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.

翻译：我们提出一种基于质量源于设计(QbD)理念的脂质纳米颗粒(LNP)配方优化方法，旨在为科研人员提供易于实施的工作流程。此类研究中存在固有约束条件：可电离脂质、辅助脂质和PEG脂质的摩尔比之和必须为100%，因此需要专门的设计与分析方法来处理这种混合物约束。针对LNP配方优化中常用的脂质与工艺因素，我们通过采用空间填充设计及近期发展的自验证集成模型(SVEM)统计框架，避免了传统混合物-过程实验设计与分析中常见的诸多难题。该工作流程不仅能生成候选优化配方，还可构建拟合统计模型的图形化总结，简化结果解读。新识别的候选配方通过验证实验进行评估，并可在更全面的第二阶段研究中得以实施。