Risk factors and mechanisms of ticagrelor-related dyspnoea from real-world evidence and network pharmacology

Risk factors and mechanisms of ticagrelor-related dyspnoea from real-world evidence and network pharmacology | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 January 2026 V1 Latest version Share on Risk factors and mechanisms of ticagrelor-related dyspnoea from real-world evidence and network pharmacology Authors : Shuaimin Xu 0009-0003-2864-7041 [email protected] , Weijuan Song , Yanhong Wang , and Yang Zhao Authors Info & Affiliations https://doi.org/10.22541/au.176877929.91026940/v1 121 views 28 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Introduction: In the clinical application of ticagrelor, dyspnoea is a common adverse event and is a significant factor leading to discontinuation of the drug. However, the molecular mechanisms of ticagrelor-related dyspnoea have not been definitively identified, and consequently, the therapeutic implications remain unclear. Methods: Data from the FDA Adverse Event Reporting System (FAERS) database were used. The analysis focused on asymmetry methods for ticagrelor-related dyspnoea adverse events (ADEs), including the reporting odds ratio (ROR) and the Bayesian confidence propagation neural network (BCPNN). Logistic regression analysed the impact of age, weight, and sex. Cumulative incidence and time to onset of dyspnoea events were examined and Weibull shape parameter tests. And the mechanism of ticagrelor-related dyspnoea was predicted using network pharmacology and molecular docking. Results: The results indicated that Logistic regression analysis suggested that female (adjusted odds ratio [aOR] 0.80, 95% confidence interval [CI] 0.69–0.93, P = 0.003), higher weight (aOR 2.57, 95% CI 1.57–4.48, P < 0.001), and older age (aOR 1.01, 95% CI 1.01–1.01, P < 0.001) were independent predictors of ticagrelor-related dyspnoea. The Weibull shape parameter β was <1 for ticagrelor, indicating a higher dyspnoea risk early in treatment. Network pharmacology suggests that ticagrelor may induce dyspnoea via the biological processes of positive regulation of phosphatidylinositol 3-kinase/protein kinase B signal transduction and positive regulation of the MAPK cascade. Conclusions: This study provides valuable insights into ticagrelor-related dyspnoea, though several limitations must be considered. Nevertheless, these findings highlight the need for careful monitoring and proactive management of ticagrelor-related dyspnoea. Risk factors and mechanisms of ticagrelor-related dyspnoea from real-world evidence and network pharmacology Shuaimin Xu * , Weijuan Song, Yanhong Wang, Yang Zhao Department of Pharmacy, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China Authors: Shuaimin Xu Department of Pharmacy, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 4500052, China Email: [email protected] Weijuan Song Department of Pharmacy, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 4500052, China Email: [email protected] Yanhong Wang Department of Pharmacy, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 4500052, China Email: [email protected] Yang Zhao Department of Pharmacy, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 4500052, China Email: [email protected] * Corresponding author: Corresponding author: Shuaimin Xu Department of Pharmacy, The Fifth Affiliated Hospital of Zhengzhou University, No. 3 Kangfuqian Street, Zhengzhou 450052, China Email: [email protected] Keywords: ticagrelor, dyspnoea, real-world database, risk factors, mechanism, network pharmacology Abstract Introduction: In the clinical application of ticagrelor, dyspnoea is a common adverse event and is a significant factor leading to discontinuation of the drug. However, the molecular mechanisms of ticagrelor-related dyspnoea have not been definitively identified, and consequently, the therapeutic implications remain unclear. Methods: Data from the FDA Adverse Event Reporting System (FAERS) database were used. The analysis focused on asymmetry methods for ticagrelor-related dyspnoea adverse events (ADEs), including the reporting odds ratio (ROR) and the Bayesian confidence propagation neural network (BCPNN). Logistic regression analysed the impact of age, weight, and sex. Cumulative incidence and time to onset of dyspnoea events were examined and Weibull shape parameter tests. And the mechanism of ticagrelor-related dyspnoea was predicted using network pharmacology and molecular docking. Results: The results indicated that Logistic regression analysis suggested that female (adjusted odds ratio [aOR] 0.80, 95% confidence interval [CI] 0.69–0.93, P = 0.003), higher weight (aOR 2.57, 95% CI 1.57–4.48, P < 0.001), and older age (aOR 1.01, 95% CI 1.01–1.01, P < 0.001) were independent predictors of ticagrelor-related dyspnoea. The Weibull shape parameter β was <1 for ticagrelor, indicating a higher dyspnoea risk early in treatment. Network pharmacology suggests that ticagrelor may induce dyspnoea via the biological processes of positive regulation of phosphatidylinositol 3-kinase/protein kinase B signal transduction and positive regulation of the MAPK cascade. Conclusions: This study provides valuable insights into ticagrelor-related dyspnoea, though several limitations must be considered. Nevertheless, these findings highlight the need for careful monitoring and proactive management of ticagrelor-related dyspnoea. 