{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T20:15:17Z","timestamp":1792440917611,"version":"4.5.0"},"reference-count":53,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T00:00:00Z","timestamp":1777075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ajou University Research Fund","award":["S-2025-G0001-00451"],"award-info":[{"award-number":["S-2025-G0001-00451"]}]},{"award":["S-2025-G0001-00451"],"award-info":[{"award-number":["S-2025-G0001-00451"]}],"id":[{"id":"https:\/\/ror.org\/03tzb2h73","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"crossref","award":["RS-2026-25474310"],"award-info":[{"award-number":["RS-2026-25474310"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"crossref","award":["2021R1C1C1003735"],"award-info":[{"award-number":["2021R1C1C1003735"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"crossref"}]},{"award":["RS-2026-25474310"],"award-info":[{"award-number":["RS-2026-25474310"]}],"id":[{"id":"https:\/\/ror.org\/01wpjm123","id-type":"ROR","asserted-by":"publisher"}]},{"award":["2021R1C1C1003735"],"award-info":[{"award-number":["2021R1C1C1003735"]}],"id":[{"id":"https:\/\/ror.org\/01wpjm123","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003569","name":"Ministry of Food and Drug Safety","doi-asserted-by":"crossref","award":["RS-2026-25522906"],"award-info":[{"award-number":["RS-2026-25522906"]}],"id":[{"id":"10.13039\/501100003569","id-type":"DOI","asserted-by":"crossref"}]},{"award":["RS-2026-25522906"],"award-info":[{"award-number":["RS-2026-25522906"]}],"id":[{"id":"https:\/\/ror.org\/01f7dp456","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceuticals"],"abstract":"<jats:p>Background\/Objectives: B-cell maturation antigen (BCMA)-directed immunotherapies, including chimeric antigen receptor T-cell (CAR-T) therapies and bispecific antibodies (BsAbs), have improved clinical outcomes in multiple myeloma. However, cytokine release syndrome (CRS) remains a major safety concern, and comparative real-world evidence across BCMA-directed agents remains limited. This study aimed to evaluate and compare CRS reporting patterns associated with BCMA-targeted CAR-T and BsAb therapies using the FDA Adverse Event Reporting System (FAERS) data and to identify predictors of CRS reporting using machine learning-based approaches. Methods: A pharmacovigilance analysis was conducted using FAERS reports from 2021 Q1 to 2025 Q3. Disproportionality analyses were performed using the reporting odds ratio (ROR), proportional reporting ratio (PRR), and information component (IC), and signals were considered present when predefined thresholds were met. Multivariable logistic regression was applied to estimate adjusted odds ratios (aORs) for CRS reporting while adjusting for demographic and reporting characteristics. Machine learning models, including XGBoost, LightGBM, and random forest were developed to predict CRS reporting. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: Among 4046 reports included in the final dataset, CAR-T therapies showed higher CRS reporting odds than BsAbs (aOR: 2.55, 95% CI: 2.16\u20133.01). Disproportionality analyses identified significant CRS signals for CAR-T therapies across all indices, whereas BsAbs did not meet signal detection thresholds. At the agent level, idecabtagene vicleucel was the only agent meeting all predefined signal detection criteria and exhibited the strongest reporting pattern in multivariable analysis (aOR: 6.96, 95% CI: 5.53\u20138.75). Among the evaluated models, LightGBM achieved the highest predictive test AUROC (0.762). SHAP analysis identified idecabtagene vicleucel, United States region, and reporting year as the most influential predictors of CRS reporting. Conclusions: CAR-T therapies, particularly idecabtagene vicleucel, exhibited higher CRS reporting odds than BsAbs, with substantial agent-level heterogeneity observed across BCMA-directed immunotherapies. Integrating pharmacovigilance and machine learning approaches may facilitate more individualized safety monitoring by identifying agent-specific differences in CRS risk among BCMA-targeted therapies.