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Robust and Repeatable Nanoparticle Drug Delivery Characterization with FFF-MALS-DLS

Daniel Some, Ph.D., Principal Scientist, Wyatt Technology Corp

Figure 1. DLS measurements of empty (red) and filled (green) lipo-somes using a DynaPro NanoStar batch DLS detector. The sizes are indistinguishable within the relatively low resolution of this tech-nique, providing no positive indication of their loading.

Figure 2. Hydrodynamic radius (a) and root-mean square radius (b) plotted against elution time overlaid with 90 LS signals for empty liposome sample (red) and filled liposome sample (green). The Rh and Rg values are determined by the respective online DLS and MALS detectors. The re-sults from duplicate runs of each sample are shown here to demonstrate the excellent reproducibility of the FFF-MALS analysis. Figure

Figure 3. a) Root-mean square radius, Rg, plotted against hydrodynamic radius, Rh, for empty liposome sample (red) and filled liposome sample (green). The slopes for empty and filled liposomes are 1.0 and 0.77, respectively. b) Number density calculated for each particle size.

Figure 4. Highly accurate sizing of silver-nanolipid complexes by FFF-MALS. Preparations as follows: AB1 - 5% w/w NLC, 0.1% w/w silver; AB2 - 10% w/w NLC, 0.1% w/w silver; AB3 -5% w/w NLC, 0.15% w/w silver; AB4 - 10% w/w NLC, 0.15% w/w silver.

Nanoparticles hold enormous potential for taRgeted, well-controlled drug delivery. Extensive characterization of nanoscale drug-delivery vehicles is essential to ensure their efficacy and reproducibility. While various methods are available for characterization of nanoparticle size, in- cluding dynamic light scattering, electron microscopy and nanoparticle tracking analysis, FFF-MALS is one of the most versatile techniques for determining size, structure and other properties.

This study shows that FFF-MALS-DLS is an easy-to-use and powerful characterization tool for liposomes and related formulations to obtain information on particle size, size distribution, particle count and structure. The technique is already widely used to answer environmental questions and characterize nanoparticles of polymers, extracellular vesicles and protein aggregates.

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