created_at":"2025-10-10 16:05:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27721,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/76041b8d77b26c332ece4ada.docx"},{"id":93253000,"identity":"120620a3-7b8d-405f-bff6-7b60ca5f1d26","added_by":"auto","created_at":"2025-10-10 16:05:57","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26825,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/a82c72c104687b64acac7e23.docx"},{"id":93253007,"identity":"7ff7f2ae-f8a7-4d88-969d-6dc25c4c9b9a","added_by":"auto","created_at":"2025-10-10 16:05:57","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18164,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7223810/v1/e8a0c09b00309a2c8ac938dd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Association Between Upadacitinib and Venous Thromboembolism: An Integrated Analysis of Adverse Drug Events and Network Toxicology Mechanisms","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eUpadacitinib is a selective Janus kinase 1 (JAK1) inhibitor used to treat immune-mediated inflammatory disorders, including rheumatoid arthritis (RA), psoriatic arthritis (PsA), atopic dermatitis, axial spondyloarthritis, moderate to severe ulcerative colitis (UC) and Crohn\u0026rsquo;s disease (CD)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Compared to other biologic therapies, JAK inhibitors such as upadacitinib offer advantages including oral administration and rapid onset of action, and have shown high efficacy in inducing and maintaining remission in UC and CD \u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, concerns have emerged regarding a possible thrombotic risk associated with upadacitinib, prompted by an increasing number of reports of venous thromboembolism (VTE) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. VTE, primarily presenting as deep vein thrombosis (DVT), is a serious vascular complication linked to significant morbidity and mortality\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Although case reports suggest a potential link between JAK inhibitors and VTE, there remains a lack of comprehensive investigation into the adverse event profiles and toxicological mechanisms underlying upadacitinib-induced VTE. To address this gap, the present study examines the association between upadacitinib and VTE by integrating data from adverse drug events (ADEs) reports with network toxicology analysis. This integrative approach aims to elucidate potential mechanistic pathways and toxicological targets, providing a theoretical foundation for improved safety assessment and supporting the application of precision medicine in upadacitinib therapy.\u003c/p\u003e"},{"header":"2 Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data source\u003c/h2\u003e\u003cp\u003eThe FAERS database was used as the data source between January 2004 and March 2025. FAERS is an open database maintained by the FDA that collects ADE reports submitted by healthcare professionals, consumers, and manufacturers from across the world.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data cleaning\u003c/h2\u003e\u003cp\u003eBefore analysis, duplicate data with identical gender, age, reporting country, adverse events, drug names, and start and end dates were removed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data processing\u003c/h2\u003e\u003cp\u003eUpadacitinib was selected as the study object. The generic name, brand name, and abbreviation of the drug from the FDA, EMA, and relevant clinical guidelines were used as search keywords to identify the relevant ADE reports. Only ADE reports wherein upadacitinib was considered the primary suspect were included. The preferred terms for VTE adverse events were established using MedDRA (version 25.0) and relevant literature, as detailed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Disproportionality analysis\u003c/h2\u003e\u003cp\u003eFour algorithms were used in the pharmacovigilance research: ROR, PRR, BCPNN, and MGPS, to identify signals related to adverse drug reactions (detailed formulas and positive signal criteria are provided in Supplementary Table\u0026nbsp;2). The algorithm values reflect the strength of the association between the drug and corresponding ADE signals \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. A signal was considered positive only when it met the criteria of two algorithms simultaneously. All statistical analyses were conducted using R software (version 4.1.2) and Excel 2019.