Search Results - "Клиническая фармакология"

Source: World of Medicine : Journal of Biomedical Sciences; Vol. 1 No. 3 (2024): World of Medicine : Journal of Biomedical Sciences; 21-28 ; 2960-9356

Subject Terms: антиагрегантная терапия, клопидогрел, Инстаклоп, клиническая фармакология, безопасность, эффективность

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Relation: https://wom.semanticjournals.org/index.php/biomed/article/view/30/25; https://wom.semanticjournals.org/index.php/biomed/article/view/30

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7

Academic Journal

АСПЕКТЫ ПРИМЕНЕНИЯ ПРЕПАРАТА ИНСТАКЛОП ПЛЮС (КЛОПИДОГРЕЛ+АСПИРИН) ДЛЯ ЛЕЧЕНИЯ И ПРОФИЛАКТИКИ ТРОМБОЗА

Authors: Мидатовна, Шарипова Эльвина

Source: SCIENTIFIC JOURNAL OF APPLIED AND MEDICAL SCIENCES; Vol. 3 No. 4 (2024): AMALIY VA TIBBIYOT FANLARI ILMIY JURNALI; 446-452 ; НАУЧНЫЙ ЖУРНАЛ ПРИКЛАДНЫХ И МЕДИЦИНСКИХ НАУК; Том 3 № 4 (2024): AMALIY VA TIBBIYOT FANLARI ILMIY JURNALI; 446-452 ; 2181-3469

Subject Terms: двойная антиагрегантная терапия, клопидогрел, аспирин, Инстаклоп плюс, клиническая фармакология, безопасность, эффективность

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Relation: https://sciencebox.uz/index.php/amaltibbiyot/article/view/10472/9573; https://sciencebox.uz/index.php/amaltibbiyot/article/view/10472

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8

Academic Journal

Artificial intelligence in pharmacogenetics: A narrative review of current and future applications ; Перспективы использования искусственного интеллекта в фармакогенетических исследованиях: литературный обзор

Authors: M. A. Abdullaev, B. I. Kantemirova, O. A. Bashkina, D. A. Sychev, O. V. Ivanchuk, A. N. Romanova, М. А. Абдуллаев, Б. И. Кантемирова, О. А. Башкина, Д. А. Сычев, О. В. Иванчук, А. Н. Романова

Contributors: Научное исследование выполнено в рамках реализации государственного задания Министерства здравоохранения Российской Федерации «Разработка алгоритмов персонализированного назначения антиагрегантов у пациентов с острым коронарным синдромом» (сроки реализации 2021–2023 гг.).

Source: Acta Biomedica Scientifica; Том 9, № 5 (2024); 12-21 ; 2587-9596 ; 2541-9420

Subject Terms: клиническая фармакология, artificial intelligence, machine learning, pharmacology, pharmacy, clinical pharmacology, искусственный интеллект, машинное обучение, фармакология

