Annals of the Russian academy of medical sciencesAnnals of the Russian academy of medical sciencesВестник Российской академии медицинских наук0869-60472414-3545"Paediatrician" Publishers LLC1246410.15690/vramn12464PHARMACOLOGY: CURRENT ISSUESАКТУАЛЬНЫЕ ВОПРОСЫ ФАРМАКОЛОГИИ И ФАРМАЦИИResearch ArticleThe Role of Artificial Intelligence in Reducing the Risk of Adverse Reactions in Multiple Drug InteractionsРоль искусственного интеллекта в снижении риска развития побочных реакций при множественных лекарственных взаимодействияхhttps://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.ruhttps://orcid.org/0000-0002-1658-19411614-4760SudakovVladimir A.СудаковВладимир Анатольевич
Russian Federation

PhD in Technical Sciences, Associate Professor

д.т.н., доцент

sudakov@ws-dss.comhttps://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.ruPlekhanov Russian University of EconomicsРоссийский экономический университет имени Г.В. ПлехановаPirogov Russian National Search Medical UniversityРоссийский национальный исследовательский медицинский университет имени Н.И. ПироговаRussian Academy of SciencesРоссийская академия наук150820247932502603011202313052024Copyright ©; 2024, "Paediatrician" Publishers LLCCopyright ©; 2024, Издательство "Педиатръ"2024"Paediatrician" Publishers LLCИздательство "Педиатръ"https://vestnikramn.spr-journal.ru/jour/article/view/12464

The available data on the role of polypragmasia in increasing the frequency of multiple drug interactions, when one drug interacts with two or more other drugs, increasing the risk of side effects associated with them, are considered. The application of network analysis and artificial intelligence to predict the development of clinically significant adverse reactions in conditions of polypharmacotherapy is described. The mechanisms of pharmacodynamic and pharmacokinetic interaction of drugs in the development of adverse reactions are considered and drugs potentially carrying an increased risk in multiple drug interactions are noted. The most dangerous drugs involved in drug interactions were psychotropic drugs, which accounted for about a third of all applicable medicines. The most common serious potential complications associated with this interaction were serotonin syndrome, seizures, QT prolongation, and bleeding. Graph probabilistic models, machine learning models for analyzing reliable sources of medical data, factor models that allow assessing the risks of taking two or more drugs together are proposed. These models are implemented in software and can be implemented in clinical decision support systems. It is concluded that the use of artificial intelligence can reduce the risk of adverse reactions during polypharmacotherapy, especially in elderly patients.

Рассмотрены имеющиеся сведения о роли полипрагмазии в увеличении частоты множественных лекарственных взаимодействий, когда одно лекарственное средство взаимодействует с двумя или более другими лекарственными средствами, увеличивая риск связанных с ними побочных эффектов. Описано применение сетевого анализа и искусственного интеллекта для прогнозирования развития клинически значимых побочных реакций в условиях полифармакотерапии. Рассмотрены механизмы фармакодинамического и фармакокинетического взаимодействия лекарственных средств в развитии побочных реакций и отмечены лекарственные средства, потенциально несущие повышенный риск при множественных лекарственных взаимодействиях. Наиболее опасными препаратами, участвующими в лекарственных взаимодействиях, оказались психотропные средства, на долю которых приходилось около трети всех применимых лекарственных средств. Наиболее распространенными серьезными потенциальными осложнениями, связанными с этим взаимодействием, были серотониновый синдром, судороги, удлинение интервала QT и кровотечение. Предложены графовые вероятностные модели, модели машинного обучения для анализа достоверных источников медицинских данных, факторные модели, позволяющие оценить риски совместного приема двух и более препаратов. Данные модели реализуются в программном обеспечении и могут быть внедрены в системы поддержки принятия клинических решений. Сделан вывод, что применение искусственного интеллекта может снизить риск развития побочных реакций при полифармакотерапии, особенно у пожилых пациентов.

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