1 Introduction Ticagrelor is a P2Y12 receptor antagonist widely used in clinical practice. Compared to clopidogrel, ticagrelor has a faster onset of action and less inter-individual variability in efficacy 1 . As a potent P2Y12 receptor antagonist, ticagrelor is more effective than clopidogrel in treating patients with Acute Coronary Syndrome (ACS). Several randomized controlled trials have confirmed that ticagrelor can reduce the risk of ischemic events following a myocardial infarction and improve long-term prognosis 2,3 . The Guidelines for the Management of ACS recommend ticagrelor as first-line antiplatelet therapy 4,5 . Clopidogrel is recommended as an alternative for patients who are intolerant to ticagrelor. Dyspnoea is a common adverse effect associated with ticagrelor 6 . Dyspnoea of variable severity occurs predominantly within the first week of dosing, typically occurs 2-3 hours after dosing, and may persist for hours, days, or throughout the duration of treatment. This phenomenon generally correlates with peak plasma concentrations of ticagrelor 7–9 . Studies suggest that approximately 20% of patients may experience unexplained dyspnoea following ticagrelor administration, and approximately one-third of these patients discontinue ticagrelor and switch to alternative antiplatelet therapies 10,11 . This discontinuation may increase the risk of cardiovascular adverse events 12 . Despite extensive research, the mechanism underlying ticagrelor-related dyspnoea remains inconclusive 13 . Furthermore, although the causality of ticagrelor-related dyspnoea has been established, the identification of high-risk factors requires further investigation. The FDA Adverse Event Reporting System (FAERS) is a publicly accessible web-based tool that facilitates easy access to FAERS data and serves as a user-friendly database. Network pharmacology represents an emerging interdisciplinary field. This field employs multi-omics approaches, including systems biology, genomics, proteomics, and metabolomics, in conjunction with bioinformatics and computational biology methodologies to investigate the mechanisms of drug action and interactions 14–17 . It transcends the traditional paradigm of ”one phenotype, one target, and one drug”. This approach can integrate diverse data types, construct scale-free networks, and comprehensively characterize the intricate interrelationships among research entities 18 . This capability enables researchers to conduct investigations into the molecular mechanisms underlying ADEs and to identify common mechanisms among various drugs. Additionally, it offers a more comprehensive perspective for understanding the differential effects of drug therapies and their potential causes. In this study, we used the FAERS database to conduct mining and analysis of ticagrelor-related dyspnoea reports and identified potential risk factors for ticagrelor-related dyspnoea. This provides a scientific basis for early identification and intervention of adverse drug reactions, thereby reducing patient withdrawals due to adverse effects. In addition, we explored the mechanism of ticagrelor-related dyspnoea using network pharmacology for the first time and predicted its potential molecular mechanism. 2 Materials and methods 2.1 Data Source This study utilized data from the FAERS public pharmacovigilance database to evaluate potential ticagrelor-related dyspnoea. FAERS integrates spontaneous reports submitted by healthcare professionals, consumers, and manufacturers. The database comprises seven core files: DEMO (demographic and administrative data), DRUG (drug information), REAC (adverse drug reactions), OUTC (patient outcomes), THER (therapy start and end dates), RPSR (report sources), and INDI (indications). Data were retrieved from Q1 2013 to Q3 2025. Duplicate reports were eliminated according to FDA recommendations, retaining only the most recent version based on CASEID and FDA_DT. Records flagged in quarterly deletion files (available from Q1 2019 onward) were also excluded. Age and sex were standardized, and inconsistent entries or misspellings in adverse event terms were normalized to ensure data integrity. Ticagrelor was used as the primary suspect drug during screening in this study. To minimize missed reports due to inconsistent naming, all generic, brand, and synonymous drug names were retrieved from the National Center for Biotechnology Information (NCBI) database and used to filter FAERS records in which the drugs were designated as the primary suspect (PS). The final list of drugs was cross-checked and verified by a second researcher to ensure data integrity and reliability. The Medical Dictionary for Regulatory Activities (MedDRA v28.1) provided the preferred terms for dyspnoea, which included dyspnoea (MedDRA code 10013968), exertional dyspnoea (MedDRA code 10013971), dyspnoea at rest (MedDRA code 10013969), nocturnal dyspnoea (MedDRA code 10049235), paroxysmal nocturnal dyspnoea (MedDRA code 10013974) and bendopnoea (MedDRA code 10077819). 