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

Summary

This protocol provides an approach to formulation optimization over mixture, continuous, and categorical study factors that minimizes subjective choices in the experimental design construction. For the analysis phase, an effective and easy-to-use modeling fitting procedure is employed.

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.

Introduction

Lipid nanoparticle (LNP) formulations for in vivo gene delivery systems generally involve four constituent lipids from the categories of ionizable, helper, and PEG lipids^1,2,3. Whether these lipids are being studied alone or simultaneously with other non-mixture factors, experiments for these formulations require "mixture" designs because - given a candidate formulation - increasing or decreasing the ratio of any one of the lipids necessarily leads to a corresponding decrease or increase in the sum of the ratios of the other three lipids.

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Protocol

The experiment described in the Representative Results section was carried out in accordance with the Guide for the Care and Use of Laboratory Animals and the procedures were performed following guidelines established by our Institutional Animal Care and Use Committee (IACUC). 6-8 week old female Balb/C mice were commercially obtained. Animals received ad libitum standard chow and water and were housed under standard conditions with 12 hour light/dark cycles, at a temperature of 65-75 °F (~18-23 °C) with 40-60% humidity.

1. Recording the study purpose, responses, and factors

NOTE: Througho....

Results

This approach has been validated across both broadly classified lipid types: MC3-like classical lipids and lipidoids (e.g., C12-200), generally derived from combinatorial chemistry. Compared to a benchmark LNP formulation developed using a One Factor at a Time (OFAT) method, the candidate formulations generated through our workflow frequently demonstrate potency improvements of 4- to 5-fold on a logarithmic scale, such as shown in the mouse liver luciferase readings in Figure 18. Tab.......

Discussion

Modern software for the design and analysis of mixture-process experiments makes it possible for scientists to improve their lipid nanoparticle formulations in a structured workflow that avoids inefficient OFAT experimentation. The recently developed SVEM modeling approach eliminates many of the arcane regression modifications and model reduction strategies that may have previously distracted scientists with extraneous statistical considerations. Once the results are collected, the SVEM analysis framework offers an appro.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
JMP Pro 17.1	JMP Statistical Discovery LLC