Application of in-silico approaches in subunit vaccines: Overcoming the challenges of antigen and adjuvant development.

Journal: Journal of controlled release : official journal of the Controlled Release Society
Published Date: May 10, 2025

Abstract

Subunit vaccines are crucial in preventing modern diseases due to their safety, stability, and ability to elicit targeted immune responses. However, challenges in antigen and adjuvant design hinder their development. Recent advancements in in-silico approaches, including reverse vaccinology, structural vaccinology, and machine learning, have revolutionized vaccine development from empirical practices to rational design approaches. This review summarizes the transformative impact of in-silico approaches on subunit vaccine development. We address the challenges of antigen identification and designation, highlighting how advanced computational techniques are employed to accelerate antigen acquisition. We also examine the challenges in adjuvant discovery and illustrate how machine learning helps overcome these barriers. Finally, we explore potential future directions for subunit vaccines, highlighting the importance of combining computational methods with other technologies to tackle the challenges associated with subunit vaccine development.

Authors

  • Xue Tang
    Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology), Floor 1 Bldg 4, Dongbeilu 1017, Shenzhen 518020, Guangdong, China (C.J., X.T., Y.L., J.G.).
  • Jiayin Deng
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China. Electronic address: mc05824@umac.mo.
  • Chunting He
    Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China.
  • Yanhua Xu
    Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China.
  • Shuting Bai
    Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China.
  • Zhaofei Guo
    Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China.
  • Guangsheng Du
    Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China.
  • Defang Ouyang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Xun Sun
    Guizhou Aerospace Institute of Measuring and Testing Technology, Guiyang 550009, P. R. China.

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