{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:45:24Z","timestamp":1760060724516,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Background: Deep learning methods may be useful for drug compound interaction and discovery analysis. However, there has been limited research on their use for screening biologically related adverse effects. Objectives: This study aims to pre-emptively screen for likely drug use persistence or success in clinical trials. Methods: This shall be achieved through the extension, application, and evaluation of a deep learning-based framework. Specifically, it shall be considered in the discovery of novel candidates and mechanisms underlying AF management-related adverse effects. The targets were linked to their adverse effects specified in two previous studies, their corresponding protein sequences, and the organs affected. Results: The new model showed good performance when compared to existing approaches in the Side Effect Resource (SIDER) and Food and Drug Administration Adverse Event Reporting System (FAERS) external validation datasets. A precision of 0.879 was obtained for enoxaparin, along with a recall of 0.746 for rivaroxaban. Stronger bleeding-related adverse effects were found for edoxaban compared with apixaban and enoxaparin. The binding and safety profiles of sequoiaflavone were very similar to those of rivaroxaban. Conclusions: This study presents a framework that could be used to pre-emptively screen for adverse effects. In doing so, it considers the biological basis for guiding optimal drug selection.<\/jats:p>","DOI":"10.3390\/bioengineering12090972","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T12:34:03Z","timestamp":1757680443000},"page":"972","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Rapid Screening of Anticoagulation Compounds for Biological Target-Associated Adverse Effects Using a Deep-Learning Framework in the Management of Atrial Fibrillation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1953-0063","authenticated-orcid":false,"given":"Tim","family":"Dong","sequence":"first","affiliation":[{"name":"Bristol Heart Institute, Translational Health Sciences, University of Bristol, Bristol BS2 8HW, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rhys","family":"Llewellyn","sequence":"additional","affiliation":[{"name":"Pharmacy Department, Liverpool Heart and Chest Hospital, Thomas Dr., Liverpool L14 3PE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1890-6161","authenticated-orcid":false,"given":"Melanie","family":"Hezzell","sequence":"additional","affiliation":[{"name":"Bristol Veterinary School, University of Bristol, Langford House, Langford, Bristol BS40 5DU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gianni D.","family":"Angelini","sequence":"additional","affiliation":[{"name":"Bristol Heart Institute, Translational Health Sciences, University of Bristol, Bristol BS2 8HW, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1016\/S0140-6736(22)00456-1","article-title":"Safety of the Oral Factor XIa Inhibitor Asundexian Compared with Apixaban in Patients with Atrial Fibrillation (PACIFIC-AF): A Multicentre, Randomised, Double-Blind, Double-Dummy, Dose-Finding Phase 2 Study","volume":"399","author":"Piccini","year":"2022","journal-title":"Lancet"},{"key":"ref_2","unstructured":"(2025, April 03). Asundexian Inferior to Apixaban for Stroke Prevention in Atrial Fibrillation. Available online: https:\/\/www.escardio.org\/The-ESC\/Press-Office\/Press-releases\/Asundexian-inferior-to-apixaban-for-stroke-prevention-in-atrial-fibrillation."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s40264-023-01302-7","article-title":"Factors Associated with Underreporting of Adverse Drug Reactions by Health Care Professionals: A Systematic Review Update","volume":"46","author":"Costa","year":"2023","journal-title":"Drug Saf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1056\/NEJMoa2105872","article-title":"Abelacimab for Prevention of Venous Thromboembolism","volume":"385","author":"Verhamme","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1358\/dof.2021.46.12.3349024","article-title":"Abelacimab. Anti-Factor XI\/XIa Monoclonal Antibody, Treatment of Atrial Fibrillation, Treatment of Thrombotic Disorders","volume":"46","author":"Hardy","year":"2021","journal-title":"Drugs Future"},{"key":"ref_6","unstructured":"(2025, April 03). Apixaban 2.5 Mg Film-Coated Tablets-Summary of Product Characteristics (SmPC)-(Emc) 100159. Available online: https:\/\/www.medicines.org.uk\/emc\/product\/100159\/smpc."