Annals of the Russian academy of medical sciencesAnnals of the Russian academy of medical sciencesВестник Российской академии медицинских наук0869-60472414-3545"Paediatrician" Publishers LLC1796510.15690/vramn17965PHARMACOLOGY AND PHARMACY: CURRENT ISSUESАКТУАЛЬНЫЕ ВОПРОСЫ ФАРМАКОЛОГИИ И ФАРМАЦИИReview ArticlePolypharmacotherapy: the Use of Artificial Intelligence to Reduce Risk of Adverse Drug Reactions (Review)Полифармакотерапия: использование искусственного интеллекта для снижения рисков побочных эффектов лекарственных средств (обзор литературы)https://orcid.org/0000-0002-0210-45705940-7554BeregovykhValery V.БереговыхВалерий Васильевич
Russian Federation

PhD in Technical Sciences, Professor, Academician of the RAS

д.т.н., профессор, академик РАН

beregovykh@ramn.ruhttps://orcid.org/0000-0002-1575-12674095-8670PanteleevVladimir I.ПантелеевВладимир Игоревич
Russian Federation

MD, PhD

к.м.н.

vpantel@mail.ruhttps://orcid.org/0000-0001-8887-44205232-8230ShimanovskyNikolay L.ШимановскийНиколай Львович
Russian Federation

MD, PhD, Professor, Corresponding Member of the RAS

д.м.н., профессор, член-корреспондент РАН

shimann@yandex.ruRussian Academy of SciencesРоссийская академия наукPlekhanov Russian University of EconomicsРоссийский экономический университет имени Г.В. Плеханова101020247943463521403202426072024Copyright ©; 2024, "Paediatrician" Publishers LLCCopyright ©; 2024, Издательство "Педиатръ"2024"Paediatrician" Publishers LLCИздательство "Педиатръ"https://vestnikramn.spr-journal.ru/jour/about/submissionshttps://vestnikramn.spr-journal.ru/jour/article/view/17965

Artificial intelligence (AI) in healthcare can be used to solve a wide range of tasks, such as diagnosis, treatment and self-monitoring of patients. This review is devoted to the problem of polypharmacotherapy, the development of adverse drug reactions as a consequence of it and the use of AI in this field. AI allows to analyze drug interactions, identify possible adverse drug reactions and suggest optimal combinations of drugs and drug regimen. The use of clinical decision support systems, which are developed in various countries, has shown improved efficiency of the doctor’s work and increased patient’s safety with the help of AI. The use of AI in polypharmacotherapy requires further research and development to improve software products that would allow evaluating not only paired, but also multiple drug interactions.

Искусственный интеллект (ИИ) в медицине может использоваться для решения широкого спектра задач, таких как диагностика заболеваний, лечение, самоконтроль пациентов. Данный обзор посвящен проблеме полифармакотерапии, развитию нежелательных лекарственных реакций на ее фоне и использовании ИИ для ее решения. ИИ позволяет анализировать межлекарственные взаимодействия, определять возможные нежелательные лекарственные реакции и предлагать оптимальные комбинации препаратов и режим их дозирования. Использование разработанных в различных странах систем поддержки принятия врачебных решений показало возможности повышения эффективности работы врача и безопасности пациента с помощью ИИ. Применение ИИ при полифармакотерапии требует дальнейших исследований и разработки для совершенствования программных продуктов, позволяющих оценивать не только парные, но и множественные взаимодействия лекарственных препаратов.