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AAPS J. 2022; 24(1): 19. doi:10.1208/s12248-021-00644-3; Krishnaveni C, Arvapalli S, Sharma JV. Artificial intelligence in pharma industry – A review. Int J Innov Pharmaceut Sci Res. 2019; 7(10): 37-50. doi:10.21276/IJIPSR.2019.07.10.506; Garcia-Agundez A, García-Martín E, Eickhoff C. The potential of machine learning in pharmacogenetics, pharmacogenomics and pharmacoepidemiology. Front Pharmacol. 2022; 13: 928527. doi:10.3389/fphar.2022.928527; Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: Progress in machine intelligence for rational drug discovery. Drug Discov Today. 2017; 22(11): 1680-1685. doi:10.1016/j.drudis.2017.08.010; Patel V, Shah M. Artificial intelligence and machine learning in drug discovery and development. Intell Med. 2022; 2(3): 134-140. doi:10.1016/j.imed.2021.10.001; Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021; 26(1): 80-93. doi:10.1016/j.drudis.2020.10.010; Silva P, Jacobs D, Kriak J, Abu-Baker A, Udeani G, Neal G, et al. Implementation of pharmacogenomics and artificial intelligence tools for chronic disease management in primary care setting. J Pers Med. 2021; 11(6): 443. doi:10.3390/jpm11060443; van der Lee M, Swen JJ. Artificial intelligence in pharmacology research and practice. Clin Transl Sci. 2023; 16(1): 31-36. doi:10.1111/cts.13431; Henstock P. Artificial intelligence in pharma: Positive trends but more investment needed to drive a transformation. Arch Pharmacol Therapeutics. 2021; 2(2): 24-28. doi:10.33696/Pharmacol.2.017; Beunk L, Nijenhuis M, Soree B, de Boer-Veger NJ, Buunk AM, Guchelaar HJ, et al. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction between CYP2D6, CYP3A4 and CYP1A2 and antipsychotics. Eur J Hum Genet. 2023; 8(1): 1-8. doi:10.1038/s41431-023-01347-3; Belle DJ, Singh H. Genetic factors in drug metabolism. Am Fam Physician. 2008; 77(11): 1553-1560.; Wang L, McLeod HL, Weinshilboum RM. Genomics and drug response. N Engl J Med. 2011; 364(12): 1144-1153. doi:10.1056/NEJMra1010600; Adam G, Rampášek L, Safikhani Z, Smirnov P, Haibe-Kains B, Goldenberg A. Machine learning approaches to drug response prediction: Challenges and recent progress. NPJ Precis Oncol. 2020; 4(1): 19. doi:10.1038/s41698-020-0122-1; Gerdes H, Casado P, Dokal A, Hijazi M, Akhtar N, Osuntola R, et al. Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs. Nat Commun. 2021; 12(1): 1850. doi:10.1038/s41467-021-22170-8; Chugh H, Singh S. Machine learning applications in rational drug discovery. Drug Design Using Machine Learning. 2022: 97-116. doi:10.1002/9781394167258.ch3; Roche-Lima A, Roman-Santiago A, Feliu-Maldonado R, Rodriguez-Maldonado J, Nieves-Rodriguez BG, Carrasquillo-Carrion K, et al. Machine learning algorithm for predicting warfarin dose in Caribbean Hispanics using pharmacogenetic data. Front Pharmacol. 2020; 10: 1550. doi:10.3389/fphar.2019.01550; Cosgun E, Limdi NA, Duarte CW. High-dimensional pharmacogenetic prediction of a continuous trait using machine learning techniques with application to warfarin dose prediction in African Americans. Bioinformatics. 2011; 27(10): 1384-1389. doi:10.1093/bioinformatics/btr159; International Warfarin Pharmacogenetics Consortium. Estimation of the warfarin dose with clinical and pharmacogenetic data. N Engl J Med. 2009; 360(8): 753-764. doi:10.1056/NEJMoa0809329; Johnson JA, Caudle KE, Gong L, Whirl‐Carrillo M, Stein CM, Scott SA, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for pharmacogenetics‐guided warfarin dosing: 2017 update. Clin Pharmacol Therapeutics. 2017; 102(3): 397-404. doi:10.1002/cpt.668; Asiimwe IG, Zhang EJ, Osanlou R, Jorgensen AL, Pirmohamed M. Warfarin dosing algorithms: A systematic review. Br J Clin Pharmacol. 2021; 87(4): 1717-1729. doi:10.1111/bcp.14608; Ren Y, Yang C, Chen H, Dai D, Wang Y, Zhu H, et al. Pharmacogenetic-guided algorithm to improve daily dose of warfarin in elder Han-Chinese population. Front Pharmacol. 2020; 11: 1014. doi:10.3389/fphar.2020.01014; Carlquist JF, Anderson JL. Using pharmacogenetics in real time to guide warfarin initiation: A clinician update. Circulation. 