Leveraging artificial intelligence in vaccine development: A narrative review.

Autor: Olawade DB; Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom. Electronic address: d.olawade@uel.ac.uk., Teke J; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom., Fapohunda O; Department of Chemistry and Biochemistry, University of Arizona, USA., Weerasinghe K; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom., Usman SO; Department of Systems and Industrial Engineering, University of Arizona, USA., Ige AO; Department of Chemistry, Faculty of Science, University of Ibadan, Ibadan, Nigeria., Clement David-Olawade A; Endoscopy Department, University Hospitals of Leicester NHS Trust. Leicester, United Kingdom.
Jazyk: angličtina
Zdroj: Journal of microbiological methods [J Microbiol Methods] 2024 Sep; Vol. 224, pp. 106998. Date of Electronic Publication: 2024 Jul 15.
DOI: 10.1016/j.mimet.2024.106998
Abstrakt: Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE