计算机模拟助力现代疫苗研发的挑战与进展

    Challenges and Progress of Computer Simulation in Aiding Modern Vaccine Development

    • 摘要:
      目的  探讨传统疫苗研发面临的核心挑战,分析计算机模拟技术在应对这些挑战、优化疫苗研发流程中的应用价值与具体实践,为现代疫苗研发提供理论参考和方法学支持。
      方法 结合近期美国食品药品监督管理局和中国批准的代表性疫苗案例,系统综述计算机模拟技术(包括分子对接、人工智能/机器学习、系统生物学及多组学整合等)在疫苗抗原筛选与设计、佐剂开发与优化、免疫原性及保护力预测等关键环节的具体策略与应用路径。
      结果 计算机模拟技术可显著缩短疫苗研发周期,提升候选抗原与佐剂筛选的准确性,增强对快速变异病原体的应对能力,并为疫苗生产的质量控制和检测方法开发提供数据支持。实际疫苗案例表明,该技术能够有效提高疫苗保护率、优化免疫程序并推动广谱疫苗设计。
      结论 计算机模拟已成为现代疫苗研发从“经验驱动”转向“数据-模型双驱动”的核心支撑,但其应用仍受数据质量参差不齐,模型跨物种外推能力有限,闭环迭代延迟等瓶颈制约;未来需通过多源数据整合、可解释人工智能模型开发及自动化实验平台建设,进一步强化“计算-实验”协同,为应对病毒变异与新发传染病提供更精准、高效的疫苗研发方案。

       

      Abstract:
      OBJECTIVE  To explore the core challenges faced by traditional vaccine development, analyze the application value and specific practices of computer simulation technology in addressing these challenges and optimizing the vaccine research and development(R&D) process, and provide theoretical references and methodological support for modern vaccine R&D.
      METHODS  Combined with recent representative vaccine cases approved by the U.S. Food and Drug Administration and Chinese regulatory authorities, this study systematically reviews the specific strategies and application paths of computer simulation technologies(including molecular docking, artificial intelligence/machine learning, systems biology, and multi-omics integration) in key links of vaccine R&D, such as vaccine antigen screening and design, adjuvant development and optimization, as well as prediction of immunogenicity and protective efficacy.
      RESULTS  Computer simulation technology could significantly shorten the vaccine R&D cycle, improve the accuracy of candidate antigen and adjuvant screening, enhance the ability to respond to rapidly mutating pathogens, and provide data support for quality control in vaccine production and the development of detection methods. Practical vaccine cases showed that this technology can effectively improve vaccine protection rates, optimize immunization procedures, and promote the design of broad-spectrum vaccines.
      CONCLUSION  Computer simulation has become the core support for the transformation of modern vaccine R&D from “experience-driven” to “data-model dual-driven”. However, its application is still restricted by bottlenecks such as uneven data quality, limited cross-species extrapolation ability of models, and delays in closed-loop iteration. In the future, it is necessary to further strengthen “computation-experiment” collaboration through multi-source data integration, development of interpretable artificial intelligence models, and construction of automated experimental platforms, so as to provide more accurate and efficient vaccine R&D solutions for responding to viral mutations and emerging infectious diseases.

       

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