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Network toxicological analysis\u003c/h2\u003e\u003cp\u003eThe predicted targets of upadacitinib were obtained from the TOXRIC, STITCH, and SwissTargetPrediction databases, whereas VTE-related targets were collected from GeneCards. The intersection of these two sets of targets gave the potential toxicity targets. A protein\u0026ndash;protein interaction (PPI) network was constructed using STRING and Cytoscape to identify the key targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the DAVID database, and the results were selected based on p-values and corrected p-values (q-values), both less than 0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Molecular docking\u003c/h2\u003e\u003cp\u003eMolecular docking was performed to analyze the binding modes and affinities between upadacitinib and the key target proteins. CB-Dock2 was used to preprocess the small-molecule ligands and the key target proteins. The docking site with the lowest Vina score was selected as the optimal binding mode. The binding energies were calculated, and the binding mode was visualized in 3D. The 2D docking images were generated using Discovery Studio to illustrate the key interactions such as hydrogen bonds and hydrophobic interactions.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 General characteristics\u003c/h2\u003e\u003cp\u003eBetween January 2004 and March 2025, a total of 191,310 adverse event reports related to JAK inhibitors were identified in the FAERS database, including 2,227 cases of VTE, of which 712 were associated with upadacitinib. The majority of these upadacitinib-related VTE cases occurred in females (414 cases, 58.1%), while males accounted for 245 cases (34.4%). The highest age-specific incidence was observed in the 18\u0026ndash;65 age group (40.0%), followed by those aged\u0026thinsp;\u0026ge;\u0026thinsp;85 years (21.9%). Among reported outcomes, hospitalization occurred in 45.5% of cases, life-threatening events in 5.9%, and death in 4.2%. Overall, 98.5% of the events were classified as serious. Reporting trends showed a steady increase over time, peaking in 2021 with 245 reports (34.4%). Most reports originated from consumers (48.3%) and physicians (35.0%), with the United States (60.5%) and Germany (6.2%) being the leading countries of origin (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Disproportionality analysis\u003c/h2\u003e\u003cp\u003eThe correlation and signal values of upadacitinib with VTE-related ADEs are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;2. Upadacitinib was associated with positive signals for VTE (ROR(95% CI): 1.65 (1.54\u0026ndash;1.77), IC(IC025): 0.72 (0.61)). The VTE-related adverse events that exhibited positive signals include: Pulmonary embolism (ROR(95% CI): 1.36 (1.22\u0026ndash;1.51), IC(IC025): 0.44 (0.28)), Pulmonary thrombosis (ROR(95% CI): 7.94 (6.92\u0026ndash;9.12), PRR: 7.93 (1228.84), EBGM(EBGM05): 7.79 (6.94), IC(IC025): 2.96 (2.76)), Venous Occlusion (ROR(95% CI): 2.63 (1.49\u0026ndash;4.65), PRR: 2.63 (12.08), IC(IC025): 1.39 (0.59)), Portal Vein Thrombosis (ROR(95% CI): 1.94 (1.17\u0026ndash;3.23), IC(IC025): 0.96 (0.23)), Venous Thrombosis Limb (ROR(95% CI): 4.33 (2.89\u0026ndash;6.47), PRR: 4.33 (60.66), EBGM(EBGM05): 4.29 (3.06), IC(IC025): 2.1 (1.52)), Retinal Vein Occlusion (ROR(95% CI): 3.54 (2.37\u0026ndash;5.29), PRR: 3.54 (43.35), EBGM(EBGM05): 3.52 (2.51), IC(IC025): 1.81 (1.23)), Thrombosed Varicose Vein (EBGM(EBGM05): 14.49 (4.44), IC(IC025): 3.86 (2.14)), and Portal Vein Embolism (EBGM(EBGM05): 17.13 (3.2), IC(IC025): 4.1 (1.97)).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 The time of adverse reaction onset\u003c/h2\u003e\u003cp\u003eBased on the START_DT and EVENT_DT data fields, the median time to VTE onset was 122 days (interquartile range: 40\u0026ndash;399.75 days). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, adverse events occurred most frequently after 360 days of treatment, accounting for approximately 27.8% of the total. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the cumulative incidence of VTE-related adverse events that demonstrated significant signals.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 PPI Network Analysis of Upadacitinib-Related VTE Toxicity Targets\u003c/h2\u003e\u003cp\u003eTo investigate the toxicological mechanisms of upadacitinib-related VTE, we analyzed the two most common VTE-related adverse events: embolism venous and venous thrombosis limb. A total of 225 predicted targets of upadacitinib were identified, and their intersection with VTE-related genes from the GeneCards database was used to define the key toxicity-related targets. The PPI network was constructed using STRING and visualized in Cytoscape, revealing the following key targets: STAT3, NFKB1, GSK3B, HIF1A, HSP90AB1, and CCND1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 GO and KEGG Enrichment Analysis of Upadacitinib-Related VTE Toxicity Targets\u003c/h2\u003e\u003cp\u003eTo explore the biological mechanisms of the toxic targets of upadacitinib-induced VTE, GO and KEGG enrichment analyses were performed using the DAVID database. GO enrichment revealed significant involvement of these targets in key biological processes, cellular components, and molecular functions, including oxidative stress response, regulation of lipid metabolism, membrane integrity, and nuclear receptor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eKEGG pathway analysis identified 138 significantly enriched pathways (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), many of which are associated with hypoxic stress, inflammation, and endothelial dysfunction. Notably enriched pathways included Human cytomegalovirus infection, HIF-1 signaling, Ras signaling, Neurotrophin signaling, and PI3K-Akt signaling. