A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization Using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
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This work summarizes a Quality by Design (QbD) styled approach to the optimization of lipid nanoparticle (LNP) formulations with a goal of providing an accessible workflow for scientists. The restriction in these studies that the molar ratios of the ionizable, helper, and PEG lipids add up to 100 requires modified 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 pragmatic suggestions for avoiding the difficulties that traditionally arise in the analysis of experiments that include mixture factors and show how the recently developed statistical framework of self-validated ensemble models (SVEM) can simultaneously simplify the analysis of results from mixture-process experiments and improve the quality of the candidate optimal formulations. These steps are illustrated with a running example. We also present graphical tools based on the fitted model that simplify the interpretation of the results and facilitate the design of follow up studies in the form of confirmation runs or augmented designs.
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