Nanoparticle sizing in the field of nanomedicine: Power of an analytical ultracentrifuge

Hydrodynamic and light scattering methods are urgently required for accurate characterization of nanoparticles (NPs) in the field of nanomedicine to unveil their sizes and distributions. A fundamental characterization approach in the field of nanomedicines is, next to standard batch dynamic light scattering (DLS) and increasingly more applied (asymmetrical flow) field-flow fractionation (FFF) coupled to multi-angle laser light scattering (MALLS), the utilization of an analytical ultracentrifuge (AUC). Here, we demonstrate the power of an AUC in comparison to batch DLS and FFF-MALLS to decipher, in detail, the size and dispersity of pharma-relevant, commercial and in-house prepared soft matter NPs, suitable for life science applications. In this study, size and dispersity of poly(lactic-co-glycolic acid) (PLGA) NPs and in-house prepared NPs, consisting of the commercially available pharmapolymer Eudragit (R) E or of a polymer of similar composition synthesized via reversible addition fragmentation chain transfer (RAFT) polymerization, were investigated. Simultaneously, an insight on the presence of the utilized surfactant on the NP formulations, which is usually limited with other techniques, could be achieved by multi-speed experiments with the AUC in one experimental setting. While the repeatability and ruggedness of observations with modern AUC instruments of the newest generation is demonstrated, the results are further underpinned by the classical relations of hydrodynamics. Investigations aiming at hydrodynamic diameters (from DLS) and radii of gyration (from FFF-MALLS) are critically discussed and compared to the repeatable and rugged investigations by an AUC. The latter is proven to provide a self-sufficient experimental approach for NP characterization in the field of nanomedicine based on absolute principles, compares well to FFF-MALLS, and can unravel issues in NP sizing that arise when more common techniques, such as DLS, are used. (C) 2022 The Authors. Published by Elsevier B.V.