2.2 Disproportionality analysis Disproportionality analysis was conducted to assess the potential association between ticagrelor and dyspnoea 19 . The reporting odds ratio (ROR) method and the Bayesian confidence propagation neural network (BCPNN) method were used as measures to identify the signal 20 . The statistical analysis was based on two-by-two contingency tables (Table 1). The ROR method generated a signal of disproportionate reporting when the number of reports (n) was greater than or equal to 3 and the lower bound of the 95% confidence interval (CI) exceeded 1, with higher ROR values indicating stronger signal intensity. While the IC was deemed significant if the 95% CI Lower (IC025) was above 0. To ensure robust signal detection, AEs were considered significant only when identified by both methodological approaches, with detailed mathematical formulae for both ROR and BCPNN calculations provided in Table 2. Table 1 Two-by-two contingency table for the disproportionality analyses Target events a b a+b Other events c d c+d Total a+c b+d N=a+b+c+d Table 2 Formula for the computation of disproportionality measures ROR ROR=(a/b)/(c/d) 95%CI=e ln(ROR)±1.96(1/a+1/b+1/c+1/d)^0.5 a ≥ 3 95%CI (lower limit) > 1 BCPNN IC=log 2 a(a+b+c+d)/[(a+c)(a+b)] IC025=e ln(IC)-1.96(1/a+1/b+1/c+1/d)^0.5 IC025>0 ROR, reporting odds ratio; BCPNN, Bayesian confidence propagation neural network; CI, confidence interval; IC, information component 2.3 Logistic regression analyses Univariate and multivariate logistic regression analyses were performed to explore the effects of age, weight, and sex on the incidence of dyspnoea associated with ticagrelor. Age, weight and sex were included simultaneously in the multivariate analysis. 2.4 Time-to-onset analysis The interval between the onset date of the adverse event (EVENT_DT, DEMO file) and the start date of ticagrelor treatment (START_DT, THER file) was defined as the time-to-onset (TTO). Reports with implausible or incomplete date information, such as EVENT_DT earlier than START_DT, clearly erroneous dates, or missing values, were excluded to ensure the accuracy and robustness of the analysis. To characterize the temporal distribution of dyspnoea associated with ticagrelor, we constructed cumulative distribution curves to visualize the TTO profile for ticagrelor. For selected ticagrelor-related dyspnoea, Weibull shape parameter (WSP) analysis was further applied at the PT level to evaluate whether the reporting pattern varied over time. The WSP model is defined by two parameters: the shape parameter (β) and the scale parameter (α). The scale parameter α represents the time at which 63.2% of reported events have occurred within the distribution. The shape parameter β describes the temporal reporting pattern: β 1 indicates an increasing reporting rate over time (wear-out failure type), and β = 1 suggests a constant reporting rate throughout the observation period (random failure type). In addition to WSP analysis, descriptive statistics including median, interquartile range, minimum, and maximum values were calculated to comprehensively describe the TTO distribution. 2.5 Statistical analysis Data processing and statistical analyses were performed using R version 4.5.1 (R Foundation for Statistical Computing, Vienna, Austria) and Microsoft Excel 2021. All statistical tests were two-tailed, with a significance level of p < 0.05. Confidence intervals for all estimates were calculated at the 95% level. 2.6 Target identification of ticagrelor and dyspnoea The SwissTargetPrediction database (http://www.swisstargetprediction.ch/) was used to screen the pharmacological targets of ticagrelor. The search terms “dyspnoea”, “dyspnoea exertional”, “dyspnoea at rest”, “nocturnal dyspnoea”, and “dyspnoea paroxysmal nocturnal” were utilized to identify ticagrelor-related dyspnoea targets in the GeneCards (https://www.genecards.org/), OMIM (https://www.omim.org/), and DisGeNET (http://www.disgenet.org/) databases. For simplicity, ”dyspnoea” was used to represent all five terms in this study. The target genes associated with dyspnoea were subsequently obtained by removing duplicate entries. The intersection targets of ticagrelor and dyspnoea were evaluated using Venn diagrams to identify potential targets for ticagrelor-related dyspnoea. 