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Construction of Pathway-Target Network and Identification of Key Toxicity Targets\u003c/h2\u003e\u003cp\u003eTo clarify the distribution of candidate targets across relevant pathways, a VTE pathway\u0026ndash;target network was constructed. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA displays the overlap of multiple key targets among GO terms, while Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB highlights their aggregation within thrombosis-associated KEGG pathways, including the PI3K-Akt and HIF-1 signaling pathways. Topological analysis identified six targets with a degree value of 9: STAT3, NFKB1, GSK3B, HIF1A, HSP90AB1, and CCND1. These targets are associated with key KEGG pathways and may serve as crucial toxicological targets of upadacitinib. (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Molecular Docking of Upadacitinib with Key Toxicity Targets\u003c/h2\u003e\u003cp\u003eTo assess the interaction between upadacitinib and key toxicological targets, molecular docking was performed to analyze the affinity of upadacitinib for these targets. The binding energies of upadacitinib with the target proteins were as follows: STAT3 (-6.71 kcal/mol), NFKB1 (-6.101 kcal/mol), GSK3B (-8.244 kcal/mol), HIF1A (-6.949 kcal/mol), HSP90AB1 (-7.216 kcal/mol), and CCND1 (-6.789 kcal/mol). The docking results for the key targets GSK3B and HSP90AB1, which exhibited the most significant binding activity, were visualized in 3D and 2D using CB-Dock2 and Discovery Studio.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, upadacitinib forms stable interactions with the active site of GSK3B through multiple hydrogen bonds and hydrophobic contacts. Key interacting residues include GLU12, LYS94, and ARG96. In Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the binding conformation of HSP90AB1 reveals interactions within the ATP-binding pocket, supported by stable hydrogen bonds and electrostatic interactions involving ASP311, ARG337, and PRO336.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.1Analysis of ADE Signals\u003c/h2\u003e\u003cp\u003eJAK inhibitors, particularly upadacitinib, are increasingly used in the treatment of various inflammatory diseases\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. While clinical trials have demonstrated favorable short-term safety profiles for biologics, their limited duration (\u0026le;\u0026thinsp;12 months) and stringent exclusion criteria often preclude the detection of rare or delayed adverse events such as VTE. Post-marketing surveillance using large-scale datasets is therefore essential to assess long-term risks\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In this study, 712 VTE cases associated with upadacitinib were identified in the FAERS database. Females accounted for 58.1% of these cases, with the highest prevalence (32.4%) observed in women aged 45\u0026ndash;65 years. This pattern may be attributed to a combination of hormonal factors and the underlying thrombotic risk associated with upadacitinib.\u003c/p\u003e\u003cp\u003eDuring the perimenopausal and postmenopausal periods, levels of estrone (E1) and estriol (E3) increase, promoting excessive fibrin clot formation and impairing fibrinolytic activity, thereby elevating the risk of VTE\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Furthermore, the use of oral contraceptives and hormone replacement therapy (HRT) has been independently linked to increased VTE incidence\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Roach et al\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e reported a sixfold increased VTE risk among women over 50 using oral contraceptives, and a fourfold increase with oral HRT use. In addition to hormonal influences, the higher prevalence of autoimmune diseases in women may contribute to the observed sex disparity. Over 85% of individuals with multiple autoimmune conditions are female. The incidence of systemic lupus erythematosus (SLE) is 7\u0026ndash;9 times higher in women than in men, while rheumatoid arthritis (RA) is 2\u0026ndash;3 times more prevalent in women\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePrevious studies have reported mixed findings regarding the association