Machine Learning-Assisted Screening of Herbal Medicine Extracts as Vaccine Adjuvants.

Adjuvants, Immunologic / chemistry Adjuvants, Pharmaceutic Adjuvants, Vaccine Animals Cytokines Herbal Medicine Machine Learning Mice Vaccines
adjuvant herbal extracts human machine learning mouse vaccine

Journal

Frontiers in immunology
ISSN: 1664-3224
Titre abrégé: Front Immunol
Pays: Switzerland
ID NLM: 101560960

Informations de publication

Date de publication:
2022
Historique:
received: 03 01 2022
accepted: 30 03 2022
entrez: 6 6 2022
pubmed: 7 6 2022
medline: 9 6 2022
Statut: epublish

Résumé

Adjuvants are important vaccine components, composed of a variety of chemical and biological materials that enhance the vaccine antigen-specific immune responses by stimulating the innate immune cells in both direct and indirect manners to produce a variety cytokines, chemokines, and growth factors. It has been developed by empirical methods for decades and considered difficult to choose a single screening method for an ideal vaccine adjuvant, due to their diverse biochemical characteristics, complex mechanisms of, and species specificity for their adjuvanticity. We therefore established a robust adjuvant screening strategy by combining multiparametric analysis of adjuvanticity

Identifiants

DOI: 10.3389/fimmu.2022.847616 PMID: 35663999 PMC: PMC9160479
pubmed: 35663999
doi: 10.3389/fimmu.2022.847616
pmc: PMC9160479
doi:

Substances chimiques

Adjuvants, Immunologic 0
Adjuvants, Pharmaceutic 0
Adjuvants, Vaccine 0
Cytokines 0
Vaccines 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

847616

Informations de copyright

Copyright © 2022 Hioki, Hayashi, Natsume-Kitatani, Kobiyama, Temizoz, Negishi, Kawakami, Fuchino, Kuroda, Coban, Kawahara and Ishii.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Kou Hioki (K)

Division of Vaccine Science, Department of Microbiology and Immunology, International Vaccine Design Center (vDesC), The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan.
Laboratory of Mockup Vaccine, Center for Vaccine and Adjuvant Research Center (CVAR), National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan.

Tomoya Hayashi (T)

Division of Vaccine Science, Department of Microbiology and Immunology, International Vaccine Design Center (vDesC), The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan.
Laboratory of Mockup Vaccine, Center for Vaccine and Adjuvant Research Center (CVAR), National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan.

Yayoi Natsume-Kitatani (Y)

Laboratory of Bioinformatics, Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan.

Kouji Kobiyama (K)

Division of Vaccine Science, Department of Microbiology and Immunology, International Vaccine Design Center (vDesC), The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan.
Laboratory of Mockup Vaccine, Center for Vaccine and Adjuvant Research Center (CVAR), National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan.

Burcu Temizoz (B)

Division of Vaccine Science, Department of Microbiology and Immunology, International Vaccine Design Center (vDesC), The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan.
Laboratory of Mockup Vaccine, Center for Vaccine and Adjuvant Research Center (CVAR), National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan.

Hideo Negishi (H)

Division of Vaccine Science, Department of Microbiology and Immunology, International Vaccine Design Center (vDesC), The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan.

Hitomi Kawakami (H)

Research Center for Medicinal Plant Resources, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Japan.

Hiroyuki Fuchino (H)

Research Center for Medicinal Plant Resources, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Japan.

Etsushi Kuroda (E)

Department of Immunology, Hyogo College of Medicine, Hyogo, Japan.

Cevayir Coban (C)

Division of Malaria Immunology, Department of Microbiology and Immunology, International Vaccine Design Center (vDesC), The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan.
Immunology Frontier Research Center (IFReC), Osaka University, Osaka, Japan.

Nobuo Kawahara (N)

Research Center for Medicinal Plant Resources, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Japan.

Ken J Ishii (KJ)

Division of Vaccine Science, Department of Microbiology and Immunology, International Vaccine Design Center (vDesC), The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan.
Laboratory of Mockup Vaccine, Center for Vaccine and Adjuvant Research Center (CVAR), National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan.
Immunology Frontier Research Center (IFReC), Osaka University, Osaka, Japan.

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Classifications MeSH

questionsmedicales.fr › Actions chimiques et utilisations › Actions pharmacologiques › Effets physiologiques des médicaments › Facteurs immunologiques › Adjuvants immunologiques › Machine Learning-Assisted Screening of Herbal...
questionsmedicales.fr › Actions chimiques et utilisations › Utilisations spécialisées de produits chimiques › Excipients pharmaceutiques › Adjuvants pharmaceutiques › Machine Learning-Assisted Screening of Herbal...
questionsmedicales.fr › Actions chimiques et utilisations › Utilisations spécialisées de produits chimiques › Excipients pharmaceutiques › Adjuvants pharmaceutiques › Adjuvants vaccinaux › Machine Learning-Assisted Screening of Herbal...
questionsmedicales.fr › Eucaryotes › Animaux › Machine Learning-Assisted Screening of Herbal...
questionsmedicales.fr › Facteurs biologiques › Protéines et peptides de signalisation intercellulaire › Cytokines › Machine Learning-Assisted Screening of Herbal...
questionsmedicales.fr › Professions de santé › Pharmacologie › Science des plantes médicinales › Machine Learning-Assisted Screening of Herbal...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Machine Learning-Assisted Screening of Herbal...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Rodentia › Muridae › Murinae › Souris › Machine Learning-Assisted Screening of Herbal...
questionsmedicales.fr › Mélanges complexes › Produits biologiques › Vaccins › Machine Learning-Assisted Screening of Herbal...