2.7 Network construction The intersection targets of ticagrelor and dyspnoea were uploaded to the STRING database (https://cn.string-db.org/), specifying the species as ”Homo sapiens” and setting a minimum interaction score of 0.7. Free nodes were also removed. The resulting data were then imported into Cytoscape 3.9.1 software for visualization. 2.8 GO and KEGG enrichment analysis The intersection targets were uploaded to the DAVID database (https://david.ncifcrf.gov/) for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Based on the bioinformatics analysis results, KEGG enrichment bubble maps and GO term enrichment maps were subsequently generated using the online mapping tool Bioinformatics (https://www.bioinformatics.com.cn/) 21 , illustrating the interactions between ticagrelor targets and pathways related to dyspnoea targets. 2.9 Molecular docking The 3D conformer of ticagrelor in SDF format was downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and converted to PDB format using OpenBabel 3.1.1 software. The crystal structures of the target proteins were acquired from the PDB database (https://www.rcsb.org/). For the molecular docking study, target genes with higher degrees from the target-pathway map were selected. The target genes GTPase HRas (HRAS), epidermal growth factor receptor (EGFR), and mitogen-activated protein kinase 14 (MAPK14) were identified as most likely associated with ticagrelor-related dyspnoea. Molecular docking and visualization of results were conducted using AutoDock Vina 1.1.2 and PyMOL Version 2.4.0a0 Open-Source. 3 Results 3.1 The clinical information of ticagrelor-related dyspnoea reports The clinical and demographic characteristics of the dyspnoea reports associated with ticagrelor are detailed in Table 3. A total of 2521, 120, 23, 16, and 4 cases of ticagrelor-related dyspnoea were reported for dyspnoea, dyspnoea exertional, dyspnoea at rest, nocturnal dyspnoea, and paroxysmal nocturnal dyspnoea, respectively. According to the established criteria for positive signal identification, all signals except for bendopnoea were identified as positive. The detailed results are presented in Table 4. Table 3 The clinical information of the ticagrelor-related dyspnoea adverse events reported (N=16999) (N=2647) (N=19646) Sex, n (%) Female 5724 (33.7%) 1021 (38.6%) 6745 (34.3%) Male 9834 (57.9%) 1483 (56.0%) 11317 (57.6%) Missing 1441 (8.5%) 143 (5.4%) 1584 (8.1%) Weight (kg), n (%) ＜50 119 (0.7%) 22 (0.8%) 141 (0.7%) ＞100 439 (2.6%) 167 (6.3%) 606 (3.1%) 50～100 3697 (21.7%) 948 (35.8%) 4645 (23.6%) Missing 12744 (75.0%) 1510 (57.0%) 14254 (72.6%) Age (yr), n (%) 85 372 (2.2%) 53 (2.0%) 425 (2.2%) 18～64.9 4110 (24.2%) 608 (23.0%) 4718 (24.0%) 65～85 5227 (30.7%) 969 (36.6%) 6196 (31.5%) Missing 7272 (42.8%) 1014 (38.3%) 8286 (42.2%) Reporter countries, n (%) United States 9169 (53.9%) 1789 (67.6%) 10958 (55.8%) China 781 (4.6%) 241 (9.1%) 1022 (5.2%) Russian Federation 1398 (8.2%) 185 (7.0%) 1583 (8.1%) United Kingdom 444 (2.6%) 57 (2.2%) 501 (2.6%) Germany 351 (2.1%) 47 (1.8%) 398 (2.0%) Other countries 4856 (28.6%) 328 (12.4%) 5184 (26.4%) Reporter, n (%) Consumer 6138 (36.1%) 1184 (44.7%) 7322 (37.3%) Health Professional 607 (3.6%) 105 (4.0%) 712 (3.6%) Pharmacist 557 (3.3%) 93 (3.5%) 650 (3.3%) Physician 6186 (36.4%) 693 (26.2%) 6879 (35.0%) Missing 3511 (20.7%) 572 (21.6%) 4083 (20.8%) Table 4 Signal detection results of disproportionality analysis dyspnoea 2521 6.43 ( 6.18 - 6.69 ) 2.61 ( 2.55 ) dyspnoea exertional 120 4.17 ( 3.48 - 4.99 ) 2.05 ( 1.75 ) dyspnoea at rest 23 9.73 ( 6.45 - 14.68 ) 3.27 ( 2.23 ) nocturnal dyspnoea 16 17.81 ( 10.86 - 29.22 ) 4.13 ( 2.44 ) dyspnoea paroxysmal nocturnal 4 9.12 ( 3.41 - 24.43 ) 3.18 ( 0.49 ) bendopnoea 2 11.67 ( 2.89 - 47.05 ) 3.53 ( -0.33 ) PT, preferred term; ROR, reporting odds ratio; CI, confidence interval; IC, information component 3.2 Time-to-onset analyses This study performed a cumulative percentage analysis on the frequency of ticagrelor-related dyspnoea and adverse events other than dyspnoea associated with ticagrelor, as shown in Figure 1. Median time-to-onset was 5.0 days (IQR: 2.0–31.8) for ticagrelor-related dyspnoea and 37.0 days (IQR: 6.0–211.0) for adverse events other than dyspnoea associated with ticagrelor. Statistically significant difference was observed among the treatment groups (Kruskal–Wallis test, P ＜0.001). Figure 1 Cumulative distribution functions of time-to-onset for dyspnoea associated with ticagrelor To further characterize the temporal pattern of ticagrelor-related dyspnoea onset, a Weibull distribution model was applied to the time-to-event data, as shown in table 5. A shape parameter (β) less than 1 confirms a decreasing hazard rate over time, consistent with