  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.
