Advanced Drug Delivery, Pharmaceutical Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB21 6GH, UK.
Department of Molecular & Integrative Physiology, University of Michigan Medical School Ann Arbor, Michigan 48109-5624, USA.
Nanoscale. 2022 Jan 27;14(4):1480-1491. doi: 10.1039/d1nr06858j.
mRNA lipid nanoparticles (LNPs) are at the forefront of nucleic acid intracellular delivery, as exemplified by the recent emergency approval of two mRNA LNP-based COVID-19 vaccines. The success of an LNP product largely depends on the systematic optimisation of the four lipidic components, namely the ionisable lipid, PEG lipid, structural and helper lipids. However, the screening of novel lipidic components and LNP compositions is limited by the low-throughput of LNP preparation. To address these issues, we herein present an automated high-throughput screening platform to select novel ionisable lipids and corresponding LNPs encapsulating mRNA . This high-throughput platform employs a lab-based automated liquid handling system, amenable to high-throughput (up to 384 formulations per plate and several plates per run) and allows precise mixing and reproducible mRNA LNP preparation which ensures a direct head-to-head comparison of hundreds and even thousands of novel LNPs. Most importantly, the robotic process has been successfully applied to the screening of novel LNPs encapsulating mRNA and has identified the same novel mRNA LNP leads as those from microfluidics-mixing technology, with a correlation coefficient of 0.8751. This high-throughput platform can facilitate to narrow down the number of novel ionisable lipids to be evaluated . Moreover, this platform has been integrated into a fully-automated workflow for LNP property control, physicochemical characterisation and biological evaluation. The high-throughput platform may accelerate proprietary lipid development, mRNA LNP lead optimisation and candidate selection to advance preclinical mRNA LNP development to meet urgent global needs.
mRNA 脂质纳米粒 (LNP) 是核酸细胞内递药的前沿,最近两种基于 mRNA LNP 的 COVID-19 疫苗的紧急获批就是例证。LNP 产品的成功在很大程度上取决于对四种脂质成分(即可离子化脂质、PEG 脂质、结构脂质和辅助脂质)的系统优化。然而,新型脂质成分和 LNP 配方的筛选受到 LNP 制备低通量的限制。为了解决这些问题,我们在此提出了一种自动化高通量筛选平台,用于选择新型可离子化脂质和包封 mRNA 的相应 LNPs。该高通量平台采用基于实验室的自动化液体处理系统,适用于高通量(每个平板高达 384 种配方,每个运行可进行多个平板),并允许精确混合和可重复的 mRNA LNP 制备,从而确保数百甚至数千种新型 LNP 的直接直接比较。最重要的是,该机器人过程已成功应用于包封 mRNA 的新型 LNP 的筛选,并鉴定出与微流控混合技术相同的新型 mRNA LNP 先导化合物,相关系数为 0.8751。该高通量平台有助于减少需要评估的新型可离子化脂质的数量。此外,该平台已集成到 LNP 性能控制、理化特性表征和生物学评估的全自动工作流程中。高通量平台可能会加速专有脂质的开发、mRNA LNP 先导化合物的优化和候选物的筛选,从而推进临床前 mRNA LNP 的开发,以满足全球紧迫的需求。