2011; 124(23): 2554-2559. doi:10.1161/CIRCULATIONAHA.111.019737; Yang T, Zhou Y, Chen C, Lu M, Ma L, Cui Y. Genotype‐guided dosing versus conventional dosing of warfarin: A meta‐analysis of 15 randomized controlled trials. J Clin Pharm Ther. 2019; 44(2): 197-208. doi:10.1111/jcpt.12782; Pirmohamed M, Burnside G, Eriksson N, Jorgensen AL, Toh CH, Nicholson T, et al. A randomized trial of genotypeguided dosing of warfarin. N Engl J Med. 2013; 369: 2294-22303. doi:10.1056/NEJMoa1311386; Li X, Li D, Wu JC, Liu ZQ, Zhou HH, Yin JY. Precision dosing of warfarin: Open questions and strategies. Pharmacogenomics J. 2019; 19(3): 219-229. doi:10.1038/s41397-019-0083-3; Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol. 2017; 2(4): 230-243. doi:10.1136/svn-2017-000101; Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019; 40(8): 577-591. doi:10.1016/j.tips.2019.05.005; Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine learning methods in drug discovery. Molecules. 2020; 25(22): 5277. doi:10.3390/molecules25225277; Lin CC, Wang YC, Chen JY, Liou YJ, Bai YM, Lai IC, et al. Artificial neural network prediction of clozapine response with combined pharmacogenetic and clinical data. Comput Methods Programs Biomed. 2008; 91(2): 91-99. doi:10.1016/j.cmpb.2008.02.004; Chiu YC, Chen HI, Zhang T, Zhang S, Gorthi A, Wang LJ, et al. Predicting drug response of tumors from integrated genomic profiles by deep neural networks. BMC Med Genomics. 2019; 12(Suppl 1): 143-155. doi:10.1186/s12920-018-0460-9; Russell LE, Zhou Y, Almousa AA, Sodhi JK, Nwabufo CK, Lauschke VM. Pharmacogenomics in the era of next generation sequencing – From byte to bedside. Drug Metab Rev. 2021; 53(2): 253-278. doi:10.1080/03602532.2021.1909613; Chiu YC, Chen HI, Gorthi A, Mostavi M, Zheng S, Huang Y, et al. Deep learning of pharmacogenomics resources: Moving towards precision oncology. Brief Bioinform. 2020; 21(6): 2066-2083. doi:10.1093/bib/bbz144; Hertz DL, Ramsey LB, Gopalakrishnan M, Leeder JS, Van Driest SL. Analysis approaches to identify pharmacogenetic associations with pharmacodynamics. Clin Pharmacol Ther. 2021; 110(3): 589-594. doi:10.1002/cpt.2312; Yeh CH, Chou YJ, Tsai TH, Hsu PW, Li CH, Chan YH, et al. Artificial-intelligence-assisted discovery of genetic factors for precision medicine of antiplatelet therapy in diabetic peripheral artery disease. Biomedicines. 2022; 10(1): 116. doi:10.3390/biomedicines10010116; Mega JL, Close SL, Wiviott SD, Shen L, Hockett RD, Brandt JT, et al. Cytochrome p-450 polymorphisms and response to clopidogrel. N Engl J Med. 2009; 360(4): 354-362. doi:10.1056/nejmoa0809171; Тарасочкина Д.С., Полунина Е.А., Севостьянова И.В., Воронина Л.П., Кантемирова Б.И. Взаимосвязи уровня фракталкина и показателей эхокардиоскопии при артериальной гипертензии, стенокардии напряжения и их сочетании. Кубанский научный медицинский вестник. 2015; (4): 119-123.; Сычев Д.А., Шуев Г.Н., Торбенков Е.С., Адриянова М.А. Персонализированная медицина: взгляд клинического фармаколога. Consilium Medicum. 2017; 19(1): 61-68.; Sahu A, Mishra J, Kushwaha N. Artificial intelligence (AI) in drugs and pharmaceuticals. Comb Chem High Throughput Screen. 2022; 25(11): 1818-1837. doi:10.2174/1386207325666211207153943; Roosan D, Chok J, Baskys A, Roosan MR. PGxKnow: A pharmacogenomics educational HoloLens application of augmented reality and artificial intelligence. Pharmacogenomics. 2022; 23(4): 235-245. doi:10.2217/pgs-2021-0120; Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019; 17: 1-9. doi:10.1186/s12916-019-1426-2; Wang F, Preininger A. AI in health: State of the art, challenges, and future directions. Yearb Med Inform. 2019; 28(1): 16-26. doi:10.1055/s-0039-1677908; Auwerx C, Sadler MC, Reymond A, Kutalik Z. From pharmacogenetics to pharmaco-omics: Milestones and future directions. HGG Adv. 2022; 3(2): 100100. doi:10.1016/j.xhgg.2022.100100; Arabi AA. Artificial intelligence in drug design: Algorithms, applications, challenges and ethics. Fut Drug Discov. 2021; 3(2): FDD59. doi:10.4155/fdd-2020-0028; https://www.actabiomedica.ru/jour/article/view/5029

