Background and the purpose of the studyThe purpose of this study was to prepare pegylated poly lactide-co-glycolide (PEG-PLGA) nanoparticles (NPs) loaded with roxithromycin (RXN) with appropriate physicochemical properties and antibacterial activity. Roxithromycin, a semi-synthetic derivative of erythromycin, is more stable than erythromycin under acidic conditions and exhibits improved clinical effects.MethodsRXN was loaded in pegylated PLGA NPs in different drug;polymer ratios by solvent evaporation technique and characterized for their size and size distribution, surface charge, surface morphology, drug loading, in vitro drug release profile, and in vitro antibacterial effects on S. aureus, B. subtilis, and S. epidermidis.Results and conclusionNPs were spherical with a relatively mono-dispersed size distribution. The particle size of nanoparticles ranged from 150 to 200 nm. NPs with entrapment efficiency of up to 80.0±6.5% and drug loading of up to 13.0±1.0% were prepared. In vitro release study showed an early burst release of about 50.03±0.99% at 6.5 h and then a slow and steady release of RXN was observed after the burst release. In vitro antibacterial effects determined that the minimal inhibitory concentration (MIC) of RXN loaded PEG-PLGA NPs were 9 times lower on S. aureus, 4.5 times lower on B. subtilis, and 4.5 times lower on S. epidermidis compared to RXN solution. In conclusion it was shown that polymeric NPs enhanced the antibacterial efficacy of RXN substantially.
The aim of this study was to find a model using artificial neural networks (ANNs) to predict PLGA-PMBH nanoparticles (NPs) size in preparation by modified nanoprecipitation. The input variables were polymer content, drug content, power of sonication and ratio of organic/aqueous phase (i.e. acetone/water), while the NPs size of PLGA-PMBH was assumed as the output variable. Forty samples of PLGA-PMBH NPs containing anticancer drug (i.e. paclitaxel) were synthesized by changing the variable factors in the experiments. The data modeling were performed using ANNs. The effects of input variables (namely, polymer content, drug content, power of sonication and ratio of acetone/water) on the output variables were evaluated using the 3D graphs obtained after modeling. Contrasting the 3D graphs from the generated model revealed that the amount of polymer (PLGA-PMBH) and drug content (PTX) have direct relation with the size of polymeric NPs in the process. In addition, it was illustrated that the ratio of acetone/water was the most important factor affecting the particle size of PLGA-PMBH NPs provided by solvent evaporation technique. Also, it was found that increasing the sonication power (up to a certain amount) indirectly affects the polymeric NPs size however it was directly affected in higher values.
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