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Abstract：
The robust characterization of lipid nanoparticles (LNPs) encapsulating therapeutics or vaccines is an important and multifaceted translational problem. Sedimentation velocity analytical ultracentrifugation (SV-AUC) has proven to be a powerful approach in the characterization of size-distribution, interactions, and composition of various types of nanoparticles across a large size range, including metal nanoparticles (NPs), polymeric NPs, and also nucleic acid loaded viral capsids. Similar potential of SV-AUC can be expected for the characterization of LNPs, but is hindered by the flotation of LNPs being incompatible with common sedimentation analysis models. To address this gap, we developed a high-resolution, diffusion-deconvoluted sedimentation/flotation distribution analysis approach analogous to the most widely used sedimentation analysis model c(s). The approach takes advantage of independent measurements of the average particle size or diffusion coefficient, which can be conveniently determined, for example, by dynamic light scattering (DLS). We demonstrate the application to an experimental model of extruded liposomes as well as a commercial LNP product and discuss experimental potential and limitations of SV-AUC. The method is implemented analogously to the sedimentation models in the free, widely used SEDFIT software.
摘要：
包封治疗剂或疫苗的脂质纳米颗粒(LNP)的稳健表征是重要且多方面的翻译问题。沉降速度分析超速离心（SV-AUC）已被证明是表征尺寸分布的强大方法，互动,以及大尺寸范围内各种类型的纳米颗粒的组成，包括金属纳米颗粒(NPs)，聚合物NP，以及载有核酸的病毒衣壳。对于LNP的表征，可以预期SV-AUC的类似潜力，但由于LNPs的浮选与常见的沉降分析模型不兼容而受到阻碍。为了解决这个差距，我们开发了高分辨率，扩散-去卷积沉降/浮选分布分析方法类似于最广泛使用的沉降分析模型c(s)。该方法利用了平均粒径或扩散系数的独立测量，可以方便地确定，例如,通过动态光散射(DLS)。我们展示了挤出脂质体的实验模型以及商业LNP产品的应用，并讨论了SV-AUC的实验潜力和局限性。该方法类似于自由沉降模型，广泛使用SEDFIT软件。
