Effect of various formulation parameters on the properties of polymeric nanoparticles prepared by multiple emulsion method.

This work evaluates and interprets underlying mechanisms behind various aspects related to preparation and physical characteristics of polymeric nanoparticles (NP). These were prepared from different biodegradable polymers according to a water-in-oil-in-water emulsion solvent evaporation method. Polymers used were poly(lactic-co-glycolic) acid (PLGA), poly (lactic acid) (PLA), (PLA-PEG-PLA) triblock and (PLA-PEG-PLA)n multi-block co-polymers. A model DNA, as an example of a hydrophilic drug, was encapsulated in the internal aqueous phase. The primary emulsion was prepared using a high shear turbine mixer. The secondary emulsion was prepared by high-pressure homogenization. Surface morphology and internal structure were characterized by scanning electron microscopy (SEM) and atomic force microscopy (AFM). Influence of process variables on the physical properties of NP has been studied. Release of DNA was evaluated. In addition, changes occurring to NP porosity and surface area during degradation were followed. Nanoparticle size was ranging between 200-700 nm, according to the preparation conditions. Homogenizing pressure, concentration of the emulsifying agent used, polymer concentration and type and the concentration of a cryoprotectant had variable effects on NP size, surface area and porosity. Batches of NP where no emulsifying agent was added were obtained successfully. The release rate of the DNA from NP was mainly dependent on porosity, which varied significantly among used polymers. The preparation technique was efficient in encapsulating the model DNA and will be used for plasmid encapsulation in a future work.

Artificial neural networks: comparison of two programs for modeling a process of nanoparticle preparation.

Artificial Neural Networks (ANNs) were used to predict nanoparticle size and micropore surface area of polylactic acid nanoparticles, prepared by a double emulsion method. Different batches were prepared while varying polymer and surfactant concentration, as well as homogenization pressure. Two commercial ANNs programs were evaluated: Neuroshell Predictor, a black-box software adopting both neural and genetic strategies, and Neurosolutions, allowing a step-by-step building of the network. Results were compared to those obtained by statistical method. Predictions from ANNs were more accurate than those calculated using non-linear regression. Neuroshell Predictor allowed quantification of the relative importance of the inputs. Furthermore, by varying the network topology and parameters using Neurosolutions, it was possible to obtain output values which were closer to experimental values. Therefore, ANNs represent a promising tool for the analysis of processes involving preparation of polymeric carriers and for prediction of their physical properties.