between upadacitinib and VTE. Yates et al\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e noted that while individual studies suggested a potential link between JAK inhibitors and VTE, a meta-analysis did not find a statistically significant association. Other studies\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e identified a possible correlation between upadacitinib and VTE but observed no significant difference in incidence when compared with adalimumab or methotrexate. Importantly, VTE risk appears to be higher in patients with pre-existing cardiovascular conditions or a history of thromboembolism. Yang et al\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e reported that upadacitinib at a dosage of 30 mg once daily showed a trend toward increased VTE risk compared to placebo. Additionally, patients who developed VTE during treatment tended to have more active disease at the time of the event, compared to those in clinical remission while receiving upadacitinib 15 mg or 30 mg.There are divergent views regarding the role of upadacitinib in inducing VTE, which may stem from several contributing factors. First, the FAERS database encompasses a large and diverse population with long-term pharmacovigilance, increasing the likelihood of capturing adverse events in high-risk individuals. Second, VTE is a chronic and often latent pathological condition, making it difficult to detect in short-term clinical trials. Third, it is important to emphasize that a positive signal in the FAERS database indicates a potential association, not a confirmed causal relationship. Signal detection merely suggests that the observed event warrants further investigation. Notably, this study found that 98.5% of upadacitinib-associated VTE cases were classified as serious, with high rates of hospitalization and life-threatening outcomes, underscoring the clinical severity of these events. Therefore, it is imperative that clinicians closely monitor patients with known cardiovascular risk factors\u0026mdash;such as age over 65, chronic smoking, prolonged anticoagulant use, or a prior history of VTE\u0026mdash;to minimize the likelihood of serious adverse reactions\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Toxicological Mechanism Analysis of Upadacitinib-Induced VTE\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study further investigated the potential toxicological mechanisms underlying upadacitinib-induced VTE. PPI network analysis identified several key targets implicated in VTE pathogenesis, including STAT3, NFKB1, GSK3B, HIF1A, HSP90AB1, and CCND1. These targets are involved in multiple critical signaling pathways, notably the Human Cytomegalovirus (HCMV) infection, HIF-1 signaling, Ras signaling, Neurotrophin signaling, and PI3K-Akt signaling pathways. Moreover, HCMV has been associated with acute VTE, ACS, and other thrombotic events\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The upregulation of HIF1A was induced by HCMV infection, which is regulated by the PI3K/Akt signaling pathway\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The expression of the lac Z gene, a reporter for HIF1A, is driven by the minimal CMV immediate-early promoter\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Notably, HIF1A enhances the expression of coagulation factors, such as TF, and integrins on the surface of endothelial cells, including αVβ3 and αVβ5, thereby promoting thrombus formation. Concurrently, HIF1A inhibits the conversion of plasminogen to plasmin, which contributes to thrombus formation\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Additionally, after thrombus formation, reduced local blood flow exacerbates hypoxia, which re-activates the HIF pathway and creates a vicious cycle\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. STAT3 also plays a crucial role in stabilizing HIF-1α, which in turn induces VTE through the regulation of VEGF\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Inhibition of STAT3 suppresses both HIF-1 and VEGFexpression\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Elevated VEGF levels increase vascular permeability, contributing to endothelial leakage, reduced blood flow velocity, and subsequent thrombusformation\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe activity of reactive oxygen species (ROS) generated by non-phagocytic NAD(P)H oxidase is regulated by the Ras signaling pathway via angiotensin II, which affects nitric oxide (NO) bioavialability\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Persistent activation of Ras in endothelial cells can cause vascular remodeling, a process modulated through the PI3K signaling pathway\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Additionally, HIF1A is stabilized by the PI3K/Akt pathway, promoting the expression of angiogenic factors such as