the ”Early failure” type, indicating that the majority of ticagrelor-related dyspnoea adverse events occurred shortly after treatment initiation. Table 5 Time-to-onset analysis of ticagrelor-related dyspnoea signals using the Weibull distribution test ticagrelor 474 5.0 (2.0-31.8) 21.0 (16.9-25.0) 0.5 (0.5-0.5) Early failure IQR, interquartile range; CI, confidence interval 3.3 Logistic regression analyses Table 6 presents the results of the univariable and multivariable logistic regression analyses identifying factors associated with ticagrelor-related dyspnoea. In the multivariable analysis, sex, age, and weight were identified as independent predictors of dyspnoea. Male sex was significantly associated with a lower risk of dyspnoea compared to female sex (adjusted odds ratio [aOR] 0.80, 95% confidence interval [CI] 0.69–0.93, P = 0.003). Advanced age was found to be a significant risk factor. Compared with patients aged 18–44 years, the risk of dyspnoea increased progressively with age. The highest risk was observed in the 65–80 age group (aOR 2.57, 95% CI 1.57–4.48, P < 0.001), followed by patients aged ≥80 years (aOR 2.31, 95% CI 1.35–4.18, P = 0.003). Patients aged 45–64 years also showed a significant increase in risk (aOR 1.72, 95% CI 1.05–3.01, P = 0.043). Higher body weight was also a significant factor. The median weight was higher in the dyspnoea group (81.7 kg) compared to the no dyspnoea group (78.8 kg). Both univariable and multivariable analyses indicated that each 1 kg increase in weight was associated with a small but statistically significant increase in the odds of dyspnoea (aOR 1.01, 95% CI 1.01–1.01, P < 0.001). Table 6 Logistic regression analysis of dyspnoea associated with ticagrelor No dyspnoea Dyspnoea Sex Female, n(%) 1220 (75.4) 397 (24.6) - - Male, n (%) 2128 (78.2) 592 (21.8) 0.85 (0.74-0.99), 0.034 0.80 (0.69-0.93), 0.003 Age (yr) 18-44, n (%) 115 (87.1) 17 (12.9) - - 45-64, n (%) 1389 (80.4) 339 (19.6) 1.65 (1.01-2.88), 0.060 1.72 (1.05-3.01), 0.043 65-80, n (%) 1507 (73.9) 533 (26.1) 2.39 (1.46-4.16), 0.001 2.57 (1.57-4.48, <0.001 ≥80, n (%) 337 (77.1) 100 (22.9) 2.01 (1.18-3.61), 0.014 2.31 (1.35-4.18), 0.003 Weight (kg), median (IQR) 78.8 (18.6) 81.7 (19.6) 1.01 (1.00-1.01), <0.001 1.01 (1.01-1.01), <0.001 OR, Odds Ratio; IQR, interquartile range; CI, confidence interval 3.4 Network pharmacology A total of 1,313 genes associated with dyspnoea were identified for analysis. A total of 108 targets associated with ticagrelor were identified. A Venn diagram was used to identify 29 intersection targets between dyspnoea and ticagrelor target sets (Figure 2). The STRING database was utilized to gather protein-protein interaction (PPI) data for these 29 targets. Subsequently, the pharmacological target network and PPI network associated with ticagrelor-related dyspnoea were constructed (Figure 2). In the PPI network, nodes represent target proteins. The node size corresponds to the degree value, indicating the number of interacting partners; larger nodes denote higher connectivity. The node color gradient from light orange to deep red reflects the centrality of the protein in the network, with darker colors representing more critical hub proteins. Edges represent protein–protein interactions, where thicker and darker lines indicate stronger or higher-confidence associations based on interaction scores. Figure 2 The total targets of ticagrelor, dyspnoea and 29 intersection targets of ticagrelor action causing dyspnoea were identified by Venn diagram and PPI network 3.5 GO and KEGG enrichment analysis In the GO and KEGG enrichment analyses of this study, the significance criterion was based on the p-value as the core judgment basis. The GO enrichment analysis identified a total of 169 biological process (BP) entries, 33 cellular component (CC) entries, and 39 molecular function (MF) entries from the 29 intersection targets. The top 10 entries were visualized and ranked by p-value, as shown in Figure 3. BP were mainly involved in peptidyl-tyrosine phosphorylation, positive regulation of protein phosphorylation, phosphorylation, positive regulation of phosphatidylinositol 3-kinase/protein kinase B signal transduction, positive regulation of kinase activity, among others. CC were primarily involved in the cell surface, plasma membrane, membrane raft, neuronal cell body, receptor complex, among others. MF were primarily involved in protein tyrosine kinase activity, protein phosphatase binding, ATP binding, identical protein binding, 3’,5’-cyclic-AMP phosphodiesterase activity, among others. KEGG enrichment analysis via the DAVID functional annotation tool identified 77 pathways. The top 20 pathways, ranked by p-value, were visualized using a bioinformatics platform, as shown in Figure 4. The top-ranked pathways included the phospholipase D signaling pathway, prostate cancer, proteoglycans