Availability: https://www.actabiomedica.ru/jour/article/view/5029
https://doi.org/10.29413/ABS.2024-9.5.2

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9

Academic Journal

ОСОБЕННОСТИ ФАРМАЦЕВТИЧЕСКОЙ ОПЕКИ И СПРОСА ЛЕКАРСТВЕННЫХ ПРЕПАРАТОВ ПРИ КАШЛЕ

Subject Terms: pharmaceutical counselling and information, клиническая фармакология, students, фармацевтическое консультирование и информирование, анкетирование, студенты, clinical pharmacology, questionnaires, 3. Good health

10

Academic Journal

Use of unlicensed drugs and off-label drug use: focus on COVID-19

Authors: S. A. Mishinova, A. A. Zhuravkov, V. K. Zhuravko

Source: Качественная клиническая практика, Vol 0, Iss 4S, Pp 120-129 (2020)

Subject Terms: клиническая фармакология, расширенный доступ, реальная клиническая практика, 3. Good health, RS1-441, жизнеугрожающие заболевания, 03 medical and health sciences, Pharmacy and materia medica, 0302 clinical medicine, covid-19, Medical technology, off-label назначение, нежелательные лекарственные реакции, R855-855.5

Access URL: https://www.clinvest.ru/jour/article/download/542/550
https://doaj.org/article/2e28d4f3fcf94b8ebdc51bd2fb4d1ca2
https://www.clinvest.ru/jour/article/view/542
https://www.clinvest.ru/jour/article/download/542/550
https://cyberleninka.ru/article/n/naznachenie-ofitsialno-ne-razreshyonnyh-k-primeneniyu-lekarstvennyh-sredstv-i-ispolzovanie-lekarstvennyh-sredstv-s-narusheniem
https://cyberleninka.ru/article/n/naznachenie-ofitsialno-ne-razreshyonnyh-k-primeneniyu-lekarstvennyh-sredstv-i-ispolzovanie-lekarstvennyh-sredstv-s-narusheniem/pdf

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11

Academic Journal

Clinical Pharmacology in Health Care, Teaching and Research

Authors: Article Editorial

Source: Качественная клиническая практика, Vol 0, Iss 2S, Pp 7-66 (2020)

Subject Terms: рациональное использование лекарств, фармаконадзор, гериатрия, 03 medical and health sciences, Pharmacy and materia medica, 0302 clinical medicine, перечень важнейших лекарств, Medical technology, персонализированная медицина, взаимозаменяемость, R855-855.5, фармакоэпидемиология, клиническая фармакология это, фармакодинамика, фармакоэкономика, биопрепараты, крайний возраст, биоаналоги, 3. Good health, RS1-441, клинические исследования, фармакогенетика, дженерики, клинический фармаколог, фармакокинетика, терапевтический лекарственный мониторинг, педиатрия

Access URL: https://www.clinvest.ru/jour/article/download/498/502
https://doaj.org/article/00785766e6e547099a3718dd6333d3c5
https://www.clinvest.ru/jour/article/download/498/501
https://www.clinvest.ru/jour/article/view/498
https://www.clinvest.ru/jour/article/download/498/502

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12

Academic Journal

ФАРМАЦЕВТИЧЕСКОЕ КОНСУЛЬТИРОВАНИЕ И ИНФОРМИРОВАНИЕ В АСПЕКТЕ АПТЕЧНОЙ ПРАКТИКИ СТУДЕНТОВ

Subject Terms: pharmaceutical counselling and information, клиническая фармакология, students, 4. Education, questionnaire, фармацевтическое консультирование и информирование, анкетирование, студенты, clinical pharmacology, 3. Good health

13

Academic Journal

РЕАЛЬНОСТЬ ПРАКТИКИ ФАРМАЦЕВТИЧЕСКОГО КОНСУЛЬТИРОВАНИЯ

Subject Terms: клиническая фармакология, фармацевтическое консультирование, самолечение, фармацевтическое информирование, 3. Good health

14

Academic Journal

Quantitative clinical pharmacology and patient-centered healthcare technologies: perspectives 2030 ; Количественная клиническая фармакология и пациент-ориентированные технологии здравоохранения: перспективы 2030

Authors: V. I. Petrov, B. E. Tolkachev, В. И. Петров, Б. Е. Толкачев

Contributors: The study was performed without external funding., Работа выполнена без спонсорской поддержки.

Source: Regulatory Research and Medicine Evaluation; Том 12, № 2 (2022); 205-213 ; Регуляторные исследования и экспертиза лекарственных средств; Том 12, № 2 (2022); 205-213 ; 3034-3453 ; 3034-3062

Subject Terms: оценка технологий здравоохранения, population pharmacokinetic modelling, precision medicine, clinical pharmacology, health technology assessment, популяционное фармакокинетическое моделирование, прецизионная медицина, клиническая фармакология