<\/jats:p>","DOI":"10.3390\/ph19050669","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T10:34:42Z","timestamp":1777458882000},"page":"669","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Signal Detection and Machine Learning-Based Prediction of Cytokine Release Syndrome in B-Cell Maturation Antigen-Targeting Immunotherapies Using FAERS Data"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7034-2115","authenticated-orcid":false,"given":"Suhyeon","family":"Moon","sequence":"first","affiliation":[{"name":"Department of Biohealth Regulatory Science, Graduate School, Ajou University, Suwon 16499, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7088-867X","authenticated-orcid":false,"given":"Dong-Won","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Biohealth Regulatory Science, Graduate School, Ajou University, Suwon 16499, Republic of Korea"},{"name":"Department of Pharmacy, College of Pharmacy, Ajou University, Suwon 16499, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0635-4374","authenticated-orcid":false,"given":"Yeo Jin","family":"Choi","sequence":"additional","affiliation":[{"name":"College of Pharmacy and Institute of Integrated Pharmaceutical Science, Kyung Hee University, Seoul 02447, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2388-1122","authenticated-orcid":false,"given":"Sooyoung","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Biohealth Regulatory Science, Graduate School, Ajou University, Suwon 16499, Republic of Korea"},{"name":"Department of Pharmacy, College of Pharmacy, Ajou University, Suwon 16499, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1053\/j.seminoncol.2016.11.004","article-title":"Multiple myeloma epidemiology and survival: A unique malignancy","volume":"43","author":"Kazandjian","year":"2016","journal-title":"Semin. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/978-3-540-85772-3_2","article-title":"Epidemiology of multiple myeloma","volume":"183","author":"Becker","year":"2011","journal-title":"Mult. Myeloma"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1002\/ajh.26590","article-title":"Multiple myeloma: 2022 update on diagnosis, risk stratification, and management","volume":"97","author":"Rajkumar","year":"2022","journal-title":"Am. J. Hematol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"vii143","DOI":"10.1093\/annonc\/mdq370","article-title":"Multiple myeloma","volume":"21","author":"Dimopoulos","year":"2010","journal-title":"Ann. Oncol."},{"key":"ref_5","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1802","DOI":"10.1002\/ajh.27422","article-title":"Multiple myeloma: 2024 update on diagnosis, risk-stratification, and management","volume":"99","author":"Rajkumar","year":"2024","journal-title":"Am. J. Hematol."},{"key":"ref_7","first-page":"e528","article-title":"Multiple myeloma: EHA-ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up","volume":"5","author":"Dimopoulos","year":"2021","journal-title":"Hemasphere"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1056\/NEJMoa1611750","article-title":"Lenalidomide, bortezomib, and dexamethasone with transplantation for myeloma","volume":"376","author":"Attal","year":"2017","journal-title":"N. Engl. J. Med."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fonseca, R., Usmani, S.Z., Mehra, M., Slavcev, M., He, J., Cote, S., Lam, A., Ukropec, J., Maiese, E.M., and Nair, S. (2020). Frontline treatment patterns and attrition rates by subsequent lines of therapy in patients with newly diagnosed multiple myeloma. BMC Cancer, 20.","DOI":"10.1186\/s12885-020-07503-y"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1056\/NEJMoa2024850","article-title":"Idecabtagene vicleucel in relapsed and refractory multiple myeloma","volume":"384","author":"Munshi","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1002\/ajh.27582","article-title":"Evaluating minimal residual disease negativity as a surrogate endpoint for treatment efficacy in multiple myeloma: A meta-analysis of randomized controlled trials","volume":"100","author":"Filippatos","year":"2025","journal-title":"Am. J. Hematol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1038\/s41375-020-0734-z","article-title":"B-cell maturation antigen (BCMA) in multiple myeloma: Rationale for targeting and current therapeutic approaches","volume":"34","author":"Shah","year":"2020","journal-title":"Leukemia"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1056\/NEJMoa1817226","article-title":"Anti-BCMA CAR T-cell therapy bb2121 in relapsed or refractory multiple myeloma","volume":"380","author":"Raje","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1056\/NEJMoa2203478","article-title":"Teclistamab in relapsed or refractory multiple myeloma","volume":"387","author":"Moreau","year":"2022","journal-title":"N. Engl. J. