Development of screening method for intranasal influenza vaccine and adjuvant safety in preclinical study.

Recently, many vaccine adjuvants have been developed; however, most of the newly developed adjuvants have been dropped out of preclinical and clinical trials owing to their unexpected toxicity. Thus, the development of highly quantitative and comparable screening methods for evaluating adjuvant safety is needed. In a previous study, we identified specific biomarkers for evaluating the safety of an intranasal influenza vaccine with CpG K3 adjuvant by comparing biomarker expression. We hypothesized that these biomarkers might be useful for screening newly developed adjuvant safety. We compared the expression of biomarkers in mouse lungs by the intranasal administration of 4 types of adjuvants: Alum, Pam3CSK4, NanoSiO2, and DMXAA with subvirion influenza vaccine. The control adjuvant alum did not show any significant increase in biomarker expression or preclinical parameters; however, NanoSiO2 and Pam3CSK4 increased the expression of biomarkers, such as Timp1 and Csf1. DMXAA at 300 µg induced the expression of over 80% of biomarkers. Hierarchical clustering analysis showed that 300 µg DMXAA was classified in the toxicity reference whole-particle influenza vaccine cluster. FACS analysis to confirm specific phenotypes that the number of T cells decreased in DMXAA-treated mouse lungs. Thus, our biomarkers are useful for initial adjuvant safety and toxicity screening.

Evaluation of marker gene expression as a potential predictive marker of leukopenic toxicity for inactivated influenza vaccines.

The leukopenic toxicity test (LTT) is used to evaluate the safety and lot-to-lot consistency of influenza hemagglutinin split vaccine (HAv) and is included in the Japanese Minimum Requirements for Biological Products. LTT assesses the reduced leukocyte levels in murine peripheral blood after HAv administration. However, they require large numbers of animals, and therefore it would be beneficial to develop a more accurate and sensitive alternative method. In this study, we selected biomarkers of leukocyte reduction from 18 previously identified marker genes that were associated with an abnormal toxicity test (ATT). Among these 18 genes, the expressions of 15 marker genes were strongly associated with leukocyte reduction levels. A stepwise single addition multiple regression analysis was used to further extract the genes responsible for leukocyte reduction, with significant (p < 0.25) regression coefficients. The expression of 7 genes significantly predicted the leukocyte reduction. The prediction accuracy of this approach was approximately >90% (mean) for the direct measurement of leukocyte numbers. These results indicate that the expression of these 18 previously identified genes can provide information for both ATT and LTT.

Modeling for influenza vaccines and adjuvants profile for safety prediction system using gene expression profiling and statistical tools.

Historically, vaccine safety assessments have been conducted by animal testing (e.g., quality control tests and adjuvant development). However, classical evaluation methods do not provide sufficient information to make treatment decisions. We previously identified biomarker genes as novel safety markers. Here, we developed a practical safety assessment system used to evaluate the intramuscular, intraperitoneal, and nasal inoculation routes to provide robust and comprehensive safety data. Influenza vaccines were used as model vaccines. A toxicity reference vaccine (RE) and poly I:C-adjuvanted hemagglutinin split vaccine were used as toxicity controls, while a non-adjuvanted hemagglutinin split vaccine and AddaVax (squalene-based oil-in-water nano-emulsion with a formulation similar to MF59)-adjuvanted hemagglutinin split vaccine were used as safety controls. Body weight changes, number of white blood cells, and lung biomarker gene expression profiles were determined in mice. In addition, vaccines were inoculated into mice by three different administration routes. Logistic regression analyses were carried out to determine the expression changes of each biomarker. The results showed that the regression equations clearly classified each vaccine according to its toxic potential and inoculation amount by biomarker expression levels. Interestingly, lung biomarker expression was nearly equivalent for the various inoculation routes. The results of the present safety evaluation were confirmed by the approximation rate for the toxicity control. This method may contribute to toxicity evaluation such as quality control tests and adjuvant development.