Lipid nanoparticles (LNPs) are being intensively researched and developed to leverage their ability to safely and effectively deliver therapeutics. To achieve optimal therapeutic delivery, a comprehensive understanding of the relationship between formulation, structure, and efficacy is critical. However, the vast chemical space involved in the production of LNPs and the resulting structural complexity make the structure to function relationship challenging to assess and predict. New components and formulation procedures, which provide new opportunities for the use of LNPs, would be best identified and optimized using high-throughput characterization methods. Recently, a high-throughput workflow, consisting of automated mixing, small-angle X-ray scattering (SAXS), and cellular assays, demonstrated a link between formulation, internal structure, and efficacy for a library of LNPs. As SAXS data can be rapidly collected, the stage is set for the collection of thousands of SAXS profiles from a myriad of LNP formulations. In addition, correlated LNP small-angle neutron scattering (SANS) datasets, where components are systematically deuterated for additional contrast inside, provide complementary structural information. The centralization of SAXS and SANS datasets from LNPs, with appropriate, standardized metadata describing formulation parameters, into a data repository will provide valuable guidance for the formulation of LNPs with desired properties. To this end, we introduce Simple Scattering, an easy-to-use, open data repository for storing and sharing groups of correlated scattering profiles obtained from LNP screening experiments. Here, we discuss the current state of the repository, including limitations and upcoming changes, and our vision towards future usage in developing our collective knowledge base of LNPs.

中文翻译：

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简单散射：脂质纳米粒子结构数据存储库

脂质纳米颗粒 (LNP) 正在得到深入研究和开发，以利用其安全有效地提供治疗的能力。为了实现最佳的治疗效果，全面了解配方、结构和功效之间的关系至关重要。然而，LNP 生产涉及的巨大化学空间以及由此产生的结构复杂性使得结构与功能的关系难以评估和预测。新的成分和配方程序为 LNP 的使用提供了新的机会，最好使用高通量表征方法来识别和优化。最近，由自动混合、小角度 X 射线散射 (SAXS) 和细胞测定组成的高通量工作流程证明了 LNP 库的配方、内部结构和功效之间的联系。由于可以快速收集 SAXS 数据，因此为从无数 LNP 配方中收集数千个 SAXS 配置文件做好了准备。此外，相关的 LNP 小角中子散射 (SANS) 数据集（其中的成分被系统地氘化以增加内部对比度）提供了补充的结构信息。将来自 LNP 的 SAXS 和 SANS 数据集以及描述配方参数的适当标准化元数据集中到数据存储库中，将为具有所需特性的 LNP 的配方提供有价值的指导。为此，我们引入了 Simple Scattering，这是一个易于使用的开放数据存储库，用于存储和共享从 LNP 筛选实验中获得的相关散射剖面组。在这里，我们讨论存储库的当前状态，包括限制和即将发生的变化，以及我们对未来开发 LNP 集体知识库的愿景。