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用于癌症免疫治疗的mRNA疫苗设计中的计算生物学与人工智能

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

作者信息

Imani Saber, Li Xiaoyan, Chen Keyi, Maghsoudloo Mazaher, Jabbarzadeh Kaboli Parham, Hashemi Mehrdad, Khoushab Saloomeh, Li Xiaoping

机构信息

Shulan International Medical College, Zhejiang Shuren University, Hangzhou, Zhejiang, China.

Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou, Zhejiang, China.

出版信息

Front Cell Infect Microbiol. 2025 Jan 20;14:1501010. doi: 10.3389/fcimb.2024.1501010. eCollection 2024.

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.

摘要

信使核糖核酸(mRNA)疫苗为癌症免疫治疗提供了一个适应性强且可扩展的平台,需要进行优化设计以引发强大且有针对性的免疫反应。生物信息学和人工智能(AI)的最新进展显著增强了mRNA疫苗的设计、预测和优化。本文综述了从基因组测序到脂质纳米颗粒(LNP)制剂等简化mRNA疫苗开发的技术。我们讨论了新抗原结构的准确预测如何指导有效靶向免疫细胞和癌细胞的mRNA序列设计。此外,我们研究了优化mRNA-LNP制剂、增强递送和稳定性的人工智能驱动方法。这些技术创新不仅改进了疫苗设计,还增强了药代动力学和药效学,为个性化癌症免疫治疗提供了有前景的途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3254/11788159/8ba0865f1249/fcimb-14-1501010-g001.jpg

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