An adjuvant database for preclinical evaluation of vaccines and immunotherapeutics

Adjuvants are immunostimulators used to enhance vaccine efficacy against infectious diseases. However, current methods for evaluating their efficacy and safety are limited, hindering large-scale screening. To address this, we developed a prototype Adjuvant Database (ADB) containing transcriptome data, generated using the same protocols as the widely used Open TG-GATEs (OTG) toxicogenomics database, covering 25 adjuvants across multiple species, organs, time points, and doses. This enabled cross-database integration of ADB and OTG. Transcriptomic patterns successfully distinguished each adjuvant regardless of organs or species. Using both databases, we built machine learning models to predict adjuvanticity and hepatotoxicity. Notably, we identified colchicine's adjuvant activity and FK565's liver toxicity through data-driven analysis. Overall, ADB combined with OTG offers a framework for transcriptomics-based, data-driven screening of adjuvant candidates.

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Publication Details

Journal
Cell chemical biology
Published
2025-08-01
DOI
https://doi.org/10.1016/j.chembiol.2025.07.005
Citations
1
Primary Topic
interferon and immune responses
Type
article
Field-Weighted Citation Impact
0.80
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An adjuvant database for preclinical evaluation of vaccines and immunotherapeutics

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An adjuvant database for preclinical evaluation of vaccines and immunotherapeutics

Daron M. Standley, Yuji Kosugi, Jun‐ichi Ito, Johan Nyström-Persson, Burcu Temizoz, Cevayir Coban, Hiroshi Yamada, Taiki Aoshi, Rodolfo S. Allendes Osorio, Chioko Nagao, Kenji Nakanishi, Satoshi Uematsu, Yoshinobu Igarashi, Etsushi Kuroda, Kenji Mizuguchi, Lokesh P. Tripathi, Ken J. Ishii, Yayoi Natsume‐Kitatani, Yasuhiro Yasutomi, Noriyuki Nakatsu, Kouji Kobiyama, Jun Kunisawa, Hiroshi Kiyono, Isao Hamaguchi, Sho Yamasaki
article en
1 citations

Abstract

Adjuvants are immunostimulators used to enhance vaccine efficacy against infectious diseases. However, current methods for evaluating their efficacy and safety are limited, hindering large-scale screening. To address this, we developed a prototype Adjuvant Database (ADB) containing transcriptome data, generated using the same protocols as the widely used Open TG-GATEs (OTG) toxicogenomics database, covering 25 adjuvants across multiple species, organs, time points, and doses. This enabled cross-database integration of ADB and OTG. Transcriptomic patterns successfully distinguished each adjuvant regardless of organs or species. Using both databases, we built machine learning models to predict adjuvanticity and hepatotoxicity. Notably, we identified colchicine's adjuvant activity and FK565's liver toxicity through data-driven analysis. Overall, ADB combined with OTG offers a framework for transcriptomics-based, data-driven screening of adjuvant candidates.

Cell chemical biologyVol. 32(8)
Hyogo Medical University (JP), National Institute of Infectious Diseases (JP), National Institute of Biomedical Innovation, Health and Nutrition (JP), Chiba University Hospital (JP), Protein Research Foundation (JP), Medidata (United States) (US), Nagasaki University (JP), Eisai (Japan) (JP), Tokyo Metropolitan University (JP), The University of Tokyo (JP), Hyogo University (JP), Tokushima University (JP), Chuo University (JP), The University of Osaka (JP)
Good health and well-being
Openalex Percentile: Top 25%
interferon and immune responses
0.80
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