Department of Chemistry, University of California, Riverside, 900 University Ave., Riverside, CA, 92521, USA.
Genentech Research and Early Development, Genentech Inc., 1 DNA Way, South San Francisco, CA, 94080, USA.
Anal Bioanal Chem. 2024 Oct;416(24):5281-5293. doi: 10.1007/s00216-024-05455-x. Epub 2024 Aug 5.
In recent years, the use of lipid nanoparticles (LNPs) for delivery of messenger RNA (mRNA)-based therapies has gained substantial attention in the field of drug development. In such an application, multiple LNP attributes have to be carefully characterized to ensure product safety and quality, whereas accurate and efficient characterization of these complex mRNA-LNP formulations remains a challenging endeavor. Here, we present the development and application of an online separation and characterization platform designed for the isolation and in-depth analysis of mRNAs and mRNA-loaded LNPs. Our asymmetrical flow field-flow fractionation with a multi-detector (MD-AF4) method has demonstrated exceptional resolution between mRNA-LNPs and mRNAs, delivering excellent recoveries (over 70%) for both analytes and exceptional repeatability. Notably, this platform allows for comprehensive and multi-attribute LNP characterization, including online particle sizing, morphology characterization, and determination of encapsulation efficiency, all within a single injection. Furthermore, real-time online sizing by synchronizing multi-angle light scattering (MALS) and dynamic light scattering (DLS) presented higher resolution over traditional batch-mode DLS, particularly in differentiating heterogeneous samples with a low abundance of large-sized particles. Additionally, our method proves to be a valuable tool for monitoring LNP stability under varying stress conditions. Our work introduces a robust and versatile analytical platform using MD-AF4 that not only efficiently provides multi-attribute characterizations of mRNA-LNPs but also holds promise in advancing studies related to formulation screening, quality control, and stability assessment in the evolving field of nanoparticle delivery systems for mRNAs.
近年来，脂质纳米粒（LNPs）在信使 RNA（mRNA）为基础的治疗药物的传递中得到了广泛关注，在药物开发领域。在这种应用中，必须仔细地对多个 LNP 属性进行特征描述，以确保产品的安全性和质量，而对这些复杂的 mRNA-LNP 制剂的准确和高效的特征描述仍然是一项具有挑战性的工作。在这里，我们提出了一种在线分离和特征描述平台的开发和应用，该平台旨在分离和深入分析 mRNA 和负载 mRNA 的 LNPs。我们的不对称流场流分离与多检测器（MD-AF4）方法在 mRNA-LNP 和 mRNAs 之间表现出了出色的分辨率，为两种分析物提供了优异的回收率（超过 70%）和出色的重复性。值得注意的是，该平台允许对 LNP 进行全面的多属性特征描述，包括在线颗粒粒径分析、形态特征描述和包封效率的测定，所有这些都可以在一次注射中完成。此外，通过将多角度光散射（MALS）和动态光散射（DLS）同步进行实时在线粒径分析，提供了比传统批处理模式 DLS 更高的分辨率，特别是在区分具有低丰度大粒径颗粒的异质样品方面。此外，我们的方法被证明是监测 LNP 在不同应激条件下稳定性的有用工具。我们的工作引入了一个使用 MD-AF4 的强大而通用的分析平台，该平台不仅能够高效地提供 mRNA-LNP 的多属性特征描述，而且在推进纳米颗粒传递系统中与制剂筛选、质量控制和稳定性评估相关的研究方面也具有很大的潜力。
