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1Academic Journal
Authors: Yu. V. Shevchuk, I. I. Shamigulov, I. V. Sychev, A. V. Kryukov, I. I. Temirbulatov, K. B. Mirzaev, N. P. Denisenko, Sh. P. Abdullaev, S. N. Tuchkova, V. I. Vechorko, O. V. Averkov, D. A. Sychev, Ю. В. Шевчук, И. И. Шамигулов, И. В. Сычев, А. В. Крюков, И. И. Темирбулатов, К. Б. Мирзаев, Н. П. Денисенко, Ш. П. Абдуллаев, С. Н. Тучкова, В. И. Вечорко, О. В. Аверков, Д. А. Сычев
Contributors: Данная работа выполнена при финансовой поддержке Министерства здравоохранения Российской Федерации, тематика государственного задания «Разработка системы поддержки принятия врачебных решений для прогнозирования нежелательных лекарственных реакций у пациентов с COVID-19 на основе фармакогенетического тестирования» (ЕГИСУ НИОКТР № 122021800321-2).
Source: Acta Biomedica Scientifica; Том 9, № 6 (2024); 52-62 ; 2587-9596 ; 2541-9420
Subject Terms: модель риска, remdesivir, adverse drug reactions, hepatotoxicity, pharmacogenetic testing, predictors of adverse reactions, machine learning, risk model, ремдесивир, нежелательные реакции, гепатотоксичность, фармакогенетическое исследование, предикторы нежелательных реакций, машинное обучение
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Relation: https://www.actabiomedica.ru/jour/article/view/5118/2933; Временные методические рекомендации по профилактике, диагностике и лечению новой коронавирусной инфекции (COVID-19). М.; 2023.; Gilead Sciences Biopharmaceutical Companies, Veklury (remdesivir). U.S. Food and Drug Administration. 2022. URL: https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/214787Orig1s010Lbl.pdf. [date of access: 20.05.2024].; Pantazis N, Pechlivanidou E, Antoniadou A, Akinosoglou K, Kalomenidis I, Poulakou G, et al. Remdesivir: Effectiveness and safety in hospitalized patients with COVID-19 (ReEs-COVID-19) – Analysis of data from daily practice. Microorganisms. 2023; 11(8): 1998. doi:10.3390/microorganisms11081998; Kang H, Kang CK, Im JH, Cho Y, Kang DY, Lee JY. Adverse drug events associated with remdesivir in real-world hospitalized patients with COVID-19, including vulnerable populations: A retrospective multicenter study. J Korean Med Sci. 2023; 38(44): e346. doi:10.3346/jkms.2023.38.e346; Wang Y, Zhang D, Du G, Du R, Zhao J, Jin Y, et al. Remdesivir in adults with severe COVID-19: A randomised, double-blind, placebo-controlled, multicentre trial. Lancet. 2020; 395(10236): 1569-1578. doi:10.1016/S0140-6736(20)31022-9; Шевчук Ю.В., Крюков А.В., Темирбулатов И.И., Сычев И.В., Мирзаев К.Б., Денисенко Н.П., и др. Модель прогнозирования риска развития лекарственного поражения печени на фоне терапии ремдесивиром: обсервационное проспективное открытое контролируемое исследование. Фармация и фармакология. 2023; 11(3): 228-239. doi:10.19163/2307-9266-2023-11-3-228-239; Falconer N, Barras M, Cottrell N. Systematic review of predictive risk models for adverse drug events in hospitalized patients. Br J Clin Pharmacol. 2018; 84: 846-864. doi:10.1111/bcp.13514; Salas M, Petracek J, Yalamanchili P, Aimer O, Kasthuril D, Dhingra S, et al. The use of artificial intelligence in pharmacovigilance: A systematic review of the literature. Pharm Med. 2022; 36(5): 295-306. doi:10.1007/s40290-022-00441-z; Goldberger J, Roweis ST, Hinton GE, Salakhutdinov R. Neighbourhood components analysis. 2004: 513-520.; Hosmer DW Jr, Lemeshow S, Sturdivant RX. Applied logistic regression. 2013.; Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing. 2020; 408: 189-215. doi:10.1016/j.neucom.2019.10.118; Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. 2017. 13. Biau G, Scornet E. A random forest guided tour. TEST. 2016; 25(1): 197-227. doi:10.1007/s11749-016-0481-7; Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A. CatBoost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems. 2018; 31.; Hossin M, Sulaiman MN. A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manag Process. 