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A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization Using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Andrew T. KarlSean EssexJames WisnowskiHeath Rushing
Dec 2022
摘要
This work summarizes a Quality by Design (QbD) styled approach to theoptimization of lipid nanoparticle (LNP) formulations with a goal of providingan accessible workflow for scientists. The restriction in these studies thatthe molar ratios of the ionizable, helper, and PEG lipids add up to 100%requires modified design and analysis methods to accommodate this mixtureconstraint. Focusing on lipid and process factors that are commonly used in LNPdesign optimization, we provide pragmatic suggestions for sidestepping thedifficulties that traditionally arise in the analysis of experiments thatinclude mixture factors and show how the recently developed statisticalframework of self-validated ensemble models (SVEM) can simultaneously simplifythe analysis of results from mixture-process experiments and improve thequality of the candidate optimal formulations. These steps are illustrated witha running example. We also present graphical tools based on the fitted modelthat simplify the interpretation of the results and facilitate the design offollow up studies in the form of confirmation runs or augmented designs.
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