VEGF\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The eNOS\u0026ndash;PI3K/Akt\u0026ndash;HIF-1α\u0026ndash;VEGF signaling axis is further regulated by HSP90AB1, which enhances eNOS phosphorylation and NO production while supporting HIF-1α stability and VEGF secretion\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Molecular docking results from this study indicate that upadacitinib binds to the ATP-binding domain of HSP90AB1, potentially inhibiting its ATPase activity. This disruption may impair HSP90AB1\u0026rsquo;s chaperone functions, which are essential for stabilizing and activating the eNOS, PI3K/Akt, and HIF-1α complexes, thereby inducing VTE toxicity. NO, a crucial signaling molecule in the vascular system, is primarily produced by eNOS (endothelial nitric oxide synthase) \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Impaired NO bioavailability is associated with endothelial and platelet dysfunction\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. NO suppresses the exocytosis of Weibel-Palade bodies, which are involved in vascular inflammation and thrombosis\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Stagliano et al\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e demonstrated that the NOS inhibitor L-NAME enhances platelet deposition and promotes thrombosis, while Freedman et al\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e reported increased platelet aggregation in eNOS-deficient mice.\u003c/p\u003e\u003cp\u003eThe neurotrophin signaling pathway comprising molecules such as brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF), interacts with high-affinity receptors (TrkA/B/C) and the low-affinity receptor p75NTR. These receptors are expressed not only in the nervous system but also in endothelial cells, platelets, and immune cells \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In inflammatory environments, neurotrophin signaling regulates endothelial barrier function, platelet activation, and pro-inflammatory cytokine release through pathways including PI3K-Akt, MAPK, PLCγ, and NF-κB\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Studies suggest that low BDNF levels may alter fibrin fiber formation. Upon binding to the truncated TrkB receptor (TrkB-T1), BDNF activates the Rac1\u0026ndash;PKC\u0026ndash;PI3K/Akt\u0026ndash;STAT3 pathway, leading to platelet aggregation\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNFKB1, a central component of the NF-κB signaling pathway, plays a critical role in platelet activation. Upon activation, it promotes the release of pro-thrombotic granules such as P-selectin, sCD40L, and IL-1β, facilitating platelet aggregation\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Antiplatelet agents, including aspirin and ticagrelor, have been shown to suppress platelet reactivity by inhibiting the NF-κB pathway\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. GSK3β, a known negative regulator of platelet function, inhibits platelet activation through Akt-dependent phosphorylation\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Molecular docking results from this study indicate that upadacitinib binds stably within the active pocket of GSK3B via hydrogen bonding and hydrophobic interactions. This interaction may suppress GSK3B\u0026rsquo;s inhibitory effect on platelets, thereby enhancing platelet activation and aggregation\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Such enhanced platelet reactivity may represent a key mechanism by which upadacitinib contributes to VTE toxicity. Additionally, Cyclin D1 promotes vascular smooth muscle cell proliferation and vascular remodeling by forming a complex with CDK4/6, further increasing the risk of thrombosis\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Collectively, these findings suggest that upadacitinib may induce VTE through the combined effects of multiple signaling pathways and molecular targets.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the FAERS database, as a spontaneous reporting system, is subject to inherent biases, including underreporting, duplicate records, incomplete data, and potential reporting errors, which may affect result accuracy. Second, FAERS-based pharmacovigilance studies are descriptive and cannot establish a causal relationship between upadacitinib ADEs. Although the signals detected may indicate a potential association, confounding factors and the absence of patient-level baseline information prevent definitive risk assessment. Lastly, target prediction in network toxicology relies heavily on algorithm-driven databases, which lack experimental validation. Therefore, the regulatory effects of upadacitinib on the predicted targets remain uncertain and warrant further empirical investigation.