Synthesis and Characterization of Gold-Nanoparticle-Loaded Block Copolymer Vectors for Biomedical Applications: A...

Gold nanoparticles (GNPs) encapsulated in amphiphilic block copolymers are a promising system for numerous biomedical applications, although critical information on the effects of various preparation variables on the structure and properties of this unique type of nanomaterial is currently missing from the literature. In this research, we synthesized GNPs functionalized with thiol-terminated polycaprolactone (PCL-GNPs) before encapsulating them into poly(ε-caprolactone)-b-poly(ethylene glycol) (PCL-b-PEG) micellar nanoparticles via nanoprecipitation to yield GNP-loaded polymeric nanoparticles (GNP-PNPs). We explored the role of different manufacturing variables (water volume, PCL-b-PEG to PCL-GNP ratio, and PEG block length) on the sizes, morphologies, GNP occupancies, colloidal gold concentrations, and time stability of GNP-PNPs. Despite our motivation to increase colloidal gold concentrations for K-edge CT imaging applications, there was only moderate variation in the concentration of colloidal gold (c_Au = ∼100-150 μg/mL) over the range of investigated experimental variables; however, postformulation exposure to compressed air flow provided samples with increased gold concentrations and CT contrast above the visual threshold in imaging phantoms. The range of formulation variables also had only a weak effect on mean effective hydrodynamic diameters (d_h,eff = ∼150 nm). Statistical analysis of TEM images revealed that the mean number of GNPs within GNP-PNP vectors smaller than 50 nm, Z_ave,d<50 nm, is generally higher for preparations involving PCL-b-PEG with the shorter of the two different PEG block lengths. Preparations with the shorter PEG block length copolymer were also found to produce GNP-PNP colloids with greater time stability in d_h,eff and c_Au. Consistent with our previous study using MB-MDA-231 cells, we found increased gold uptake in MCF-7 cells with increasing Z_ave,d<50 nm. This study provides a roadmap for optimizing important figures of merit for existing biomedical applications, including CT imaging and radiotherapy sensitization, and for developing new diagnostic and therapeutic strategies using GNP-PNPs.