针对快速变异病毒设计广谱疫苗的挑战与进展。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Rishi Bedi, Nicholas L Bayless, Jacob Glanville
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引用次数: 0

摘要

病毒进化以逃避先前的免疫,造成重大的疾病负担。疫苗的有效性随着病原体的突变而恶化,需要重新设计。由于人口增长、全球旅行和农业实践,这个问题变得越来越严重。因此,人们对开发能够减轻疾病严重程度并理想地抑制疾病传播而无需频繁更新的广谱疫苗非常感兴趣。即使在针对快速变异病原体的疫苗有一定效果的情况下,如季节性流感和SARS-CoV-2(严重急性呼吸综合征冠状病毒2),设计针对常规观察到的病毒变异提供广谱免疫的疫苗仍然是一个理想的目标,但尚未实现。这篇综述强调了在理解多态性与疫苗效力之间相互作用方面的关键理论进展,设计广谱疫苗的挑战,以及技术进步和可能的发展途径。我们还讨论了监测疫苗效力和预测病毒从疫苗诱导的保护中逃逸的数据驱动方法。在每种情况下,我们都考虑了流感、SARS-CoV-2和HIV(人类免疫缺陷病毒)疫苗开发中的说明性例子,这三个例子都是高度流行的快速变异病毒,具有不同的系统发育和独特的疫苗技术开发历史。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Challenges and Progress in Designing Broad-Spectrum Vaccines Against Rapidly Mutating Viruses.

Viruses evolve to evade prior immunity, causing significant disease burden. Vaccine effectiveness deteriorates as pathogens mutate, requiring redesign. This is a problem that has grown worse due to population increase, global travel, and farming practices. Thus, there is significant interest in developing broad-spectrum vaccines that mitigate disease severity and ideally inhibit disease transmission without requiring frequent updates. Even in cases where vaccines against rapidly mutating pathogens have been somewhat effective, such as seasonal influenza and SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), designing vaccines that provide broad-spectrum immunity against routinely observed viral variation remains a desirable but not yet achieved goal. This review highlights the key theoretical advances in understanding the interplay between polymorphism and vaccine efficacy, challenges in designing broad-spectrum vaccines, and technology advances and possible avenues forward. We also discuss data-driven approaches for monitoring vaccine efficacy and predicting viral escape from vaccine-induced protection. In each case, we consider illustrative examples in vaccine development from influenza, SARS-CoV-2, and HIV (human immunodeficiency virus)-three examples of highly prevalent rapidly mutating viruses with distinct phylogenetics and unique histories of vaccine technology development.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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