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.bbmt.2018.12.758","article-title":"ASTCT consensus grading for cytokine release syndrome and neurologic toxicity associated with immune effector cells","volume":"25","author":"Lee","year":"2019","journal-title":"Biol. Blood Marrow Transplant."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1002\/cpt.3223","article-title":"Impact of Treatment Modality and Route of Administration on Cytokine Release Syndrome in Relapsed or Refractory Multiple Myeloma: A Meta-Analysis","volume":"115","author":"Soltantabar","year":"2024","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s40425-018-0343-9","article-title":"Cytokine release syndrome","volume":"6","author":"Subklewe","year":"2018","journal-title":"J. Immunother. Cancer"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1186\/s13046-021-02148-6","article-title":"Mechanisms of cytokine release syndrome and neurotoxicity of CAR T-cell therapy and associated prevention and management strategies","volume":"40","author":"Xiao","year":"2021","journal-title":"J. Exp. Clin. Cancer Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"796","DOI":"10.7150\/ijms.6048","article-title":"Data mining of the public version of the FDA adverse event reporting system","volume":"10","author":"Sakaeda","year":"2013","journal-title":"Int. J. Med. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1080\/10543406.2013.736810","article-title":"Likelihood ratio test-based method for signal detection in drug classes using FDA\u2019s AERS database","volume":"23","author":"Huang","year":"2013","journal-title":"J. Biopharm. Stat."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1158\/2643-3230.BCD-24-0118","article-title":"Bispecific antibodies as bridging to BCMA CAR-T cell therapy for relapsed\/refractory multiple myeloma","volume":"6","author":"Fandrei","year":"2025","journal-title":"Blood Cancer Discov."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1038\/s41408-024-01023-9","article-title":"Adverse effects and non-relapse mortality of BCMA directed T cell therapies in multiple myeloma: An FAERS database study","volume":"14","author":"Gong","year":"2024","journal-title":"Blood Cancer J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.tmrv.2019.08.003","article-title":"Anti-CD19 CAR T-cell therapy for B-cell non-Hodgkin lymphoma","volume":"34","author":"Abramson","year":"2020","journal-title":"Transfus. Med. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s40264-015-0289-5","article-title":"Comparison of statistical signal detection methods within and across spontaneous reporting databases","volume":"38","author":"Candore","year":"2015","journal-title":"Drug Saf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1038\/sj.clpt.6100258","article-title":"Novel statistical tools for monitoring the safety of marketed drugs","volume":"82","author":"Almenoff","year":"2007","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_26","unstructured":"U.S. Food and Drug Administration (2026, March 16). FDA Approves Idecabtagene Vicleucel for Multiple Myeloma, Available online: https:\/\/www.fda.gov\/drugs\/resources-information-approved-drugs\/fda-approves-idecabtagene-vicleucel-multiple-myeloma?utm_source=chatgpt.com."},{"key":"ref_27","unstructured":"U.S. Food and Drug Administration (2026, March 16). FDA Approves Ciltacabtagene Autoleucel for Relapsed or Refractory Multiple Myeloma, Available online: https:\/\/www.fda.gov\/drugs\/resources-information-approved-drugs\/fda-approves-ciltacabtagene-autoleucel-relapsed-or-refractory-multiple-myeloma."},{"key":"ref_28","unstructured":"U.S. Food and Drug Administration (2026, March 16). FDA Approves Teclistamab-cqyv for Relapsed or Refractory Multiple Myeloma, Available online: https:\/\/www.fda.gov\/drugs\/resources-information-approved-drugs\/fda-approves-teclistamab-cqyv-relapsed-or-refractory-multiple-myeloma?utm_source=chatgpt.com."},{"key":"ref_29","unstructured":"U.S. Food and Drug Administration (2026, March 16). FDA Grants Accelerated Approval to Elranatamab-bcmm for Multiple Myeloma, Available online: https:\/\/www.fda.gov\/drugs\/resources-information-approved-drugs\/fda-grants-accelerated-approval-elranatamab-bcmm-multiple-myeloma?utm_source=chatgpt.com."},{"key":"ref_30","unstructured":"U.S. Food and Drug Administration (2025, December 17). Good Pharmacovigilance Practices and Pharmacoepidemiologic Assessment, Available online: https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/good-pharmacovigilance-practices-and-pharmacoepidemiologic-assessment."