Article

Characterization of a nanoparticulate drug delivery system using scanning ion occlusion sensing.

School of Pharmacy, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand.
Pharmaceutical Research (impact factor: 4.09). 05/2012; 29(9):2578-86. DOI:10.1007/s11095-012-0788-3 pp.2578-86
Source: PubMed

ABSTRACT To explore the application of scanning ion occlusion sensing (SIOS) as a novel technology for characterization of nanoparticles.
Liposomes were employed as model nanoparticles. The size distribution of the liposomes was measured by both SIOS and dynamic light scattering (DLS). Particle number concentration was determined based on particle translocation rate. The ability of SIOS and DLS to resolve bimodal samples was evaluated by measuring a mixture of 217 and 355 nm standard nanoparticles. Opsonization of liposomes by plasma was also studied using SIOS.
SIOS was shown to measure the size of different liposomes with higher sensitivity than DLS and it requires a smaller sample volume than DLS. With appropriate calibration, SIOS could be used to determine particle number concentrations. In comparison, SIOS analysis of the mixture showed accurate resolution of the population as a bimodal distribution over a wide range of number ratios of the particles. SIOS could detect plasma opsonization of liposomes by demonstrating a increase in particle size and also changes in the particle translocation rate.
SIOS is a useful technology for nanoparticle characterization. It shows some advantages over DLS and is clearly a useful tool for the study of nanoparticle drug delivery systems.

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Keywords

355 nm standard nanoparticles
 
accurate resolution
 
bimodal distribution
 
bimodal samples
 
different liposomes
 
dynamic light scattering
 
model nanoparticles
 
nanoparticle characterization
 
nanoparticle drug delivery systems
 
number ratios
 
Opsonization
 
Particle number concentration
 
particle number concentrations
 
particle size
 
particle translocation rate
 
plasma opsonization
 
scanning ion occlusion
 
SIOS analysis
 
size distribution
 
smaller sample volume