Effects of surfactants on properties of polymer-coated magnetic nanoparticles for drug delivery application
Source abstract
Abstract
The objective of this research was to compare the effects of two different surfactants on the physicochemical properties of thermo-responsive poly(N-isopropylacrylamide-acrylamide-allylamine) (PNIPAAm-AAm-AH)-coated magnetic nanoparticles (MNPs). Sodium dodecyl sulfate (SDS) as a commonly used surfactant in nanoparticle formulation process and Pluronic F127 as an FDA approved material were used as surfactants to synthesize PNIPAAm-AAm-AH-coated MNPs (PMNPs). The properties of PMNPs synthesized using SDS (PMNPs-SDS) and PF127 (PMNPs-PF127) were compared in terms of size, polydispersity, surface charge, drug loading efficiency, drug release profile, biocompatibility, cellular uptake, and ligand conjugation efficiency. These nanoparticles had a stable core–shell structure with about a 100-nm diameter and were superparamagnetic in behavior with no difference in the magnetic properties in both types of nanoparticles. In vitro cell studies showed that PMNPs-PF127 were more cytocompatible and taken up more by prostate cancer cells than that of PMNPs-SDS. Cells internalized with these nanoparticles generated a dark negative contrast in agarose phantoms for magnetic resonance imaging. Furthermore, a higher doxorubicin release at 40 °C was observed from PMNPs-PF127, and the released drugs were pharmacologically active in killing cancer cells. Finally, surfactant type did not affect the conjugation efficiency to the nanoparticles when folic acid was used as a targeting ligand model. These results indicate that PF127 might be a better surfactant to form polymer-coated magnetic nanoparticles for targeted and controlled drug delivery.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nanoparticle-Based Drug Delivery · Characterization and Applications of Magnetic Nanoparticles · Hydrogels: synthesis, properties, applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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