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cutroneo, P.M., Sartori, D., Tuccori, M., Crisafulli, S., Battini, V., Carnovale, C., Rafaniello, C., Capuano, A., Poluzzi, E., and Moretti, U. (2024). Conducting and interpreting disproportionality analyses derived from spontaneous reporting systems. Front. Drug Saf. Regul., 3.","DOI":"10.3389\/fdsfr.2023.1323057"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1002\/cpt.3701","article-title":"FDA adverse event reporting system (FAERS) essentials: A guide to understanding, applying, and interpreting adverse event data reported to FAERS","volume":"118","author":"Potter","year":"2025","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2210","DOI":"10.1172\/JCI126397","article-title":"B cell maturation antigen\u2013specific CAR T cells are clinically active in multiple myeloma","volume":"129","author":"Cohen","year":"2019","journal-title":"J. Clin. Investig."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/s41571-020-0427-6","article-title":"CAR T cell therapies for patients with multiple myeloma","volume":"18","author":"Mikkilineni","year":"2021","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Masnoon, N., Shakib, S., Kalisch-Ellett, L., and Caughey, G.E. (2017). What is polypharmacy? A systematic review of definitions. BMC Geriatr., 17.","DOI":"10.1186\/s12877-017-0621-2"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1002\/pds.677","article-title":"Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports","volume":"10","author":"Evans","year":"2001","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/pds.668","article-title":"A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions","volume":"11","author":"Bate","year":"2002","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1002\/pds.1742","article-title":"Quantitative signal detection using spontaneous ADR reporting","volume":"18","author":"Bate","year":"2009","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1111\/jcpe.70040","article-title":"Pharmacovigilance-Based Identification and Mechanistic Exploration of Periodontitis-Related Drugs","volume":"53","author":"Huoshen","year":"2026","journal-title":"J. Clin. Periodontol."},{"key":"ref_40","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_42","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4\u20139). Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_43","unstructured":"Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Montreal, QC, Canada, 20\u201325 August 1995, Morgan Kaufmann Publishers Inc."},{"key":"ref_44","unstructured":"Garreta, R., and Moncecchi, G. (2013). Learning Scikit-Learn: Machine Learning in Python, Packt Publishing."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Saito, T., and Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118432"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1002\/bjs.10895","article-title":"LASSO regression","volume":"105","author":"Ranstam","year":"2018","journal-title":"J. Br. Surg."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4419","DOI":"10.1038\/s41598-025-34441-1","article-title":"XGBoost based machine learning prediction model for major adverse cardiovascular events after PCI in STEMI patients","volume":"16","author":"Zhang","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pudjihartono, N., Fadason, T., Kempa-Liehr, A.W., and O\u2019Sullivan, J.M. (2022). A review of feature selection methods for machine learning-based disease risk prediction. Front. Bioinform., 2.","DOI":"10.3389\/fbinf.2022.927312"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary","volume":"61","author":"Garcia","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","article-title":"Learning from imbalanced data: Open challenges and future directions","volume":"5","author":"Krawczyk","year":"2016","journal-title":"Prog. Artif. Intell."},{"key":"ref_52","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."}],"container-title":["Pharmaceuticals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8247\/19\/5\/669\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:12:55Z","timestamp":1777522375000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8247\/19\/5\/669"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,25]]},"references-count":53,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["ph19050669"],"URL":"https:\/\/doi.org\/10.3390\/ph19050669","relation":{},"ISSN":["1424-8247"],"issn-type":[{"value":"1424-8247","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,25]]}}}