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.
Rizkalla
Névine
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Faculté de Pharmacie, Université de Montréal, Montréal, Canada.
Hildgen
Patrice
P
D004339:Drug Compounding / Q000706:statistics & numerical data*; D017143:Fuzzy Logic; D005823:Genetics / Q000706:statistics & numerical data; D015233:Models, Statistical; D049329:Nanostructures; D016571:Neural Networks, Computer; D009474:Neurons; D010316:Particle Size; D016062:Porosity; D025341:Principal Component Analysis; D011897:Random Allocation; D012044:Regression Analysis; D015203:Reproducibility of Results; D012984:Software