Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy

Abstract

Messenger RNA (mRNA) vaccines offer an adaptable and scalable platform for cancer immunotherapy, requiring optimal design to elicit a robust and targeted immune response. Recent advancements in bioinformatics and artificial intelligence (AI) have significantly enhanced the design, prediction, and optimization of mRNA vaccines. This paper reviews technologies that streamline mRNA vaccine development, from genomic sequencing to lipid nanoparticle (LNP) formulation. We discuss how accurate predictions of neoantigen structures guide the design of mRNA sequences that effectively target immune and cancer cells. Furthermore, we examine AI-driven approaches that optimize mRNA-LNP formulations, enhancing delivery and stability. These technological innovations not only improve vaccine design but also enhance pharmacokinetics and pharmacodynamics, offering promising avenues for personalized cancer immunotherapy.

Abstract of "Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy" by Saber İmani, Xiaoyan Li, Keyi Chen et al., https://doi.org/10.3389/fcimb.2024.1501010, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.