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电泳微流控分析载 mRNA 和 pDNA 的脂质纳米粒。

Electrophoretic Microfluidic Characterization of mRNA- and pDNA-Loaded Lipid Nanoparticles.

机构信息

Center for Biomedical Engineering, School of Engineering, Brown University, Providence, Rhode Island 02912, United States.

Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States.

出版信息

ACS Appl Mater Interfaces. 2024 May 29;16(21):26984-26997. doi: 10.1021/acsami.4c00208. Epub 2024 May 16.

Abstract

Lipid nanoparticles (LNPs) are clinically advanced nonviral gene delivery vehicles with a demonstrated ability to address viral, oncological, and genetic diseases. However, the further development of LNP therapies requires rapid analytical techniques to support their development and manufacturing. The method developed and described in this paper presents an approach to rapidly and accurately analyze LNPs for optimized therapeutic loading by utilizing an electrophoresis microfluidic platform to analyze the composition of LNPs with different clinical lipid compositions (Onpattro, Comirnaty, and Spikevax) and nucleic acid (plasmid DNA (pDNA) and messenger RNA (mRNA)) formulations. This method enables the high-throughput screening of LNPs using a 96- or 384-well plate with approximate times of 2-4 min per sample using a total volume of 11 μL. The lipid analysis requires concentrations approximately between 10 and 10 particles/mL and has an average precision error of 10.4% and a prediction error of 19.1% when compared to using a NanoSight, while the nucleic acid analysis requires low concentrations of 1.17 ng/μL for pDNA and 0.17 ng/μL for mRNA and has an average precision error of 4.8% and a prediction error of 9.4% when compared to using a PicoGreen and RiboGreen assay. In addition, our method quantifies the relative concentration of nucleic acid per LNP. Utilizing this approach, we observed an average of 263 ± 62.2 mRNA per LNP and 126.3 ± 21.2 pDNA per LNP for the LNP formulations used in this study, where the accuracy of these estimations is dependent on reference standards. We foresee the utility of this technique in the high-throughput characterization of LNPs during manufacturing and formulation research and development.

摘要

脂质纳米颗粒 (LNPs) 是临床先进的非病毒基因传递载体,具有解决病毒、肿瘤和遗传疾病的能力。然而,LNP 疗法的进一步发展需要快速分析技术来支持其开发和制造。本文中开发和描述的方法提出了一种利用电泳微流控平台分析具有不同临床脂质成分(Onpattro、Comirnaty 和 Spikevax)和核酸(质粒 DNA(pDNA)和信使 RNA(mRNA))配方的 LNP 组成的方法,以快速准确地分析 LNP,从而优化治疗性负载。该方法可通过 96 孔或 384 孔板进行 LNP 的高通量筛选,每个样品的大致时间为 2-4 分钟,总体积为 11μL。脂质分析需要大约 10 到 10 个粒子/mL 的浓度,与使用 NanoSight 相比,平均精度误差为 10.4%,预测误差为 19.1%,而核酸分析需要 pDNA 低至 1.17ng/μL 和 mRNA 低至 0.17ng/μL 的浓度,与使用 PicoGreen 和 RiboGreen 测定法相比,平均精度误差为 4.8%,预测误差为 9.4%。此外,我们的方法还定量了每个 LNP 中核酸的相对浓度。利用这种方法,我们观察到本研究中使用的 LNP 制剂的平均每个 LNP 有 263 ± 62.2mRNA 和 126.3 ± 21.2pDNA,这些估计的准确性取决于参考标准。我们预计这项技术在制造和制剂研发过程中对 LNP 的高通量表征具有实用价值。

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