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Relation: https://www.vedomostincesmp.ru/jour/article/view/430/774; https://www.vedomostincesmp.ru/jour/article/downloadSuppFile/430/252; Клиническая фармакология в здравоохранении, образовании и науке. Качественная клиническая практика. 2020;(2S):7–66. https://doi.org/10.37489/2588-0519-2020-S2; Mentré F, Friberg LE, Duffull S, French J, Lauffenburger DA, Lang Li, et al. Pharmacometrics and systems pharmacology 2030. Clin Pharmacol Ther. 2020;107(1):76–8. https://doi.org/10.1002/cpt.1683; Pacanowski M, Liu Q. Precision Medicine 2030. Clin Pharmacol Ther. 2020;107(1):62–4. https://doi.org/10.1002/cpt.1675; Denny JC, Collins FS. Precision medicine in 2030—seven ways to transform healthcare. Cell. 2021;184(6):1415–9. https://doi.org/10.1016/j.cell.2021.01.015; Allegaert K, Flint R, Smits A. Pharmacokinetic modelling and Bayesian estimation-assisted decision tools to optimize vancomycin dosage in neonates: only one piece of the puzzle. Expert Opin Drug Metab Toxicol. 2019;15(9):735–49. https://doi.org/10.1080/17425255.2019.1655540; Mehrotra N, Bhattaram A, Earp JC, Florian J, Krudys K, Lee JE, et al. Role of quantitative clinical pharmacology in pediatric approval and labeling. Drug Metab Dispos. 2016;44(7):924–33. https://doi.org/10.1124/dmd.116.069559; Holford N, Karlsson MO. Time for quantitative clinical pharmacology: a proposal for a pharmacometrics curriculum. Clin Pharmacol Ther. 2007;82(1): 103–5. https://doi.org/10.1038/sj.clpt.6100231; Jean D, Naik K, Milligan L, Hall S, Huang SM, Isoherranen N, et al. Development of best practices in physiologically based pharmacokinetic modeling to support clinical pharmacology regulatory decision-making—A workshop summary. CPT: Pharmacometrics Syst Pharmacol. 2021;10(11):1271–5. https://doi.org/10.1002/psp4.12706; Brouwer KLR, Schmidt S, Floren LC, Johnson JA. Clinical pharmacology education — the decade ahead. Clin Pharmacol Ther. 2020;107(1):37–9. https://doi.org/10.1002/cpt.1652; Van Driest SL, Choi L. Real-world data for pediatric pharmacometrics: can we upcycle clinical data for research use? Clin Pharmacol Ther. 2019;106(1):84–6. https://doi.org/10.1002/cpt.1416; Venkatakrishnan K, Benincosa LJ. Diversity and inclusion in drug development: rethinking intrinsic and extrinsic factors with patient centricity. Clin Pharmacol Ther. Published online September 22, 2021. https://doi.org/10.1002/cpt.2416; Swift B, Jain L, White C, Chandrasekaran V, Bhandari A, Hughes DA, Jadhav PR. Innovation at the intersection of clinical trials and real-world data science to advance patient care. Clin Transl Sci. 2018;11(5):450–60. https://doi.org/10.1111/cts.12559; Shahin MH, Abdel-Rahman S, Hartman D, Johnson JA, Mitchell DY, Reynolds KS, et al. The patient-centered future of clinical pharmacology. Clin Pharmacol Ther. 2020;107(1):72–5. https://doi.org/10.1002/cpt.1681; Hill-McManus D, Marshall S, Liu J, Willke RJ, Hughes DA. Linked pharmacometric-pharmacoeconomic modeling and simulation in clinical drug development. Clin Pharmacol Ther. 2021;110(1):49–63. https://doi.org/10.1002/cpt.2051; https://www.vedomostincesmp.ru/jour/article/view/430

Availability: https://www.vedomostincesmp.ru/jour/article/view/430
https://doi.org/10.30895/1991-2919-2022-12-2-205-213

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15

Academic Journal

ИССЛЕДОВАНИЕ НЕОБХОДИМОСТИ ПРЕПОДАВАНИЯ И ВЫБОРА ФОРМЫ ПОДАЧИ ЛЕКЦИОННОГО МАТЕРИАЛА НА КУРСЕ КЛИНИЧЕСКОЙ ФАРМАКОЛОГИИ

Subject Terms: клиническая фармакология, lecture, students, 4. Education, questionnaire, анкетирование, студенты, clinical pharmacology, лекция

16

Academic Journal

The Use of Biotechnological Drugs in Pediatrics on the Example of Monoclonal Antibodies: Clinical Pharmacology View ; Применение биотехнологических лекарственных средств в педиатрии на примере моноклональных антител: взгляд с позиции клинической фармакологии

Authors: Alexey S. Kolbin, Liudmila I. Yemelyanova, А. С. Колбин, Л. И. Емельянова

Contributors: Not specified, Не указан

Source: Pediatric pharmacology; Том 18, № 4 (2021); 304-313 ; Педиатрическая фармакология; Том 18, № 4 (2021); 304-313 ; 2500-3089 ; 1727-5776

Subject Terms: дозирование, mAb, clinical pharmacology, children, dosing, monoclonal antibody, клиническая фармакология, дети

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