Optimisation of Approaches to Adverse Event Analysis in Bioequivalence Clinical Trials

SCIENTIFIC RELEVANCE. The safety assessment of investigational medicinal products is a mandatory step in clinical trials of all phases, including bioequivalence studies. However, there are no approaches providing for the individualised assessment of adverse drug reactions (ADRs), which contributes to the quality of decisions on the safety of pharmacotherapy.

AIM. The study aimed to develop and justify approaches to the individualised assessment of the safety of pharmacotherapy based on quantitative integrative analysis of adverse events (AEs).

MATERIALS AND METHODS. The authors carried out a systematic review of open-access publications and adapted quantitative integrative analysis methods for assessing the safety of pharmacotherapy in clinical trials involving healthy volunteers. The developed methodology is a step-by-step individualised assessment of ADRs, where each case is assigned a certain score and a weight, with subsequent data aggregation to obtain an integrated indicator at the system/organ and organism levels.

RESULTS. The authors developed a five-step procedure for assessing the safety of pharmacotherapy based on quantitative integrative ADR analysis. This procedure involves scoring an AE, converting the score using membership functions, assigning weights, aggregating data to obtain an integrated indicator, and interpreting individual and group indicators. The sequential implementation of the analysis steps in accordance with the proposed procedure makes it possible to assign each volunteer (study subject) to a specific group in accordance with the likelihood of developing AEs. In addition to individual assessment, the article presents an algorithm for interpreting indicators for groups of study subjects, depending on the treatment group (study or comparator medicinal product).

CONCLUSIONS. The described algorithm for converting and presenting integrative AE assessments will improve the reliability and validity of conclusions on the safety of medicinal products, which is important for planning and implementing further clinical development programmes.

1. Polozova EA. Current aspects of risk management in clinical trials. Good Clinical Practice. 2020;(1):45–52 (In Russ.). https://doi.org/10.37489/2588-0519-2020-1-45-52

2. Ushkalova EA, Zyryanov SK, Gopienko IA. Generic drugs: benefit/risk ratio. Neurology, Neuropsychiatry, Psychosomatics. 2021;13(6):98–104 (In Russ.). https://doi.org/10.14412/2074-2711-2021-6-98-104

3. Diligensky NV, Dymova LG, Sevastianov PV. Fuzzy modeling and multicriteria optimization of production systems under uncertainty: technology, economics, ecology. Moscow: Mashinostroenie; 2004 (In Russ.).

4. Podinovsky VV, Potapov MA. Weighted sum method in the analysis of multicriterial decisions: pro et contra. Business-Informatics. 2013;(3):41–8 (In Russ.). EDN: RBWNYH

5. Dmitriev VV. Determination of an integrated indicator of the state of a natural object as a complex system. Society. Environment. Development. 2009;(4):146–65 (In Russ.). EDN: LDGDUB

6. Zadeh LA. The role of soft computing and fuzzy logic in understanding, designing, and developing information/intelligent systems. News of Artificial Intelligence. 2001;(2–3):7–11 (In Russ.). EDN: LHAVBC

7. Berezovskaya IV. Forecasting of medicinal products safety in preclinical toxicological studies. Toxicological Review. 2010;(5):17–22 (In Russ.). EDN: TQUNAB

8. Eremina NV, Kolik LG, Ostrovskaya RU, Durnev AD. Preclinical in vivo neurotoxicity studies of drug candidates. Bulletin of the Scientific Centre for Expert Evaluation of Medicinal Products. 2020;10(3):164–76 (In Russ.). https://doi.org/10.30895/1991-2919-2020-10-3-164-176

9. Poleshchuk OM. About the development of fuzzy information processing systems based on the entire orthogonal semantic spaces. Forest Bulletin. 2003;(1):112–7 (In Russ.). EDN: HVYQBZ

10. Kagan ES. Construction of comprehensive fuzzy evaluations of university activity effectiveness and lecturer activity public formalization. Izvestiya of Altai State University. 2015;(1–1):152–7 (In Russ.). https://doi.org/10.14258/izvasu(2015)1.1-27

11. Georgitsa IV, Kuznetsova AV. Knowledge representation for fuzzy logic recruitment system. Modern Problems of Science and Education. 2015;(1–1):125 (In Russ.). EDN: VIDUVB

12. Fomina EE. Factor analysis and categorial principal component analysis: comparative analysis and practical application for processing of questionnaire survey results. Humanities Bulletin. 2017;(10):3 (In Russ.). https://doi.org/10.18698/2306-8477-2017-10-473

13. Iliassov FN. Scales and specific sociological mea surement. Monitoring of Public Opinion: Economic and Social Changes Journal. 2014;(1):3–16 (In Russ.). EDN: RYFKIP

14. Gorbach AN, Tseytlin NA. Buying behavior: analysis of spontaneous sequences and regression models in marketing research. Kiev: Osvita Ukrainy; 2011 (In Russ.).

15. Harrington E.C. The desirability function. Industrial Quality Control. 1965;21(10):494–8.

16. Poleshchuk OM. Construction of integrated models within the framework of fuzzy expert information. Forest Bulletin. 2003;(5):155–9 (In Russ.). EDN: HVGLNV

17. Poleshchuk OM. Methods for presenting expert information as a set of term-sets of complete orthogonal semantic spaces. Forest Bulletin. 2002;(5):198–216 (In Russ.). EDN: HVYPDT

18. Adler YuP, Markova EV, Granovsky YuV. Planning an experiment in the search for optimal conditions. Moscow: Nauka; 1976 (In Russ.).

19. Dyuyzen EYu. Method of expert assessment: practical guidance. Creative Economics. 2014;(2):24–34 (In Russ.). EDN: RXPBFD

20. Makarova IL. Methods for the analysis of weight coefficients in the integral indicator of public health. Symbol of Science. 2015;(7–1):87–95 (In Russ.). EDN: UCVXVR

21. Sirayeva GI, Kolbin AS, Sergeeva TA, Mishinova SA. Reporting of drug adverse reactions during treatment of COVID-19 in the Russian Federation and the United States. Clinical Pharmacology and Therapy. 2022;31(1):91–6 (In Russ.). EDN: YDXKLG

22. Klyushnikov EV, Shitova EM. Methodological approaches to calculation of integral index, ranking methods. InnoCentre. 2016;(1):4–18 (In Russ.). EDN: YHWMIL