Multi-Detector Frit-Inlet Asymmetric Flow Field-Flow Fractionation Method Development For Nanoparticle Mixtures: Deeper Analysis Beyond ISO Quality Standards

Abstract

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–visible (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 low-resolution techniques such as DLS and Nanoparticle Tracking Analysis (NTA). The FI-AF4 technique was subsequently applied to characterize particle size, morphology, and to evaluate the most suitable MALS fitting models across different analytical approaches. The coupling of FI-AF4 with online MALS and DLS detection enabled the simultaneous separation and inline analysis of large sub-populations contained in the sample (methods 2 - 4) 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 (LNP Peak 1), 0.765 – 0.853 (LNP Peak 2) and 1.069 – 1.263 (LNP Peak 3)). MALS fit models indicated that the Coated Sphere and Random Coil models were the most appropriate fits for the three LNP subpopulations (R2 > 0.95 and RMSE < 0.009). In summary, implementing the FI-AF4-UV-MALS-DLS protocol provided a successful separation of LNP from biological environments with additional analytical insights 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.