Eco-Friendly Sustainable Vaccine Designing Technology Aiding Personalised Immunotherapy Medicinal Chemistry
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Abstract
With the rise of pandemic and endemic cases in recent years, vaccination has been prioritised along with prophylactic therapies. The development of a vaccine candidate sometimes requires lengthy timelines of research and resources. Apart from the unpredictable side effects experienced by individuals, most of the vaccination efforts suffer from population coverage and serotype specificity. Also, lots of euthanised mammals are required for evaluating lethal doses and pharmacokinetic parameters. Worldwide genetic diversity for various human leukocyte antigens (HLAs) or alleles is one of the reasons for zone-specific clinical trials. In these cases, rigorous studies with healthy subjects are required to report statistically reasonable data. In the era of reverse vaccinology, it is now possible to predict immunoproteasomal cleavages of an antigen (upon cellular uptake) and possible nonamers produced from it. These nonamers can be identified for their allergenic, pro-inflammatory characteristics utilising machine learning (ML) and deep learning (DL) algorithms, integrating AI (Artificial Neural Network) driven epitope prediction with HLA-specific binding analyses. These computational tools reduce reliance on animal trials by simulating epitope interactions in-silico, addressing sustainable goals in peptide vaccine development for global populations. This review aims to summarise a computational workflow that can lead to an antigenic peptide as a protective immunogen in individuals.
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