polypharmacyadverse drug reactionsartificial intelligenceclinical decision support systemполифармакотерапиянежелательные лекарственные реакцииискусственный интеллектсистема поддержки принятия врачебных решенийРоссийский научный фондRussian Science Foundation23-75-30012Марцевич С.Ю., Кутишенко Н.П., Лукина Ю.В., и др. Полифармация: определение, влияние на исходы, необходимость коррекции // Рациональная фармакотерапия в кардиологии. — 2023. — Т. 19. — № 3. — С. 254–263. [Martsevich SYu, Kutishenko NP, Lukina YuV, et al. Polypharmacy: definition, impact on outcomes, need for correction. Rational Pharmacotherapy in Cardiology. 2023;19(3):254–263. (In Russ.)] doi: https://doi.org/10.20996/1819-6446-2023-2924Akyon SH, Akyon FC, Yilmaz TE. Artificial intelligence-supported web application design and development for reducing polypharmacy side effects and supporting rational drug use in geriatric patients. Front Med (Lausanne). 2023;10:1029198. doi: https://doi.org/10.3389/fmed.2023.1029198Halli-Tierney AD, Scarbrough C, Carroll D. Polypharmacy: Evaluating Risks and Deprescribing. Am Fam Physician. 2019;100(1):32–38.Hovstadius B, Petersson G. Factors leading to excessive polypharmacy. Clin Geriatr Med. 2012;28(2):159–172. doi: https://doi.org/10.1016/j.cger.2012.01.001Pesante-Pinto JL. Clinical Pharmacology and the Risks of Polypharmacy in the Geriatric Patient. Phys Med Rehabil Clin N Am. 2017;28(4):739–746. doi: https://doi.org/10.1016/j.pmr.2017.06.007Gómez C, Vega-Quiroga S, Bermejo-Pareja F, et al. Polypharmacy in the Elderly: A Marker of Increased Risk of Mortality in a Population-Based Prospective Study (NEDICES). Gerontology. 2015;61(4):301–309. doi: https://doi.org/10.1159/000365328Katzke VA, Bajracharya R, Nasser MI, et al. Number of medically prescribed pharmaceutical agents as predictor of mortality risk: a longitudinal, time-variable analysis in the EPIC-Heidelberg cohort. Sci Rep. 2024;14(1):106. doi: https://doi.org/10.1038/s41598-023-50487-5Kwak MJ, Chang M, Chiadika S, et al. Healthcare Expenditure Associated with Polypharmacy in Older Adults with Cardiovascular Diseases. Am J Cardiol. 2022;169:156–158. doi: https://doi.org/10.1016/j.amjcard.2022.01.012Bezerra HS, Brasileiro Costa AL, Pinto RS, et al. Economic impact of pharmaceutical services on polymedicated patients: A systematic review. Res Social Adm Pharm. 2022;18(9):3492–3500. doi: https://doi.org/10.1016/j.sapharm.2022.03.005Zhou D, Chen Z, Tian F. Deprescribing Interventions for Older Patients: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc. 2023;24(11):1718–1725. doi: https://doi.org/10.1016/j.jamda.2023.07.016Mangin D, Lamarche L, Agarwal G, et al. Team approach to polypharmacy evaluation and reduction: study protocol for a randomized controlled trial. Trials. 2021;22(1):746. doi: https://doi.org/10.1186/s13063-021-05685-9The 2023 American Geriatrics Society Beers Criteria® Update Expert Panel. American Geriatrics Society 2023 updated AGS Beers Criteria® for potentially inappropriate medication use in older adults. J Am Geriatr Soc. 2023;71(7):2052–2081. doi: https://doi.org/10.1111/jgs.18372O’Mahony D, Cherubini A, Guiteras AR, et al. Correction: STOPP/START criteria for potentially inappropriate prescribing in older people: version 3. Eur Geriatr Med. 2023;14(4):633. doi: https://doi.org/10.1007/s41999-023-00812-yBahat G, Ilhan B, Erdogan T, et al. Turkish inappropriate medication use in the elderly (TIME) criteria to improve prescribing in older adults: TIME-to-STOP/TIME-to-START. Eur Geriatr Med. 2020;11(3):491–498. doi: https://doi.org/10.1007/s41999-020-00297-zRenom-Guiteras A, Meyer G, Thürmann PA. The EU(7)-PIM list: a list of potentially inappropriate medications for older people consented by experts from seven European countries. Eur J Clin Pharmacol. 