AUTHOR=Ji Yuhyun , Bekkari Kavitha , Patel Ruchin , Shardar Mohammed , Walford Geoffrey A. , Kim SamMoon , Liu Yaping , Read-Button Willis , Tracy Kristina , Kriss Jennifer , Barr Colleen , Wolfle Marissa , Kummar Shailaa , LaPorta Celia , Radnoff Madison , Ghodasara Milan , Xiong Jian , Smith William J. , Bakshi Kunal , Sullivan Nicole L. , Murgolo Nicholas 

TITLE=Machine learning insights into vaccine adjuvants and immune outcomes

JOURNAL=Frontiers in Immunology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1654060

DOI=10.3389/fimmu.2025.1654060

ISSN=1664-3224

ABSTRACT=Adjuvants boost the immune response to vaccine antigens, serving as key components in safe and effective vaccines. However, selecting a suitable adjuvant for a new vaccine can be challenging. This is due to the wide variety of adjuvants and the many mechanisms of vaccines they are meant to enhance. Therefore, the adjuvant selection process heavily relies on empirical experiments, which are time-consuming and resource-intensive. In this study, we introduce a machine learning approach leveraging non-human primate RNA transcriptomic data to predict immunogenic antibody levels after vaccination. Furthermore, analysis of the trained deep learning models enabled the identification of immune response mechanisms that are stimulated by adjuvants. Integration of machine learning has the potential to expedite vaccine adjuvant selection by focusing on evaluating adjuvant candidates with the highest probability of success. This may ultimately facilitate the development of more effective vaccines.