Russian pharmacovigilance: ways to improve efficiency

Cover Page


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

The paper presents the results of a survey of pharmacovigilance specialists’ awareness of the regulation of reporting on adverse drug reactions, self-assessment of their competencies and readiness for distance learning, creation of a generalized portrait of a pharmacovigilance specialist to create mechanisms for improving pharmacovigilance activities, and continuing education of pharmacovigilance specialists. The results of the correlation analysis of the knowledge of pharmacovigilance specialists with their self-assessment of their position are reflected. Information-analytical and sociological (survey) methods and descriptive statistics were used. A questionnaire consisting of 31 items was developed for the survey. The first part of the questionnaire consisted of general questions, such as on education, work experience in the pharmaceutical field and pharmacovigilance, and position held. The second part focused on the structure of pharmacovigilance in the organization of holders of the registration certificate. The third part consisted of 17 items aimed at identifying the level of knowledge concerning the immediate daily activities in pharmacovigilance and knowledge of the legislative framework. The final element was a question about the attitude to distance learning. Specialists working in the field of pharmacovigilance at enterprises and persons authorized for pharmacovigilance objectively assessed their practical knowledge and skills in the field of drug safety. Moreover, 42 (72%) respondents believe that they do not need to update their knowledge on pharmacovigilance, whereas 51 (87%) people successfully passed the proposed survey on knowledge of current legislation. Employees of senior positions showed higher knowledge in the field of pharmacovigilance. Specialists and senior pharmacovigilance specialists need to increase their level of professional knowledge, and they are aware of the need for further training.

Full Text

Restricted Access

About the authors

Alexandra A. Taube

Scientific Centre for Expert Evaluation of Medicinal Products

Email: taubeaa@expmed.ru
ORCID iD: 0000-0001-5594-4859
SPIN-code: 7634-4399

candidate of pharmaceutical sciences

Russian Federation, Moscow

Irina Y. Evko

Saint Petersburg University of Chemistry and Pharmacy

Email: irina.Evko@pharminnotech.com
SPIN-code: 9788-4091
Russian Federation, Saint Petersburg

Svetlana V. Sinitova

Saint Petersburg University of Chemistry and Pharmacy

Email: Svetlana.Sinitova@pharminnotech.com
SPIN-code: 9181-9011
Russian Federation, Saint Petersburg

Anatoly E. Krasheninnikov

National Pharmacovigilance Research Center

Email: anatoly.krasheninnikov@drugsafety.ru
ORCID iD: 0000-0002-7791-6071
SPIN-code: 8670-9991
Russian Federation, Moscow

Marina V. Zhuravleva

Scientific Centre for Expert Evaluation of Medicinal Products

Author for correspondence.
Email: zhuravleva@expmed.ru
ORCID iD: 0000-0002-9198-8661
SPIN-code: 6267-9901
Scopus Author ID: 55878917900
Russian Federation, Moscow

Boris K. Romanov

Pirogov Russian National Research Medical University

Email: bkr@yandex.ru
ORCID iD: 0000-0001-5429-9528
SPIN-code: 8453-9166
Russian Federation, Moscow

Renad N. Alyautdin

Scientific Centre for Expert Evaluation of Medicinal Products

Email: Alyautdin@expmed.ru
ORCID iD: 0000-0002-4647-977X
SPIN-code: 1722-1817
Scopus Author ID: 6701792451
ResearcherId: L-9261-2014

