论文标题
使用设计的混合过程实验和自validated集合模型（SVEM）的脂质纳米颗粒（LNP）配方的工作流程优化的工作流程优化
A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization Using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
论文作者
论文摘要
我们通过设计（QBD）样式的方法提出了优化脂质纳米颗粒（LNP）配方的质量，旨在为科学家提供可访问的工作流程。在这些研究中，固有的限制是，可离子，助手和PEG脂质的摩尔比必须总计100％，需要专门的设计和分析方法来适应这种混合物约束。为了关注LNP设计优化中常用的脂质和过程因素，我们提供的步骤避免了通过采用空间填充设计并利用最近开发的自动化综合集合模型（SVEM）的近期开发的自动化统计框架来避免混合过程实验的设计和分析中出现的许多困难。除了产生候选最佳配方外，工作流程还构建了拟合统计模型的图形摘要，以简化结果的解释。通过确认运行评估新确定的候选公式，可以选择在更全面的二手研究中进行。
We present a Quality by Design (QbD) styled approach for optimizing lipid nanoparticle (LNP) formulations, aiming to offer scientists an accessible workflow. The inherent restriction in these studies, where the molar ratios of ionizable, helper, and PEG lipids must add up to 100%, requires specialized design and analysis methods to accommodate this mixture constraint. Focusing on lipid and process factors that are commonly used in LNP design optimization, we provide steps that avoid many of the difficulties that traditionally arise in the design and analysis of mixture-process experiments by employing space-filling designs and utilizing the recently developed statistical framework of self-validated ensemble models (SVEM). In addition to producing candidate optimal formulations, the workflow also builds graphical summaries of the fitted statistical models that simplify the interpretation of the results. The newly identified candidate formulations are assessed with confirmation runs and optionally can be conducted in the context of a more comprehensive second-phase study.
