Determination of LNP Composition and Lipid Ratios
After developing an initial formulation matrix by mixing different lipids in varying ratios, utilize analytical techniques such as dynamic light scattering (DLS), 2-(p-toluidinyl)naphthalene-6-sulphonic acid (TNS) ionization assays and plate-based high-content screening to assess the characteristics of the lipid nanoparticles (LNPs). Observe how changes in these lipid ratios influence these parameters.
Phenomenex Biozen Columns enable analytical quality by design to ensure high-quality data when determining LNP compositions and ratios. The SCIEX ZenoTOF 7600+ system provides deep insights into the structure of lipids, including lipid impurities, with excellent quantitative performance for data-driven decision-making.
Robust data management, automated data analysis and integration of results across multiple assays are needed in assessing and determining the best LNP formulations. Genedata and IDBS provide scalable, enterprise-level cloud software products from R&D through manufacturing for assured confidence in both GxP and non-GxP environments.
Out-of-the-box, the Genedata Biopharma Platform enables accurate LC/MS-based quantitation of relative abundance ratios in complex samples, accelerating large-scale high-throughput analyses. Genedata also automates and centralizes the analysis of multiple plate-based assays during mRNA-LNP development, such as dynamic light scattering (DLS), TNS ionization assays, RiboGreen mRNA encapsulation assays, toxicity, cell trafficking and automated ELISAs. The platform captures process information, mRNA cargo, LNP components, and lipid ratios, as well as raw materials, and integrates all assay results for faster decision-making. The Genedata Biopharma Platform provides cutting-edge support for in silico determination of ideal LNP composition and lipid ratios.
Capabilities within IDBS Polar software allow scientists to design, execute and analyze experiments within a single platform to understand material genealogy, process performance and determine which parameters are most influential based on statistical analysis. This allows scientists to improve reproducibility to reduce the number of experiments that are needed to gain process understanding, optimize product quality and support regulatory compliance.