in cancer, the neurotrophin signaling pathway, lipid and atherosclerosis, among others. These findings may provide insights into the mechanism of ticagrelor-related dyspnoea. To identify the key targets in the protein–protein interaction and target–pathway networks, topological parameters were calculated using the CytoHubba plugin in Cytoscape. Degree centrality was selected as the primary criterion, as it reflects the number of direct interactions of each node. Nodes with a degree value greater than the median were considered hub nodes and retained for subsequent analysis. Other parameters, including betweenness centrality and closeness centrality, were also calculated to ensure the robustness of the selection. The top 20 KEGG pathways and 29 intersection targets were visualized using Cytoscape 3.9.1 to construct the target-pathway network, as shown in Figure 5. Figure 3 The GO enrichment analysis of BP, CC, and MF. BP = biological process, CC = cellular component, MF = molecular function Figure 4 Bubble chart of the top 20 significantly enriched terms in KEGG pathways. The color coding indicates the significance of each pathway, with red indicating highly significant and green and yellow indicating less significant Figure 5 The target-pathway network. Orange indicates the pathway; Blue indicates the intersection targets 3.6 Molecular docking For validation, molecular docking of a reference positive control drug with the key targets (EGFR, MAPK14, and HRAS) was performed using the same protocol as ticagrelor. The binding energy scores obtained served as a comparative reference, supporting the reliability of our docking results. The binding conformations and interactions between ticagrelor and three proteins were obtained using AutoDock Vina v.1.1.2, and the binding energies of each interaction were calculated. The results showed that ticagrelor binds to its protein targets through visible hydrogen bonds and strong electrostatic interactions. The binding energies of ticagrelor with MAPK14 (PDB ID: 6SFO; resolution: 1.75 Å) and EGFR (PDB ID: 5UG9; resolution: 1.33 Å) were -10.1 kcal/mol and -8.5 kcal/mol, respectively, indicating high stability of the binding. The binding energy of ticagrelor with HRAS (PDB ID: 8CXF; resolution: 1.77 Å) was -7.0 kcal/mol, indicating good binding activity. These results are visualized in Figure 6. Figure 6 Interaction of ticagrelor with dyspnoea targets. (A) The docking diagram of ticagrelor and HRAS (binding energy = -7.0 kcal/mol). (B) The docking diagram of ticagrelor and EGFR (binding energy = -8.5 kcal/mol). (C) The docking diagram of ticagrelor and MAPK14 (binding energy = -10.1 kcal/mol). HRAS = GTPase HRas, EGFR = Epidermal growth factor receptor, MAPK14 = Mitogen-activated protein kinase 14 4 Discussion 4.1 Treatment and management of ticagrelor-related dyspnoea Ticagrelor-related dyspnoea is positively correlated with dosage, commonly occurring early in treatment. Despite extensive research, the mechanism of ticagrelor-related dyspnoea is not fully understood. Extensively researched mechanisms include extracellular adenosine accumulation, neuronal cell receptor inhibition, transfusion-related acute lung injury, and ticagrelor’s inherent pharmacological effects 22–24 . To our knowledge, there are currently no studies that utilize the FAERS database and network pharmacology to investigate the association and pharmacological mechanisms between ticagrelor and dyspnoea. Therefore, this study employed disproportionality analysis and network pharmacology to investigate the association between ticagrelor and dyspnoea, aiming to elucidate the underlying mechanisms. Logistic regression analysis suggested that older age, female, and higher weight were independent predictors of ticagrelor-related dyspnoea. The study suggests that age and female sex are both risk factors for ticagrelor-related dyspnoea, consistent with the results of a multicenter prospective observational cohort study 25 . In a secondary analysis of the TWILIGHT trial, obesity (BMI ≥30 kg/m²) was identified as an independent predictor of ticagrelor discontinuation due to dyspnoea 11 , which is consistent with the findings from the logistic regression analysis. Although there are issues with incomplete demographic information or errors in the FAERS database, which may introduce some bias in the incidence rate of ADEs among the population, the large sample size compensates for this deficiency, and the results still have significant reference value. In recent years, numerous global clinical trials have aimed to deepen the understanding of risk factors associated with ticagrelor-related dyspnoea. The Tamakauskas research group first identified, through a high-sensitivity ADP (ADP HS) induced platelet aggregation test, that lower levels of ADP HS (≤19.5U) could increase the risk of developing dyspnoea by