2015;71(7):861–875. doi: https://doi.org/10.1007/s00228-015-1860-9Mühlbauer B. The New PRISCUS List. Dtsch Arztebl Int. 2023;120(1–2):1–2. doi: https://doi.org/10.3238/arztebl.m2022.0408Pazan F, Weiss C, Wehling M; FORTA. Correction to: The FORTA (Fit fOR The Aged) List 2021: Fourth Version of a Validated Clinical Aid for Improved Pharmacotherapy in Older Adults. Drugs Aging. 2022;39(6):485. doi: https://doi.org/10.1007/s40266-022-00954-xFastbom J, Johnell K. National indicators for quality of drug therapy in older persons: the Swedish experience from the first 10 years. Drugs Aging. 2015;32(3):189–199. doi: https://doi.org/10.1007/s40266-015-0242-4Beers MH, Ouslander JG, Rollingher I, et al. Explicit criteria for determining inappropriate medication use in nursing home residents. UCLA Division of Geriatric Medicine. Arch Intern Med. 1991;151(9):1825–1832.Barry PJ, Gallagher P, Ryan C, et al. START (screening tool to alert doctors to the right treatment) — an evidence-based screening tool to detect prescribing omissions in elderly patients. Age Ageing. 2007;36(6):632–638. doi: https://doi.org/10.1093/ageing/afm118Сычев Д.А., Отделёнов В.А., Краснова Н.М., и др. Полипрагмазия: взгляд клинического фармаколога // Терапевтический архив. — 2016. — Т. 88. — № 12. — С. 94–102. [Sychev DA, Otdelеnov VA, Krasnova NM, et al. Polypragmasy: A clinical pharmacologist’s view. Terapevticheskii Arkhiv. 2016;88(12):94–102. (In Russ.)] doi: https://doi.org/10.17116/terarkh2016881294-102Wallerstedt SM, Svensson SA, Lönnbro J, et al. Performance of 3 Sets of Criteria for Potentially Inappropriate Prescribing in Older People to Identify Inadequate Drug Treatment. JAMA Netw Open. 2022;5(10):e2236757. doi: https://doi.org/10.1001/jamanetworkopen.2022.36757Martins C, Godycki-Cwirko M, Heleno B, et al. Quaternary prevention: reviewing the concept. Eur J Gen Pract. 2018;24(1):106–111. doi: https://doi.org/10.1080/13814788.2017.1422177Остроумова О.Д., Черняева М.С., Сычев Д.А. Депрескрайбинг антигипертензивных препаратов у пациентов старших возрастных групп // Рациональная фармакотерапия в кардиологии. — 2020. — Т. 16. — № 1. — С. 82–93. [Ostroumova OD, Cherniaeva MS, Sychev DA. Deprescribing Antihypertensive Drugs in Patients of Older Age Groups. Rational Pharmacotherapy in Cardiology. 2020;16(1):82–93. (In Russ.)] doi: https://doi.org/10.20996/1819-6446-2020-02-14Veronese N, Gallo U, Boccardi V, et al. Efficacy of deprescribing on health outcomes: An umbrella review of systematic reviews with meta-analysis of randomized controlled trials. Ageing Res Rev. 2024:95:102237. doi: https://doi.org/10.1016/j.arr.2024.102237Ibrahim K, Cox NJ, Stevenson JM, et al. A systematic review of the evidence for deprescribing interventions among older people living with frailty. BMC Geriatr. 2021;21(1):258. doi: https://doi.org/10.1186/s12877-021-02208-8Panch T, Szolovits P, Atun R. Artificial intelligence, machine learning and health systems. J Glob Health. 2018;8(2):020303. doi: https://doi.org/10.7189/jogh.08.020303Chew HSJ, Achananuparp P. Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review. J Med Internet Res. 2022;24(1):e32939. doi: https://doi.org/10.2196/32939Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi: https://doi.org/10.1038/s41591-018-0300-7Vo TH, Nguyen NTK, Kha QH, et al. On the road to explainable AI in drug-drug interactions prediction: A systematic review. Comput Struct Biotechnol J. 2022;20:2112–2123. doi: https://doi.org/10.1016/j.csbj.2022.04.021Farmantek. Farmantek rational drug use assistant. 2024. Available from: http://farmantek.herokuapp.com/Medscape. Drug interaction checker. 2024. Available from: https://reference.medscape.com/drug-interactioncheckerWebMD. Drug interaction checker. 2024. Available from: https://www.webmd.com/interaction-checker/default.htmlVademecum Online. Vademecum Api. 2024. Available from: https://www.vademecumonline.com.tr/site/vademecum-apiXiong, G, Yang, Z, Yi, J, et al. DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety. Nucleic Acids Res. 2022;50(D1):D1200–D1207. doi: https://doi.org/10.1093/nar/gkab880UpToDate. Lexicomp drug interaction checker. 2024. Available from: https://www.uptodate.com/contents/searchComputational Biologyand Drug Design Group. DDInter. 2024. Available from: http://ddinter.scbdd.com/Niehoff KM, Rajeevan N, Charpentier PA, et al. Development of the tool to reduce inappropriate medications (TRIM): a clinical decision support system to improve medication prescribing for older adults. Pharmacotherapy. 2016;36(6):694–701. doi: https://doi.org/10.1002/phar.1751MedStopper web site. MedStopper. 2024. Available from: http://www.medstopper.comCanadian Deprescribing Network web site. Canadian Deprescribing network. 2024. Available from: https://www.deprescribingnetwork.ca/Community Interest Company. Prescqipp. 2024. Available from: https://www.prescqipp.info/Working Towards Safer Prescribing. MedSafer. 2024. Available from: https://www.medsafer.org/Шимановский Н.Л., Шегай М.М., Роик Р.О. Можно ли снизить риск развития нежелательных эффектов лекарственных средств с помощью компьютерных технологий (обзор) // Проблемы социальной гигиены, здравоохранения и истории медицины. — 2023. — Т. 31. — № 4. — С. 605–612. [Shimanovskiy NL, Shegai MM, Roik RO. Is possible to decrease the risk of development of undesirable effects of medications applying computer technologies? (a review). Probl Sotsialnoi Gig Zdravookhranenniiai Istor Med. 2023;31(4):605–612. (In Russ.)] doi: http://dx.doi.org/10.32687/0869-866X-2023-31-4-605-612Судаков В.А., Шимановский Н.Л. Снижение риска развития нежелательных эффектов лекарственных средств с помощью искусственного интеллекта // Экспериментальная и клиническая фармакология. — 2023. — Т. 86. — № 11s. — С. 141. [Sudakov VA, Shimanovskij NL. Snizhenie riska razvitiya nezhelatel’nyh effektov lekarstvennyh sredstv s pomoshch’yu iskusstvennogo intellekta. Eksperimental’naya i klinicheskaya farmakologiya. 2023;86(11s):141. (In Russ.)] doi: https://doi.org/10.30906/ekf-2023-86s-141aRieckert A, Reeves D, Altiner A, et al. Use of an electronic decision support tool to reduce polypharmacy in elderly people with chronic diseases: cluster randomised controlled trial. BMJ. 2020;369:m1822. doi: https://doi.org/10.1136/bmj.m1822Doherty AS, Boland F, Moriarty F, et al. Adverse drug reactions and associated patient characteristics in older community-dwelling adults: a 6-year prospective cohort study. Br J Gen Pract. 2023;73(728):e211–e219. doi: https://doi.org/10.3399/BJGP.2022.0181Amorim WW, Passos LC, Gama RS, et al. Using a mobile application to reduce potentially inappropriate prescribing for older Brazilian adults in primary care: a triple-blind randomised clinical trial. BMC Geriatr. 2024;24(1):35. doi: https://doi.org/10.1186/s12877-023-04645-zBakker T, Klopotowska JE, Dongelmans DA, et al. The effect of computerised decision support alerts tailored to intensive care on the administration of high-risk drug combinations, and their monitoring: a cluster randomised stepped-wedge trial. Lancet. 2024;403(10425):439–449. doi: https://doi.org/10.1016/S0140-6736(23)02465-0Segal G, Segev A, Brom A, et al. Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting. J Am Med Inform Assoc. 2019;26(12):1560–1565. doi: https://doi.org/10.1093/jamia/ocz135Rathore A, Sharma R, Bansal P, et al. Knowledge, attitude, and practice of medical interns and postgraduate residents on American Geriatric Society updated Beers criteria. J Educ Health Promot. 2023;12:1. doi: https://doi.org/10.4103/jehp.jehp_769_22Ильина Е.С., Богова О.Т., Савельева М.И., и др. Результаты аудита лекарственных назначений у пациентов старческого возраста с падением в условиях стационара после обучения врачей принципам рациональной фармакотерапии // Фармакология и фармакотерапия. — 2023. — № 4. — С. 20–25. [Ilyina ES, Bogova OT, Savelyeva MI, et al. Results of an audit of drug prescriptions in elderly patients with fall in a hospital after training doctors in the principles of rational pharmacotherapy. Pharmacology & Pharmacotherapy. 2023;4:20–25. (In Russ.)] doi: https://doi.org/10.46393/27132129_2023_4_20