Multi-detector frit-inlet asymmetric flow field-flow fractionation method development for nanoparticle mixtures:...

Lipid-based nanoparticles (LNPs) are transforming the field of drug delivery, with one gene therapy product and four mRNA vaccines approved at the time of this report. As with other novel nanomedicines, the development of reliable and standardized methods for evaluating their quality attributes-factors that are essential for quality control and regulatory compliance-is essential to support their bench-to-bedside transition. Frit-Inlet Asymmetric Flow Field-Flow Fractionation (FI-AF4) combined with ultraviolet (UV), multi-angle light scattering (MALS) and dynamic light scattering (DLS) online detectors is widely recognized as a robust and versatile technique for the physicochemical analysis of LNPs. A robust protocol for FI-AF4 method development was established for a mixture of MC3-LNPs and bovine serum albumin (BSA), guided by preliminary particle size and polydispersity assessments using lower-resolution techniques such as DLS and nanoparticle tracking analysis (NTA). The FI-AF4 technique was subsequently applied to characterize particle size and morphology, and to evaluate the most suitable MALS fitmodels across different analytical approaches. The coupling of FI-AF4 with online MALS and DLS enabled the simultaneous separation and online analysis of large sub-populations contained in the sample (methods 2-4), which could not be detected using method 1. The change in particle morphology was found to be significant amongst different methods for each subpopulation (shape factor 0.709-0.793 for LNP peak 1, 0.765-0.853 for LNP peak 2, and 1.069-1.263 for LNP peak 3). MALS fit models indicate that the coated sphere and random coil models were the most appropriate fits for the three LNP sub-populations (R² > 0.95 and RMSE < 0.009). In summary, implementing the FI-AF4-UV-MALS-DLS protocol provided successful separation of LNPs from protein-containing media with additional analytical approaches not previously described by ISO guidelines. Collecting robust and reproducible information on LNP attributes is key to the several phases of drug development that ideally transform a drug in the pre-clinical phase, from bench-to-bedside.