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

TL;DR

This paper introduces a practical workflow for optimizing lipid nanoparticle formulations using mixture-process experiments and self-validated ensemble models, simplifying design, analysis, and interpretation for scientists.

Contribution

It develops a novel, accessible workflow employing space-filling designs and SVEM to optimize LNP formulations considering mixture constraints.

Findings

Identified candidate LNP formulations with optimized properties.

Provided graphical summaries to interpret statistical models.

Validated candidate formulations through confirmation runs.

Abstract

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.