Conference Abstract | Volume 8, Abstract ELIC2025317 (Poster 036) | Published: 31 Jul 2025
Jeremiah Oluwamayowa Omojuyigbe1,2, &, Timothy Temiloluwa Orimolade1,3
1Faculty of Pharmacy, Obafemi Awolowo University, Ile-Ife, Nigeria, 2Young Researchers Hub, Nigeria, 3University College Hospital, Ibadan, Nigeria
&Corresponding author: Jeremiah Oluwamayowa Omojuyigbe, Faculty of Pharmacy, Obafemi Awolowo University, Ile-Ife, Nigeria, Email: Omojuyigbejeremiah@gmail.com
Received: 25 May 2025, Accepted: 09 Jul 2025, Published: 31 Jul 2025
Domain: Infectious Disease Epidemiology
Keywords: Lassa fever, Vaccine discovery, Computational immunology, Immunoinformatics
©Jeremiah Oluwamayowa Omojuyigbe et al. Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Jeremiah Oluwamayowa Omojuyigbe et al., Harnessing computational immunology to accelerate Lassa fever vaccine discovery: A systematic review. Journal of Interventional Epidemiology and Public Health. 2025;8(ConfProc5):00180. https://doi.org/10.37432/JIEPH-CONFPRO5-00180
Lassa fever (LF), caused by Lassa mammarenavirus (LASV), is a major public health threat in West Africa. No licensed human vaccine exists, and computational immunoinformatics has emerged as a key tool to accelerate vaccine candidate identification. This systematic review aims to assess the role of computational immunological approaches in identifying promising vaccine candidates for LF.
A systematic review was conducted using PubMed, JSTOR, and Google Scholar, covering studies from 2000 to 2024. Studies employing computational techniques such as epitope prediction, molecular docking, molecular dynamics simulations, and immunoinformatics targeting LF vaccine development were included. Data screening and extraction were conducted using Rayyan to minimize bias and ensure consistency. Each study was assessed with a refined 13-criterion framework for methodological quality and risk of bias.
Out of 463 articles screened, four studies met the inclusion criteria; all used immunoinformatic tools for LF vaccine candidate identification. Glycoprotein was targeted in all studies (100%), while 50% also addressed nucleoprotein or other structural proteins. Half of the studies reported multi-epitope constructs, incorporating between 6 and 18 CTL, HTL, and B-cell epitopes. Half of the studies used adjuvants (OmpA or human beta-defensin-3). All studies performed epitope prediction, molecular docking, and antigenicity evaluation. Molecular dynamics simulations were used in 75% of studies, and immune simulations in 50%. Population coverage analysis was conducted in 75% of studies, with up to 97% global HLA allele coverage. Risk-of-bias assessment indicated consistent epitope prediction (low risk) but inconsistent vaccine design (moderate risk) due to variable use of adjuvants and simulations. All studies lacked experimental validation, resulting in a high overall risk of bias.
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