Cargando…

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 mar...

Descripción completa

Detalles Bibliográficos
Autores principales: Sasaki, Eita, Momose, Haruka, Hiradate, Yuki, Furuhata, Keiko, Takai, Mamiko, Asanuma, Hideki, Ishii, Ken J., Mizukami, Takuo, Hamaguchi, Isao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5800680/
https://www.ncbi.nlm.nih.gov/pubmed/29408882
http://dx.doi.org/10.1371/journal.pone.0191896
_version_ 1783298247279771648
author Sasaki, Eita
Momose, Haruka
Hiradate, Yuki
Furuhata, Keiko
Takai, Mamiko
Asanuma, Hideki
Ishii, Ken J.
Mizukami, Takuo
Hamaguchi, Isao
author_facet Sasaki, Eita
Momose, Haruka
Hiradate, Yuki
Furuhata, Keiko
Takai, Mamiko
Asanuma, Hideki
Ishii, Ken J.
Mizukami, Takuo
Hamaguchi, Isao
author_sort Sasaki, Eita
collection PubMed
description 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.
format Online
Article
Text
id pubmed-5800680
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-58006802018-02-23 Modeling for influenza vaccines and adjuvants profile for safety prediction system using gene expression profiling and statistical tools Sasaki, Eita Momose, Haruka Hiradate, Yuki Furuhata, Keiko Takai, Mamiko Asanuma, Hideki Ishii, Ken J. Mizukami, Takuo Hamaguchi, Isao PLoS One Research Article 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. Public Library of Science 2018-02-06 /pmc/articles/PMC5800680/ /pubmed/29408882 http://dx.doi.org/10.1371/journal.pone.0191896 Text en © 2018 Sasaki et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sasaki, Eita
Momose, Haruka
Hiradate, Yuki
Furuhata, Keiko
Takai, Mamiko
Asanuma, Hideki
Ishii, Ken J.
Mizukami, Takuo
Hamaguchi, Isao
Modeling for influenza vaccines and adjuvants profile for safety prediction system using gene expression profiling and statistical tools
title Modeling for influenza vaccines and adjuvants profile for safety prediction system using gene expression profiling and statistical tools
title_full Modeling for influenza vaccines and adjuvants profile for safety prediction system using gene expression profiling and statistical tools
title_fullStr Modeling for influenza vaccines and adjuvants profile for safety prediction system using gene expression profiling and statistical tools
title_full_unstemmed Modeling for influenza vaccines and adjuvants profile for safety prediction system using gene expression profiling and statistical tools
title_short Modeling for influenza vaccines and adjuvants profile for safety prediction system using gene expression profiling and statistical tools
title_sort modeling for influenza vaccines and adjuvants profile for safety prediction system using gene expression profiling and statistical tools
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5800680/
https://www.ncbi.nlm.nih.gov/pubmed/29408882
http://dx.doi.org/10.1371/journal.pone.0191896
work_keys_str_mv AT sasakieita modelingforinfluenzavaccinesandadjuvantsprofileforsafetypredictionsystemusinggeneexpressionprofilingandstatisticaltools
AT momoseharuka modelingforinfluenzavaccinesandadjuvantsprofileforsafetypredictionsystemusinggeneexpressionprofilingandstatisticaltools
AT hiradateyuki modelingforinfluenzavaccinesandadjuvantsprofileforsafetypredictionsystemusinggeneexpressionprofilingandstatisticaltools
AT furuhatakeiko modelingforinfluenzavaccinesandadjuvantsprofileforsafetypredictionsystemusinggeneexpressionprofilingandstatisticaltools
AT takaimamiko modelingforinfluenzavaccinesandadjuvantsprofileforsafetypredictionsystemusinggeneexpressionprofilingandstatisticaltools
AT asanumahideki modelingforinfluenzavaccinesandadjuvantsprofileforsafetypredictionsystemusinggeneexpressionprofilingandstatisticaltools
AT ishiikenj modelingforinfluenzavaccinesandadjuvantsprofileforsafetypredictionsystemusinggeneexpressionprofilingandstatisticaltools
AT mizukamitakuo modelingforinfluenzavaccinesandadjuvantsprofileforsafetypredictionsystemusinggeneexpressionprofilingandstatisticaltools
AT hamaguchiisao modelingforinfluenzavaccinesandadjuvantsprofileforsafetypredictionsystemusinggeneexpressionprofilingandstatisticaltools