Padilla Marshall S, Shepherd Sarah J, Hanna Andrew R, Kurnik Martin, Zhang Xujun, Chen Michelle, Byrnes James, Joseph Ryann A, Yamagata Hannah M, Ricciardi Adele S, Mrksich Kaitlin, Issadore David, Gupta Kushol, Mitchell Michael J
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Wyatt Technology, LLC, Goleta, CA 93117, USA.
bioRxiv. 2025 Jan 27:2024.12.19.629496. doi: 10.1101/2024.12.19.629496.
Lipid nanoparticles (LNPs) are the most advanced delivery system currently available for RNA therapeutics. Their development has accelerated since the success of Patisiran, the first siRNA-LNP therapeutic, and the mRNA vaccines that emerged during the COVID-19 pandemic. Designing LNPs with specific targeting, high potency, and minimal side effects is crucial for their successful clinical use. However, our understanding of how the composition and mixing method influences the structural, biophysical, and biological properties of the resulting LNPs remains limited, hindering the development of LNPs. Our lack of structural understanding extends from the physical and compositional polydispersity of LNPs, which render traditional characterization methods, such as dynamic light scattering (DLS), unable to accurately quantitate the physicochemical characteristics of LNPs. In this study, we address the challenge of structurally characterizing polydisperse LNP formulations using emerging solution-based biophysical methods that have higher resolution and provide biophysical data beyond size and polydispersity. These techniques include sedimentation velocity analytical ultracentrifugation (SV-AUC), field-flow fractionation followed by multi-angle light scattering (FFF-MALS), and size-exclusion chromatography in-line with synchrotron small-angle X-ray scattering (SEC-SAXS). Here, we show that the LNPs have intrinsic polydispersity in size, RNA loading, and shape, and that these parameters are dependent on both the formulation technique and lipid composition. Lastly, we demonstrate that these biophysical methods can be employed to predict transfection in human primary T cells, intravenous administration, and intramuscular administration by examining the relationship between mRNA translation and physicochemical characteristics. We envision that employing solution-based biophysical methods will be essential for determining LNP structure-function relationships, facilitating the creation of new design rules for LNPs.
脂质纳米颗粒（LNPs）是目前用于RNA治疗的最先进的递送系统。自首款siRNA-LNP疗法Patisiran成功以及新冠疫情期间出现的mRNA疫苗以来，它们的发展加速了。设计具有特定靶向性、高效性和最小副作用的LNPs对于其临床成功应用至关重要。然而，我们对组成和混合方法如何影响所得LNPs的结构、生物物理和生物学特性的理解仍然有限，这阻碍了LNPs的发展。我们对结构的理解不足源于LNPs的物理和组成多分散性，这使得传统的表征方法，如动态光散射（DLS），无法准确量化LNPs的物理化学特性。在本研究中，我们使用新兴的基于溶液的生物物理方法来应对多分散LNP制剂结构表征的挑战，这些方法具有更高的分辨率，并能提供除尺寸和多分散性之外的生物物理数据。这些技术包括沉降速度分析超离心法（SV-AUC）、场流分级结合多角度光散射（FFF-MALS）以及与同步加速器小角X射线散射联用的尺寸排阻色谱法（SEC-SAXS）。在这里，我们表明LNPs在尺寸、RNA负载和形状方面具有内在的多分散性，并且这些参数取决于制剂技术和脂质组成。最后，我们证明通过研究mRNA翻译与物理化学特性之间的关系，这些生物物理方法可用于预测在人原代T细胞中的转染、静脉内给药和肌肉内给药。我们设想采用基于溶液的生物物理方法对于确定LNP的结构-功能关系、促进创建LNPs的新设计规则至关重要。