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study identified a potential association between upadacitinib and VTE using pharmacovigilance analysis, underscoring the importance of thorough VTE risk assessment before initiating treatment. Furthermore, the underlying mechanisms of upadacitinib-induced VTE were preliminarily investigated through network toxicology and molecular docking, revealing key targets and pathways involved in thrombogenesis. These findings contribute to the growing body of evidence supporting post-marketing drug safety surveillance and highlight the need for continued evaluation of under-researched medications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research was funded by the Joint Innovation Fund between the Chengdu Municipal Health and Wellness Commission and Chengdu University of Traditional Chinese Medicine, China, grant numbers: WXLH202402020; National Natural Science Foundation of China, grant numbers: 82274529; Hospital capability enhancement project of Hospital of Chengdu University of Traditional Chinese Medicine, grant numbers: 20-B05.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, X.L. and G.L.; methodology, X.L.; software, G.L.; writing\u0026mdash;review and editing, X.L.; supervision, M.C.; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge the use of pathway information from KEGG: Kyoto Encyclopedia of Genes and Genomes (www.kegg.jp/kegg/kegg1.html), developed by Kanehisa Laboratories.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHonap, S. et al. 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Chem.\u003c/em\u003e \u003cb\u003e287\u003c/b\u003e, 36291\u0026ndash;36304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M112.361220\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M112.361220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\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":"Food and Drug Administration Adverse Event Reporting System, pharmacovigilance, network toxicology, upadacitinib, inflammatory diseases","lastPublishedDoi":"10.21203/rs.3.rs-7223810/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7223810/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo investigate the association between upadacitinib and venous thromboembolism (VTE) and explore potential toxicological mechanisms by integrating adverse drug events (ADEs) data with network toxicology analyses.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective analysis of upadacitinib-related ADEs reported in the FAERS database from January 2004 to March 2025 was conducted. Signal detection methods included Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS). Potential molecular mechanisms were explored through network toxicology. Predicted targets of upadacitinib were obtained from TOXRIC, STITCH, and SwissTargetPrediction, while VTE-related targets were retrieved from GeneCards. Protein-protein interaction (PPI) networks were constructed using STRING and visualized with Cytoscape. Functional enrichment analyses (GO and KEGG) were performed using DAVID. Molecular docking was conducted via CB-Dock2.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 712 reports linked upadacitinib to VTE, showing a significant positive signal (ROR [95% CI]: 1.65 [1.54\u0026ndash;1.77]; IC025: 0.72 [0.61]). The median time to VTE onset was 122 days. Network toxicology identified six core targets: STAT3, NFKB1, GSK3B, HIF1A, HSP90AB1, and CCND1. Enriched pathways included Human Cytomegalovirus infection, HIF-1 signaling, Ras signaling, Neurotrophin signaling, and PI3K-Akt signaling. Molecular docking revealed strong binding affinities between upadacitinib and these targets.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eupadacitinib may be associated with an increased risk of VTE, mediated by multiple signaling pathways and key toxicological targets. These findings provide mechanistic insights into upadacitinib-induced VTE and support the need for enhanced pharmacovigilance in clinical settings.\u003c/p\u003e","manuscriptTitle":"Exploring the Association Between Upadacitinib and Venous Thromboembolism: An Integrated Analysis of Adverse Drug Events and Network Toxicology Mechanisms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 16:05:52","doi":"10.21203/rs.3.rs-7223810/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":"6894e649-38b3-496a-9ada-6619e945692e","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56048190,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56048191,"name":"Health sciences/Diseases"},{"id":56048192,"name":"Biological sciences/Drug discovery"},{"id":56048193,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-04-13T06:27:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 16:05:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7223810","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7223810","identity":"rs-7223810","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}