more than four times. Additionally, atorvastatin, compared to rosuvastatin, has been found to increase the risk of ticagrelor-related dyspnoea, potentially due to its effect on the metabolism of ticagrelor 26 . It has also been discovered that enrolled ACS patients with coronary artery double-vessel and above lesions, hypothyroidism, higher creatinine levels, and the FBG-C148T (rs1800787) gene polymorphism are associated with an increased incidence of ticagrelor-related dyspnoea 26 . Furthermore, some research has found a higher probability of ticagrelor causing dyspnoea in patients with COPD, although it does not affect their prognosis 27 . The TWILIGHT trial, a randomized, placebo-controlled study conducted at 187 research centers across 11 countries, reported independent predictive factors for an increased risk of ticagrelor-related dyspnoea, including failure to quit smoking, previous PCI, hypercholesterolemia, history of coronary artery bypass grafting, peripheral artery disease, obesity (BMI ≥30 kg/m²), and advanced age (≥65 years) 11 . The PLATO study and the GLOBAL LEADERS trial both indicate that in patients with ACS undergoing dual antiplatelet therapy with ticagrelor, the occurrence of dyspnoea does not adversely affect clinical outcomes 27,28 . However, the PLATO trial reported a discontinuation rate due to ticagrelor-related dyspnoea of only 0.9%. The GLOBAL LEADERS trial primarily focused on comparing the outcomes of ticagrelor monotherapy versus standard dual antiplatelet therapy and did not specifically evaluate the consequences of discontinuing ticagrelor due to dyspnoea. Conversely, the study examined the impact of dyspnoea on ticagrelor adherence and discontinuation within the first month after PCI in ACS patients 29 . The results showed that 16.7% of patients discontinued ticagrelor, with ticagrelor-related dyspnoea as the primary reason for discontinuation in 55.6% of these cases. Additionally, case reports describe a patient with acute myocardial infarction who experienced persistent, intolerable ticagrelor-related dyspnoea on the 10th day after PCI while taking both aspirin and ticagrelor 30 . This led to a switch to clopidogrel without a loading dose. Unfortunately, two days after the switch, the patient suffered recurrent acute chest pain, and coronary angiography revealed in-stent thrombosis. However, most cases are associated with mild and manageable symptoms. These patients should be encouraged to continue taking ticagrelor. If symptoms become intolerable, a loading dose should be administered when switching to clopidogrel, and it is important to prevent premature discontinuation of all P2Y12 receptor blockers 6 . Furthermore, the study demonstrated that in patients with acute myocardial infarction, transitioning from ticagrelor to clopidogrel after one month may be an acceptable option to alleviate ticagrelor-related dyspnoea without increasing the risk of ischemic events 31 . In most cases, modest and brief episodes of dyspnoea occur during the first days of therapy 9,10 . It is recommended that the patient be monitored for a few days while continuing the medication, as these episodes are likely to resolve after this period. However, if these adverse effects become life-threatening or intolerably severe, the patient may need to discontinue ticagrelor and switch to another medication 6 . Studies have found that theophylline, an adenosine antagonist, can alleviate ticagrelor-related dyspnoea 32,33 . In a study, after administering 180 mg of theophylline to patients with dyspnoea, all patients reported an improvement in shortness of breath 34 . When 200 mg of theophylline was given one hour later, the symptoms disappeared completely. This dose was lower than the recommended dose for acute asthma (5 mg/kg), but no prethrombotic response to theophylline was reported. The study suggested that a fixed combination of theophylline and ticagrelor might be an important step to prevent frequent discontinuation of ticagrelor treatment. However, the study only included only ten patients, which limits the generalizability and reliability of the results. Furthermore, the absence of a control group makes it difficult to determine whether the effect of theophylline is superior to other treatment methods or merely a placebo effect. Therefore, large-scale clinical trials are needed to verify the feasibility of this combination therapy. 