Department of Life Science, Dezhou University, Dezhou, 253023, China.
State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China.
Sci Rep. 2024 Oct 14;14(1):23995. doi: 10.1038/s41598-024-74221-x.
Vaccination has been widely recognized as an effective measure for preventing infectious diseases. To facilitate quantitative research into the activation of adaptive immune responses in the human body by vaccines, it is important to develop an appropriate mathematical model, which can provide valuable guidance for vaccine development. In this study, we constructed a novel mathematical model to simulate the dynamics of antibody levels following vaccination, based on principles from immunology. Our model offers a concise and accurate representation of the kinetics of antibody response. We conducted a comparative analysis of antibody dynamics within the body after administering several common vaccines, including traditional inactivated vaccines, mRNA vaccines, and future attenuated vaccines based on defective interfering viral particles (DVG). Our findings suggest that booster shots play a crucial role in enhancing Immunoglobulin G (IgG) antibody levels, and we provide a detailed discussion on the advantages and disadvantages of different vaccine types. From a mathematical standpoint, our model proposes four essential approaches to guide vaccine design: enhancing antigenic T-cell immunogenicity, directing the production of high-affinity antibodies, reducing the rate of IgG decay, and lowering the peak level of vaccine antigen-antibody complexes. Our study contributes to the understanding of vaccine design and its application by explaining various phenomena and providing guidance in comprehending the interactions between antibodies and antigens during the immune process.
疫苗接种已被广泛认为是预防传染病的有效措施。为了便于对疫苗在人体中激活适应性免疫反应进行定量研究，开发适当的数学模型非常重要，这可为疫苗开发提供有价值的指导。在这项研究中，我们根据免疫学原理构建了一个新的数学模型来模拟接种疫苗后抗体水平的动态变化。我们的模型提供了对抗体反应动力学的简洁而准确的表示。我们对几种常见疫苗（包括传统的灭活疫苗、mRNA 疫苗和基于缺陷干扰病毒颗粒（DVG）的未来减毒疫苗）在体内的抗体动力学进行了比较分析。我们的研究结果表明，加强针在提高免疫球蛋白 G（IgG）抗体水平方面起着至关重要的作用，我们还详细讨论了不同疫苗类型的优缺点。从数学角度来看，我们的模型提出了四种指导疫苗设计的基本方法：增强抗原 T 细胞免疫原性、引导产生高亲和力抗体、降低 IgG 衰减率以及降低疫苗抗原-抗体复合物的峰值水平。我们的研究通过解释各种现象并提供对免疫过程中抗体和抗原相互作用的理解指导，为疫苗设计及其应用的理解做出了贡献。
