Flotation Coefficient Distributions of Lipid Nanoparticles by Sedimentation Velocity Analytical Ultracentrifugation

IF 15.8 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
ACS Nano Pub Date : 2024-07-05 DOI:10.1021/acsnano.4c05322
Huaying Zhao, Alioscka A. Sousa and Peter Schuck*, 
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引用次数: 0

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.

Abstract Image

Abstract Image

沉降速度分析超速离心法测定脂质纳米颗粒的浮选系数分布
对封装治疗药物或疫苗的脂质纳米颗粒(LNPs)进行可靠的表征是一个重要的、多方面的转化问题。沉降速度分析超速离心法(SV-AUC)已被证明是表征各种类型纳米颗粒(包括金属纳米颗粒 (NPs)、聚合物 NPs 和负载核酸的病毒外壳)在大尺寸范围内的尺寸分布、相互作用和组成的有力方法。SV-AUC 在表征 LNPs 方面也有类似的潜力,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。为了弥补这一缺陷,我们开发了一种高分辨率、扩散去卷积沉积/浮选分布分析方法,类似于最广泛使用的沉积分析模型 c(s)。该方法利用了平均粒径或扩散系数的独立测量值,这些测量值可以通过动态光散射(DLS)等方法方便地确定。我们演示了挤压脂质体实验模型和商用 LNP 产品的应用,并讨论了 SV-AUC 的实验潜力和局限性。该方法的实现类似于广泛使用的免费 SEDFIT 软件中的沉降模型。
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来源期刊
ACS Nano
ACS Nano 工程技术-材料科学:综合
CiteScore
26.00
自引率
4.10%
发文量
1627
审稿时长
1.7 months
期刊介绍: ACS Nano, published monthly, serves as an international forum for comprehensive articles on nanoscience and nanotechnology research at the intersections of chemistry, biology, materials science, physics, and engineering. The journal fosters communication among scientists in these communities, facilitating collaboration, new research opportunities, and advancements through discoveries. ACS Nano covers synthesis, assembly, characterization, theory, and simulation of nanostructures, nanobiotechnology, nanofabrication, methods and tools for nanoscience and nanotechnology, and self- and directed-assembly. Alongside original research articles, it offers thorough reviews, perspectives on cutting-edge research, and discussions envisioning the future of nanoscience and nanotechnology.
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