2015; 5(2): 1. doi:10.5121/ijdkp.2015.5201; O’Mahony D, O’Connor MN, Eustace J, Byrne S, Petrovic M, Gallagher P. The adverse drug reaction risk in older persons (ADRROP) prediction scale: Derivation and prospective validation of an ADR risk assessment tool in older multi-morbid patients. Eur Geriatr Med. 2018; 9(2): 191-199. doi:10.1007/s41999-018-0030-x; Lavan A, Eustace J, Dahly D, Flanagan E, Gallagher P, Cullinane S, et al. Incident adverse drug reactions in geriatric inpatients: A multicentred observational study. Ther Adv Drug Saf. 2018; 9(1): 13-23. doi:10.1177/2042098617736191; Yadesa TM, Kitutu FE, Tamukong R, Alele PE. Development and validation of ‘Prediction of Adverse Drug Reactions in Older Inpatients (PADROI)’ risk assessment tool. Clin Interv Aging. 2022; 17: 195-210. doi:10.2147/CIA.S350500; Zhang F, Sun B, Diao X, Zhao W, Shu T. Prediction of adverse drug reactions based on knowledge graph embedding. BMC Med Inform Decis Mak. 2021; 21: 1-11. doi:10.1186/s12911-021-01402-3; Galeano D, Li S, Gerstein M, Paccanaro A. Predicting the frequencies of drug side effects. Nat Commun. 2020; 11(1): 4575. doi:10.1038/s41467-020-18305-y; Choudhury O, Park Y, Salonidis T, Gkoulalas-Divanis A, Sylla I, Das AK. Predicting adverse drug reactions on distributed health data using federated learning. AMIA Annu Symp Proc. 2019; 2019: 313-322.; Ayyashi M, Darbashi H, Hakami A, Sharahili F. Evaluation of remdesivir utilization pattern in critically ill patients with COVID-19 in Jazan Province. Cureus. 2023; 15(3): e36247. doi:10.7759/cureus.36247; Iloanusi S, Mgbere O, Essien EJ. Polypharmacy among COVID-19 patients: A systematic review. J Am Pharm Assoc. 2021; 61(5): e14-e25. doi:10.1016/j.japh.2021.05.006; Lee JY, Ang ASY, Mohd Ali N, Ang LM, Omar A. Incidence of adverse reaction of drugs used in COVID-19 management: A retrospective, observational study. J Pharm Policy Pract. 2021; 14: 1-9. doi:10.1186/s40545-021-00370-3; Sendekie AK, Kasahun AE, Limenh LW, Dagnaw AD, Belachew EA. Clinical and economic impact of adverse drug reactions in hospitalised patients: Prospective matched nested case-control study in Ethiopia. BMJ Open. 2023; 13: e073777. doi:10.1136/ bmjopen-2023-073777; Blair HA. Remdesivir: A review in COVID-19. Drugs. 2023; 83(13): 1215-1237. doi:10.1007/s40265-023-01926-0; Pratt VM, Cavallari LH, Fulmer ML, Gaedigk A, Hachad H, Ji Y, et al. CYP3A4 and CYP3A5 genotyping recommendations: A joint consensus recommendation of the association for molecular pathology, clinical pharmacogenetics implementation consortium, College of American Pathologists, Dutch Pharmacogenetics Working Group of the Royal Dutch Pharmacists Association, European Society for Pharmacogenomics and Personalized Therapy, and Pharmacogenomics Knowledgebase. J Mol Diagn. 2023; 25(9), 619-629. doi:10.1016/j.jmoldx.2023.06.008; Buscemi S, Corleo D, Randazzo C. Risk factors for COVID-19: Diabetes, hypertension, and obesity. Coronavirus Therapeutics, Volume II: Clinical Management and Public Health. 2022; 115-129. doi:10.1007/978-3-030-85113-2_7; Zhang X, Ha S, Lau HCH, Yu J. Excess body weight: Novel insights into its roles in obesity comorbidities. Semin Cancer Biol. 2023; 92: 16-27. doi:10.1016/j.semcancer.2023.03.008; Quek J, Chan KE, Wong ZY, Tan C, Tan B, Lim WH, et al. Global prevalence of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in the overweight and obese population: A systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2023; 8(1): 20-30. doi:10.1016/S2468-1253(22)00317-X; https://www.actabiomedica.ru/jour/article/view/5118
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2Academic Journal
Authors: Е. Э. Вайман, Н. А. Шнайдер, Р. Ф. Насырова
Source: Pharmacogenetics and Pharmacogenomics; № 2 (2019); 9-9 ; Фармакогенетика и фармакогеномика; № 2 (2019); 9-9 ; 2686-8849 ; 2588-0527
Subject Terms: фармакогенетическое исследование, ген DRD4, rs1800995, атипичные антипсихотики
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