[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119586-en":3,"doc-seo-119586-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119586,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning insights into vaccine adjuvants and immune outcomes","The study addresses the difficulty of selecting vaccine adjuvants for new vaccines, given the wide range of adjuvant types and their diverse mechanisms that shape immune responses. An integrated machine learning framework is developed using non-human primate RNA transcriptomic data to predict vaccine-induced immunogenic antibody levels. Deep learning model interpretation further identifies immune response mechanisms triggered by specific adjuvants. The approach is proposed to accelerate adjuvant screening by prioritizing candidates with higher probabilities of success, supporting development of more effective vaccines.","TYPE Original Research PUBLISHED 07 October 2025  \nDOI 10.3389/fimmu.2025.1654060  \nOPEN ACCESS  \nEDITED BY  \nAlejandro Parola,  \nNational University of Quilmes, Bernal, Argentina  \nREVIEWED BY  \nSaranya K R,  \nTiruchirapalli Campus, India Suganya Y,  \nSRM Institute of Science and Technology, India  \n*CORRESPONDENCE  \nYuhyun Ji  \n[yhji8866@gmail.com](yhji8866@gmail.com)[ ](yhji8866@gmail.com)Nicole L. Sullivan  \n [nicole.sullivan@merck.com](nicole.sullivan@merck.com)[ ](nicole.sullivan@merck.com)Nicholas Murgolo  \n [nicholas.murgolo@merck.com](nicholas.murgolo@merck.com)  \nRECEIVED 25 June 2025  \nACCEPTED 08 August 2025  \nPUBLISHED 07 October 2025  \nCITATION  \nJi Y, Bekkari K, Patel R, Shardar M,  \nWalford GA, Kim S, Liu Y, Read-Button W, Tracy K, Kriss J, Barr C, Wolfle M, Kummar S, LaPorta C, Radnoff M, Ghodasara M, Xiong J, Smith WJ, Bakshi K, Sullivan NL and Murgolo N (2025) Machine learning insights into vaccine adjuvants and immune outcomes.  \nFront. Immunol. 16:1654060 .  \ndoi: 10.3389/fimmu.2025.1654060  \nCOPYRIGHT  \n© 2025 Ji, Bekkari, Patel, Shardar, Walford, Kim, Liu, Read-Button, Tracy, Kriss, Barr, Wolfle, Kummar, LaPorta, Radnoff, Ghodasara, Xiong, Smith, Bakshi, Sullivan and Murgolo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning insights into vaccine adjuvants and immune outcomes  \nYuhyun Ji*, Kavitha Bekkari, Ruchin Patel, Mohammed Shardar, Geoffrey A. Walford, SamMoon Kim, Yaping Liu,  \nWillis Read-Button, Kristina Tracy, Jennifer Kriss, Colleen Barr, Marissa Wolfle, Shailaa Kummar, Celia LaPorta,  \nMadison Radnoff, Milan Ghodasara, Jian Xiong, William J. Smith, Kunal Bakshi, Nicole L. Sullivan* and Nicholas Murgolo*  \nMerck & Co., Inc., Rahway, NJ, United States  \nAdjuvants 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.  \nKEYWORDS  \nmachine learning, deep learning, artificial intelligence, adjuvant, vaccine, RNA transcriptomics, antibody titers, immune response  \n1 Introduction  \nAdjuvants are immune-boosting substances that are added to vaccines to enhance their immunogenicity and stimulate a more robust and long-lasting immune response (1–3) . The use of adjuvants in vaccines has played a significant role in enhancing vaccine efficacy, reducing the required antigen dose, and improving the overall effectiveness of immunization programs (4–6) . Among the different adjuvants that have been developed and utilized in vaccines, alum, monophosphoryl lipid A (MPL), and immunostimulating complexes (ISCOMs) are popular and have shown promising results in boosting immune  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nresponses (1, 3, 7) . The primary aspects of adjuva","cbCaif9GR6zyU9sM","https://ap.wps.com/l/cbCaif9GR6zyU9sM","pdf",26063504,1,16,"English","en",105,"# Introduction\n## Adjuvants and vaccine immune enhancement\n## Challenges in adjuvant selection\n## Machine learning approach and study goals","[{\"question\":\"Why is selecting a vaccine adjuvant challenging?\",\"answer\":\"Adjuvant selection is difficult because many adjuvants exist and each can act through multiple mechanisms that they are meant to enhance. This complexity makes predicting immune outcomes difficult and often relies on time- and resource-intensive experiments.\"},{\"question\":\"What data and modeling approach does the study use to predict immune outcomes?\",\"answer\":\"The study uses non-human primate RNA transcriptomic data and introduces a machine learning approach to predict immunogenic antibody levels after vaccination. Deep learning models are trained to learn relationships between RNA signatures and immune outcomes.\"},{\"question\":\"How do the authors identify immune response mechanisms linked to adjuvants?\",\"answer\":\"After training, the study analyzes the deep learning models to identify immune response mechanisms stimulated by adjuvants. This provides interpretive insight beyond predicting antibody titers.\"}]","Machine learning insights into vaccine adjuvants and immune outcomes | PDF",1785725127,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-insights-into-vaccine-adjuvants-and-immune-outcomes","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-insights-into-vaccine-adjuvants-and-immune-outcomes/119586/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is selecting a vaccine adjuvant challenging?","Question",{"text":75,"@type":76},"Adjuvant selection is difficult because many adjuvants exist and each can act through multiple mechanisms that they are meant to enhance. This complexity makes predicting immune outcomes difficult and often relies on time- and resource-intensive experiments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and modeling approach does the study use to predict immune outcomes?",{"text":80,"@type":76},"The study uses non-human primate RNA transcriptomic data and introduces a machine learning approach to predict immunogenic antibody levels after vaccination. Deep learning models are trained to learn relationships between RNA signatures and immune outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the authors identify immune response mechanisms linked to adjuvants?",{"text":84,"@type":76},"After training, the study analyzes the deep learning models to identify immune response mechanisms stimulated by adjuvants. 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