Prospects for the application of individualized integrated analysis of adverse events in vaccine clinical trials

Introduction. This work is a continuation of the study of the safety of drug therapy in clinical trials, within the framework of which a decision-making algorithm based on the quantitative integral analysis of adverse events (AE) was proposed. It is necessary to test the developed algorithm in clinical practice on various groups of drugs. Vaccines for the active prevention of viral hepatitis A were selected for testing. The results obtained can form the basis for assessing the capability of the method in forming a forecast for the development of adverse reactions during vaccination.

Objective. Determination of the effectiveness and promising areas of application of individualized integral analysis of adverse events based on the results of clinical trials using vaccines for the active prevention of viral hepatitis A as an example.

Materials and methods. Individualized integral assessment of AE was performed using an algorithm, the main stages of which are presented in detail in the previous works of the authors, based on the results of clinical studies of the vaccine’s safety and immunogenicity against viral hepatitis A, conducted in accordance with the regulatory and ethical requirements of the Russian Federation. At the stage of determining the weighting factors, an expert survey was conducted using the hierarchy analysis method to assess the importance of individual characteristics of adverse events. Mathematical and statistical analyses of individualized integral assessments were performed using validated software. To solve the problem of assessing the information content and discriminatory significance of gender-age and clinical-laboratory indicators for the purpose of subsequent prediction of the possible development of adverse health deviations, the GDA module — General Discriminant Analysis Models — was used.

Results. In the process of implementing the main stages of the quantitative integral analysis of AE based on the results of a clinical study of vaccines for the prevention of viral hepatitis A at the system-organ and organism levels, data were obtained indicating that there is no need for additional expert assessment or repeated study of this drug. The frequency of adverse reactions for individual organs and systems established in this study was higher than the level for similar vaccines listed in the study protocol. Using discriminant analysis, the information content of individual gender-age and clinical-laboratory indicators obtained during the screening of volunteers was assessed to separate groups of individuals with increased and moderate sensitivity to the introduction of vaccines for the prevention of viral hepatitis A. Only 8 of 37 indicators made a statistically significant contribution to the separation of the analyzed groups. The indicators being studied had low infor- mation content, which indicates insufficient specificity of the features among the screening examination data for predicting the development of adverse health conditions. The overall level of discrimination of the analyzed groups based on the screening examination data of volunteers was approximately 79 %. The proportion of correct classification of the group of individuals with increased sensitivity to drug introduction was 63 %.

Conclusions. The method of quantitative integral analysis of adverse events developed and tested on the example of vaccines for the prevention of viral hepatitis A has shown the potential for its use in identifying individuals with increased individual sensitivity during vaccination based on initial gender-age, demographic, and clinical-laboratory parameters. Further studies are planned to be conducted on pooled data on adverse reactions during the use of vaccines with similar organ-specific and systemic disorders during clinical trials and according to the automated information system "Pharmacovigilance", as well as by expanding the methods of mathematical and statistical data processing, including neural network analysis.

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