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dong, T., Llewellyn, R.D., Hezzell, M., and Angelini, G.D. (2025). A Deep Learning Methodology for Screening New Natural Therapeutic Candidates for Pharmacological Cardioversion and Anticoagulation in the Treatment and Management of Atrial Fibrillation. Biomedicines, 13.","DOI":"10.3390\/biomedicines13061323"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1021\/acs.chemrestox.0c00294","article-title":"Systematic Analysis of Protein Targets Associated with Adverse Events of Drugs from Clinical Trials and Postmarketing Reports","volume":"34","author":"Smit","year":"2021","journal-title":"Chem. Res. Toxicol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"100188","DOI":"10.1016\/j.comtox.2021.100188","article-title":"In Silico Approaches in Organ Toxicity Hazard Assessment: Current Status and Future Needs for Predicting Heart, Kidney and Lung Toxicities","volume":"20","author":"Bassan","year":"2021","journal-title":"Comput. Toxicol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Belfiori, M., Lazzari, L., Hezzell, M., Angelini, G.D., and Dong, T. (2025). Transcriptomics, Proteomics and Bioinformatics in Atrial Fibrillation: A Descriptive Review. Bioengineering, 12.","DOI":"10.20944\/preprints202501.1096.v1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Asgari, E., and Mofrad, M.R.K. (2015). Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0141287"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e2220778120","DOI":"10.1073\/pnas.2220778120","article-title":"Contrastive Learning in Protein Language Space Predicts Interactions between Drugs and Protein Targets","volume":"120","author":"Singh","year":"2023","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Huang, H., Wang, Y., Rudin, C., and Browne, E.P. (2022). Towards a Comprehensive Evaluation of Dimension Reduction Methods for Transcriptomic Data Visualization. Commun. Biol., 5.","DOI":"10.1038\/s42003-022-03628-x"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1093\/jamia\/ocy114","article-title":"Data and Systems for Medication-Related Text Classification and Concept Normalization from Twitter: Insights from the Social Media Mining for Health (SMM4H)-2017 Shared Task","volume":"25","author":"Sarker","year":"2018","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100348","DOI":"10.1016\/j.jpi.2023.100348","article-title":"Performance of Externally Validated Machine Learning Models Based on Histopathology Images for the Diagnosis, Classification, Prognosis, or Treatment Outcome Prediction in Female Breast Cancer: A Systematic Review","volume":"15","author":"Gonzalez","year":"2023","journal-title":"J. Pathol. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1111\/ejh.13240","article-title":"A Comparative Analysis of the Safety Profile of Direct Oral Anticoagulants Using the FDA Adverse Event Reporting System","volume":"103","author":"DeLoughery","year":"2019","journal-title":"Eur. J. Haematol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lagos, C.F., Segovia, G.F., Nu\u00f1ez-Navarro, N., Fa\u00fandez, M.A., and Zacconi, F.C. (2017). Novel FXa Inhibitor Identification through Integration of Ligand- and Structure-Based Approaches. Molecules, 22.","DOI":"10.3390\/molecules22101588"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ferri, N., Colombo, E., Tenconi, M., Baldessin, L., and Corsini, A. (2022). Drug-Drug Interactions of Direct Oral Anticoagulants (DOACs): From Pharmacological to Clinical Practice. Pharmaceutics, 14.","DOI":"10.3390\/pharmaceutics14061120"},{"key":"ref_19","first-page":"e190","article-title":"Mechanistic Basis for the Differential Effects of Rivaroxaban and Apixaban on Global Tests of Coagulation","volume":"2","author":"Kim","year":"2018","journal-title":"Open"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1007\/s11239-021-02416-4","article-title":"Prescribing Trends of Oral Anticoagulants in England over the Last Decade: A Focus on New and Old Drugs and Adverse Events Reporting","volume":"52","author":"Afzal","year":"2021","journal-title":"J. Thromb. Thrombolysis"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e27053","DOI":"10.1097\/MD.0000000000027053","article-title":"Predictors of Hemorrhagic Complications after Intravenous Thrombolysis in Acute Cerebral Infarction Patients: A Single-Center Study of 391 Cases","volume":"100","author":"Zeng","year":"2021","journal-title":"Medicine"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1161\/STROKEAHA.116.016275","article-title":"Rivaroxaban Versus Dabigatran or Warfarin in Real-World Studies of Stroke Prevention in Atrial Fibrillation","volume":"48","author":"Bai","year":"2017","journal-title":"Stroke"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s12959-015-0057-x","article-title":"Efficacy and Safety of Edoxaban versus Enoxaparin for the Prevention of Venous Thromboembolism Following Total Hip Arthroplasty: STARS J-V","volume":"13","author":"Fuji","year":"2015","journal-title":"Thromb. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1161\/STROKEAHA.123.045098","article-title":"Effectiveness and Safety of Edoxaban Compared with Apixaban in Elderly Patients With Nonvalvular Atrial Fibrillation: A Real-World Population-Based Cohort Study","volume":"55","author":"Chiv","year":"2024","journal-title":"Stroke"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.ijcard.2021.11.008","article-title":"Comparison of Clinical Outcomes of Edoxaban versus Apixaban, Dabigatran, Rivaroxaban, and Vitamin K Antagonists in Patients with Atrial Fibrillation in Germany: A Real-World Cohort Study","volume":"346","author":"Marston","year":"2022","journal-title":"Int. J. Cardiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1016\/j.jtha.2023.04.021","article-title":"News at XI: Moving beyond Factor Xa Inhibitors","volume":"21","author":"Fredenburgh","year":"2023","journal-title":"J. Thromb. Haemost."},{"key":"ref_27","unstructured":"(2024). Lipworth, K; Factor XI\/XIa Inhibitors: What We Now Know. EMJ Cardiol., 12, 2\u201313."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.jvc.2021.05.010","article-title":"Phase I Clinical Trial of an Antithrombotic Drug Protocol Combining Apixaban and Clopidogrel in Dogs","volume":"36","author":"Gagnon","year":"2021","journal-title":"J. Vet. Cardiol."},{"key":"ref_29","unstructured":"(2025, April 08). Welcome to the NOAH Compendium. Available online: https:\/\/www.noahcompendium.co.uk\/home."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"732","DOI":"10.2460\/ajvr.76.8.732","article-title":"Pharmacokinetics and Pharmacodynamics of the Factor Xa Inhibitor Apixaban after Oral and Intravenous Administration to Cats","volume":"76","author":"Myers","year":"2015","journal-title":"Am. J. Vet. Res."},{"key":"ref_31","unstructured":"(2025, April 09). Amiodarone Hydrochloride Drugs BNF Content Published by NICE. Available online: https:\/\/bnf.nice.org.uk\/drugs\/amiodarone-hydrochloride\/."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1093\/europace\/euy310","article-title":"Pre- and Post-Treatment with Amiodarone for Elective Electrical Cardioversion of Atrial Fibrillation: A Systematic Review and Meta-Analysis","volume":"21","author":"Um","year":"2019","journal-title":"EP Europace"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1177\/1358863X231206778","article-title":"Factor XI\/XIa Inhibitors for the Prevention and Treatment of Venous and Arterial Thromboembolism: A Narrative Review","volume":"29","author":"Prakash","year":"2023","journal-title":"Vasc. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101248","DOI":"10.1016\/j.jpha.2025.101248","article-title":"The Future of Pharmaceuticals: Artificial Intelligence in Drug Discovery and Development","volume":"15","author":"Fu","year":"2025","journal-title":"J. Pharm. Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"17833","DOI":"10.1021\/acs.jmedchem.4c01857","article-title":"Deep-Learning-Driven Discovery of SN3\u20131, a Potent NLRP3 Inhibitor with Therapeutic Potential for Inflammatory Diseases","volume":"67","author":"Shi","year":"2024","journal-title":"J. Med. Chem."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1007\/s10637-019-00816-1","article-title":"Dual Functionality of the Antimicrobial Agent Taurolidine Which Demonstrates Effective Anti-Tumor Properties in Pediatric Neuroblastoma","volume":"38","author":"Swift","year":"2020","journal-title":"Investig. New Drugs"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"13427","DOI":"10.1038\/ncomms13427","article-title":"In Silico Pathway Activation Network Decomposition Analysis (iPANDA) as a Method for Biomarker Development","volume":"7","author":"Ozerov","year":"2016","journal-title":"Nat. Commun."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/9\/972\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:44:38Z","timestamp":1760035478000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/9\/972"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,12]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["bioengineering12090972"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12090972","relation":{},"ISSN":["2306-5354"],"issn-type":[{"type":"electronic","value":"2306-5354"}],"subject":[],"published":{"date-parts":[[2025,9,12]]}}}