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一种用于mRNA疫苗研发与优化的多尺度定量系统药理学模型。

A Multiscale Quantitative Systems Pharmacology Model for the Development and Optimization of mRNA Vaccines.

作者信息

Dasti Lorenzo, Giampiccolo Stefano, Pettinà Elisa, Fiandaca Giada, Zangani Natascia, Leonardelli Lorena, Hedayioglu Fabio De Lima, Campanile Elio, Marchetti Luca

机构信息

Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.

Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2025 Jul;14(7):1213-1224. doi: 10.1002/psp4.70041. Epub 2025 May 26.

Abstract

The unprecedented effort to cope with the COVID-19 pandemic has unlocked the potential of mRNA vaccines as a powerful technology, set to become increasingly pervasive in the years to come. As in other areas of drug development, mathematical modeling is a pivotal tool to support and expedite the mRNA vaccine development process. This study introduces a Quantitative Systems Pharmacology (QSP) model that captures key immune responses following mRNA vaccine administration, encompassing both tissue-level and molecular-level events. The model mechanistically describes the biological processes from the uptake of mRNA by antigen-presenting cells at the injection site to the subsequent release of antibodies into the bloodstream. This two-layer model represents a first attempt to link the molecular mechanisms leading to antigen expression with the immune response, paving the way for the future integration of specific vaccine attributes, such as mRNA sequence features and nanotechnology-based delivery systems. Calibrated specifically for the BNT162b2 SARS-CoV-2 vaccine, the model has undergone successful validation across various dosing regimens and administration schedules. The results underscore the model's effectiveness in optimizing dosing strategies and highlighting critical differences in immune responses, particularly among low-responder groups such as the elderly. Furthermore, the model's adaptability has been demonstrated through its calibration for other mRNA vaccines, such as the Moderna mRNA-1273 vaccine, emphasizing its versatility and broad applicability in mRNA vaccine research and development.

摘要

应对新冠疫情的史无前例的努力释放了mRNA疫苗作为一种强大技术的潜力,预计在未来几年它将变得越来越普遍。与药物开发的其他领域一样,数学建模是支持和加速mRNA疫苗开发过程的关键工具。本研究引入了一种定量系统药理学(QSP)模型,该模型捕捉了mRNA疫苗接种后的关键免疫反应,涵盖了组织水平和分子水平的事件。该模型从注射部位抗原呈递细胞摄取mRNA到随后抗体释放到血液中的过程,以机械方式描述了生物学过程。这个两层模型首次尝试将导致抗原表达的分子机制与免疫反应联系起来,为未来整合特定疫苗属性(如mRNA序列特征和基于纳米技术的递送系统)铺平了道路。该模型专门针对BNT162b2新冠疫苗进行了校准,并在各种给药方案和接种计划中成功通过了验证。结果强调了该模型在优化给药策略和突出免疫反应的关键差异方面的有效性,特别是在老年等低反应者群体中。此外,该模型通过针对其他mRNA疫苗(如Moderna mRNA-1273疫苗)进行校准,展示了其适应性,强调了其在mRNA疫苗研发中的通用性和广泛适用性。

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