Drug Discovery Sciences, Bayer Pharmaceuticals, Wuppertal 42113, Germany.
Computational Life Science, Bayer Crop Science, Monheim am Rhein 40789, Germany.
Proc Natl Acad Sci U S A. 2024 Nov 5;121(45):e2404555121. doi: 10.1073/pnas.2404555121. Epub 2024 Oct 30.
The use of lipid nanoparticles (LNPs) for therapeutic RNA delivery has gained significant interest, particularly highlighted by recent milestones such as the approval of Onpattro and two mRNA-based SARS-CoV-2 vaccines. However, despite substantial advancements in this field, our understanding of the structure and internal organization of RNA-LNPs -and their relationship to efficacy, both in vitro and in vivo- remains limited. In this study, we present a coarse-grained molecular dynamics (MD) approach that allows for the simulations of full-size LNPs. By analyzing MD-derived structural characteristics in conjunction with cellular experiments, we investigate the effect of critical parameters, such as pH and composition, on LNP structure and potency. Additionally, we examine the mobility and chemical environment within LNPs at a molecular level. Our findings highlight the significant impact that LNP composition and internal molecular mobility can have on key stages of LNP-based intracellular RNA delivery.
脂质纳米粒(LNPs)在治疗性 RNA 递送上的应用引起了广泛关注,尤其是最近的一些里程碑事件,如 Onpattro 的批准和两种基于 mRNA 的 SARS-CoV-2 疫苗的问世,更是凸显了这一趋势。然而,尽管该领域取得了实质性进展,但我们对 RNA-LNP 的结构和内部组织及其与体外和体内疗效的关系的理解仍然有限。在这项研究中,我们提出了一种粗粒分子动力学(MD)方法,该方法可用于模拟全尺寸 LNPs。通过分析 MD 得出的结构特征,并结合细胞实验,我们研究了关键参数(如 pH 值和组成)对 LNP 结构和效力的影响。此外,我们还在分子水平上研究了 LNPs 内部的流动性和化学环境。我们的研究结果强调了 LNP 组成和内部分子流动性对 LNP 基于细胞内 RNA 递释的关键阶段的重大影响。