4.2 Molecular targets and pathway activation of ticagrelor-related dyspnoea Network pharmacology is a contemporary approach for identifying potential drug targets and understanding their actions 18 . This study employed GO and KEGG enrichment analyses to predict that ticagrelor may induce dyspnoea through the biological processes of positive regulation of PI3K/PKB signal transduction and the MAPK cascade. The positive regulation of PI3K/PKB signaling involves an increase in kinase activity, which subsequently activates the PI3K-Akt signaling pathway 35–38 . Research indicates that this pathway is implicated in several physiological processes such as reactive oxygen species generation, mast cell activation, and neutrophil migration, all of which can lead to inflammation in respiratory diseases like chronic obstructive pulmonary disease, asthma, and dyspnoea. Similarly, the positive regulation of the MAPK cascade activates the MAPK signaling pathway, which is crucial for cell proliferation, differentiation, and stress responses 39–41 . Activation of this pathway promotes the release of inflammatory cytokines, exacerbating airway inflammation, leading to airway narrowing and remodeling, and consequently, dyspnoea. Docking studies predict the energetically favorable binding conformations of ligands in target active sites. These studies revealed that ticagrelor exhibits the lowest binding energy with the MAPK14 target compared to EGFR and HRAS, indicating superior binding stability. This finding is consistent with GO and KEGG enrichment results, which suggest that ticagrelor is likely to induce dyspnoea through activation of the MAPK signaling pathway. Integrating molecular docking with network pharmacology offers a more comprehensive strategy to elucidate drug–target interactions. Network pharmacology identifies potential targets and pathways in a systematic manner, while molecular docking provides structural evidence of ligand–protein binding affinity, thereby enhancing the reliability of target prediction. However, docking simulations are limited by the static nature of crystal structures, simplified scoring functions, and the absence of dynamic physiological conditions, which may not fully reflect in vivo interactions. Therefore, the combined approach strengthens mechanistic hypotheses but requires further experimental validation to confirm the predicted results. 4.3 Strength and limitations The FAERS database, as a voluntary reporting system, may suffer from incomplete and inaccurate reporting, which can impact the quality of research findings. In addition, due to the nature of the database, this study is limited to identifying associations rather than establishing causal relationships. Therefore, it is essential to interpret these research results with caution and consider them alongside other clinical evidence for a comprehensive analysis. Moreover, the significance of bioinformatics analysis is limited, necessitating further molecular, cellular, or animal validation to delve into the underlying mechanisms of ticagrelor-related dyspnoea. 5 Conclusion Our retrospective analysis revealed a significant association between ticagrelor and adverse events of dyspnoea. In addition, this study identified potential risk factors for ticagrelor-related dyspnoea. Genes and pathways involved in dyspnoea symptoms, due to their interaction with ticagrelor, were significantly enriched in categories related to respiratory diseases. These findings underscore the need for further pharmacological research to explore these associations in greater depth. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions Shuaimin Xu and Yang Zhao conducted an experiment design and data mining. Weijuan Song conducted statistical analysis and chart drawing. Shuaimin Xu conducted paper writing and Yanhong Wang made further modifications to the paper. All authors agree to be accountable for all aspects of the work. Funding This study was funded by the Henan Provincial Medical Science and Technology Research Plan Joint Construction Project under Grant LHGJ20230400; the Henan Provincial Medical Education Research Project under Grant WJLX2024087. Acknowledgments We would like to express our sincere gratitude to the participants who provided financial support and contributed to the datasets used in this study. 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Chest . 2011;139(6):1470-1479. doi:10.1378/chest.10-1914 Information & Authors Information Version history V1 Version 1 18 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Shuaimin Xu 0009-0003-2864-7041 [email protected] The Fifth Affiliated Hospital of Zhengzhou University View all articles by this author Weijuan Song The Fifth Affiliated Hospital of Zhengzhou University View all articles by this author Yanhong Wang The Fifth Affiliated Hospital of Zhengzhou University View all articles by this author Yang Zhao The Fifth Affiliated Hospital of Zhengzhou University View all articles by this author Metrics & Citations Metrics Article Usage 121 views 28 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Shuaimin Xu, Weijuan Song, Yanhong Wang, et al. Risk factors and mechanisms of ticagrelor-related dyspnoea from real-world evidence and network pharmacology. Authorea . 18 January 2026. 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