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26 - 05 - 2020 [20:55]

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Новые технологии в области анализа Данных реального мира (RWD): вопросы и перспективы / New Technologies in Real-World Data Analysis (RWD): challenges and potential solutions

DOI: http://dx.doi.org/10.21518/1561-5936-2020-1-2-3-3-9

М.Ю. Самсонов1, Н.О. Погребной2, Е.А. Вольская3

1 «Р-Фарм»; Россия, Москва, 2 Semantic Hub; Россия, Москва, 3 Независимый междисциплинарный Комитет по этической экспертизе клинических исследований

Данные реального мира (ДРМ – Real World Data (RWD)), дополняя традиционные рандомизированные клинические исследования, но не конкурируя с ними, позволяют получить более полное представление об использовании лекарственных средств в повседневной клинической практике. Регуляторные агентства ведущих экономик мира и производители лекарственных средств признают растущий вклад ДРМ в обеспечение понимания жизненного цикла лекарственных препаратов и потребностей пациентов. Данный обзор тенденций развития ДРМ знакомит читателя с многообразием технологических и методологических инноваций, а также потенциальных путей их применения в рамках фармацевтической индустрии.

M.Yu. Samsonov1, N.O. Pogrebnoy2, E.A. Vol’skaya3

1 R-Pharm; Moscow, Russia; 2 Semantic Hub; Moscow, Russia; 3 Independent Interdisciplinary Committee for the Ethical Review of Clinical Research

Real World Data (RWD) offer the possibility to derive a more complete understanding of the use of medicines in everyday clinical practice, complementing rather than competing with evidence from traditional randomized clinical trials. Government regulators of the world’s leading economies and pharmaceutical manufacturers recognize the growing contribution of DRM to understanding of the lifecycle of medicines and patient needs. This overview of DRM development trends provides a thorough introduction to a variety of technological and methodological innovations, as well as potential ways of their application in the pharmaceutical industry.

Список литературы /References

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2. Tremoulet A., Le J., Poindexter B,, Sullivan J.E., et al. Characterization of the population pharmacokinetics of ampicillin in neonates using an opportunistic study design. Antimicrob Agents Chemother. 2014;(58):3013–3020. doi: 10.1128/AAC.02374-13

3. Borrelli F., Izzo A.A. Herb-drug interactions with St John’s wort (Hypericum perforatum): an update on clinical observations. AAPS J. 2009;(11):710–727. doi: 10.1208/s12248-009-9146-8.

4. Kish J.K., Ward M.A., Garofalo D., et al. Real-world evidence analysis of palbociclib prescribing patterns for patients with advanced/metastatic breast cancer treated in community oncology practice in the USA one year post approval. Breast Cancer Res. 2018;(20):37. doi: 10.1186/s13058-018-0958-2.

5. Pacurariu A., Plueschke K., McGettigan P., et al. Electronic healthcare databases in Europe: descriptive analysis of characteristics and potential for use in medicines regulation. BMJ Open. 2018;(8):e023090. doi: 10.1136/bmjopen-2018-023090.

6. Cave А., Kurz Х., Arlett R. Real World data for regulatory decision making: challenges and possible solutions for Europe. Clin Pharmacol Ther. 2019;106(1):36-39. doi: 10.1002/cpt.1426.

7. US Food and Drug Administration. Framework for FDA’s Real-World Evidence Program. December 2018. Available at: https://www.fda.gov/media/120060/download. Accessed September 5, 2019.

8. Вольская Е., Александрова О. Защита персональных данных пациентов. Ремедиум. 2018;(10):6-11. doi: 10.21518/1561–5936–2018–10-6-11. Volskaya E., Aleksandrova O. Protecting personal data respecting patients’ privacy. Remedium. 2018;(10):6-11. (In Russ.) doi: 10.21518/1561–5936–2018–10-6-11.

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13. Fralick M., Kesselheim A.S., Avorn J., Schneeweiss S. Use of Health Care Databases to Support Supplemental Indications of Approved Medications. JAMA Intern Med. 2018;178(1):55-63. doi: 10.1001/jamainternmed.2017.3919.

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16. Liu X., Rivera S.C., Faes L. et al. Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nat Med. 2019;(25):1467–1468. doi: 10.1038/s41591-019-0603-3.

17. HMA. EU Medicines Agencies Network Strategy to 2020 - Working together to improve health. 2015 http://www.ema.europa.eu/docs/ en_GB/document_library/Other/2015/12/WC500199060.pdf.

18. Plueschke K., McGettigan P., Pacurariu A. et al. EU-funded initiatives for real world evidence: descriptive analysis of their characteristics and relevance for regulatory decision-making. BMJ Open. 2018;8(6):e021864. doi: 10.1136/bmjopen-2018-021864.

19. Iiro Toppila. Possibilities of machine learning and artificial intelligence in RWE studies. Future care Finland. 2018; April 8. Available at: https://futurecarefinland.fi/2018/04/possibilities-of-machinelearning-and-artificial-intelligence-in-rwe-studies/.

20. Real World Evidence Solutions Market by Component (RWD, EMR, Claim, Patient Registry, Pharmacy, Service), Application (Oncology, CVD, Neuro, Infection, Regulatory), End User (Pharma, Biotech, Medical Devices, Payers, Healthcare) – Global Forecast to 2024. Meticulous Market Research Pvt. Ltd.; 2019. 166 p. Available at: https://www.researchandmarkets.com/reports/4755940/realworld-evidence-rwe-solutions-market-by.

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24. Stegenga H. Finding answers in the real world. 24 March 2017. Available at: https://www.nice.org.uk/news/blog/findinganswers-in-the-real-world.

25. Bezin J., Duong M., Lassalle R. et al. The national healthcare system claims databases in France, SNIIRAM and EGB: Powerful tools for pharmacoepidemiology. PDS. 2017;26(8):954-962. doi: 10.1002/pds.4233.

26. Cavlan O., Chilukuri S., Evers M., Westra A. Real-world evidence: From activity to impact in healthcare decision making. McKinsey & Company; 2018. Available at: https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/realworld-evidence-from-activity-to-impact-in-healthcare-decisionmaking.

27. https://www.oecd.org/health/ministerial/ministerial-statement-2017.pdf.

Ключевые слова: данные реального мира, доказательства реальной практики, понимание естественного языка, проспективные исследования, структурированные и неструктурированные данные, real world data, practice-based evidence, natural language understanding, prospective studi


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