doctor of medical sciences, professor

Russian Federation, Moscow

References

  1. Pires C. A systematic review on learning outcomes of pharmacovigilance issues: Undergraduates of pharmacy. Int J Educ Res. 2021;109:101845. doi: 10.1016/j.ijer.2021.101845
  2. Ibrahim H, Abdo A, El Kerdawy AM, Eldin AS. Signal Detection in Pharmacovigilance: A Review of Informatics-driven Approaches for the Discovery of Drug-Drug Interaction Signals in Different Data Sources. Artificial Intelligence in the Life Sciences. 2021;1:100005. doi: 10.1016/j.ailsci.2021.100005
  3. Vilar S, Friedman C, Hripcsak G. Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media. Brief Bioinform. 2018;19(5): 863–877. doi: 10.1093/bib/bbx010
  4. Lee CY, Phoebe Chen Y-P. Prediction of drug adverse events using deep learning in pharmaceutical discovery. Brief Bioinform. 2021;22(2):1884–1901. doi: 10.1093/bib/bbaa040
  5. Ward IR, Wang L, Lu J, et al. Explainable artificial intelligence for pharmacovigilance: What features are important when predicting adverse outcomes? Comput Methods Programs Biomed. 2021;212:106415. doi: 10.1016/j.cmpb.2021.106415
  6. Norén GN, Meldau E-L, Chandler RE. Consensus clustering for case series identification and adverse event profiles in pharmacovigilance. Artificial Intelligence in Medicine. 2021;122:102199. doi: 10.1016/j.artmed.2021.102199
  7. Tissot M, Valnet-Rabier M-B, Stalder T, et al. Epidemiology and economic burden of “serious” adverse drug reactions: Real-world evidence research based on pharmacovigilance data. Therapies. 2022. [In Press]. doi: 10.1016/j.therap.2021.12.007
  8. Peyvandi F, Garagiola I, Mannuccio-Mannucci P. Post-authorization pharmacovigilance for hemophilia in Europe and the USA: Independence and transparency are keys. Blood Rev. 2021;49:100828. doi: 10.1016/j.blre.2021.100828
  9. Tyagi S. Global research output in ‘pharmacovigilance’ during 2010–2020. Therapies. 2021. [In Press]. doi: 10.1016/j.therap.2021.11.011
  10. Mouffak A, Lepelley M, Revol B, et al. High prevalence of spin was found in pharmacovigilance studies using disproportionality analyses to detect safety signals: a meta-epidemiological study. J Clin Epidemiol. 2021;138:73–79. doi: 10.1016/j.jclinepi.2021.06.022
  11. Glagolev SV, Gorelov KV, Chizhova DA. Russian pharmacovigilance in a newly regulated environment: two-year results and prospects. Remedium. 2019;(3):6–14. (In Russ.). doi: 10.21518/1561-5936-2019-3-8-14
  12. Reumerman M, Tichelaar J, Piersma B, et al. Urgent need to modernize pharmacovigilance education in healthcare curricula: review of the literature. Eur J Clin Pharmacol. 2018;74(10): 1235–1248. doi: 10.1007/s00228-018-2500-y
  13. Zhuravleva MV, Romanov BK, Gorodetskaya GI, et al. Topical Issues of Drug Safety, Possibilities of Improving of Pharmacovigilance. Safety and Risk of Pharmacotherapy. 2019;7(3):109–119. (In Russ.).. doi: 10.30895/2312-7821-2019-7-3-109-119
  14. Hartman J, Härmark L, van Puijenbroek E. A global view of undergraduate education in pharmacovigilance. Eur J Clin Pharmacol. 2017;73(7):891–899. doi: 10.1007/s00228-017-2237-z
  15. Matveev AV, Krasheninnikov AE, Matveeva EA, Romanov BK. Differences between the European and Eurasian Good Pharmacovigilance Practices. Safety and Risk of Pharmacotherapy. 2021;9(2):75–84. (In Russ.). doi: 10.30895/2312-7821-2021-9-2-75-84

Supplementary files

Supplementary Files
Action
1. JATS XML
2. Fig. 1. Questionnaire for employees of the pharmacovigilance department of organizations holding registration certificates

Download (1MB)
3. Fig. 2. Distribution of survey participants on the self-assessment of the level of knowledge in the field of pharmacovigilance

Download (65KB)
4. Fig. 3. Division of survey participants’ responses by the presence of a pharmacovigilance department

Download (82KB)
5. Fig. 4. Distribution of survey participants by departments conducting pharmacovigilance activities

Download (67KB)
6. Fig. 5. Determination of survey participants by the number of employees in the departments responsible for pharmacovigilance activities

Download (52KB)
7. Fig. 6. Distribution of survey participants by their positions

Download (86KB)
8. Fig. 7. Frequency of the survey participants’ reference to the Decision of the Eurasian Economic Union № 87

Download (52KB)
9. Fig. 8. Distribution by sections to which survey participants most often refer

Download (347KB)
10. Fig. 9. Distribution of survey participants by test results

Download (73KB)
11. Fig. 10. Distribution of results of the survey participants by their positions

Download (173KB)
12. Fig. 11. Distribution of the survey participants by criteria: confidence in their knowledge/number of points scored

Download (138KB)

Copyright (c) 2022 Taube A.A., Evko I.Y., Sinitova S.V., Krasheninnikov A.E., Zhuravleva M.V., Romanov B.K., Alyautdin R.N.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № ФС 77 - 77762 от 10.02.2020.


This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies