Bovine serum albumin (BSA) is usually used for generation of nanoparticles (NPs) in drug delivery systems. This paper presents a study on the simple coacervation method for fabrication of BSA nanoparticles.An artificial neural network (ANN) model was developed to predict the diameter of nanoparticles based on experimental data. Effect of pH, temperature, initial BSA and cross linker concentration, molar ratio of BSA to ethanol, agitation rate and rate of ethanol addition were investigated on the protein particle size by ANN. Developedmodel was presented by multilayer perceptron and checked the adequacy of the ANN model by the linear regression analyses. The calculated results were in good agreement with experimental data and developed model
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Recently, NP has been concerned large attention in the basic research and their application in different fields, where they can be used as electrochemical sensors [7], catalyst [8] , lubricating oils[9], degradation of pollutants[10], drug delivery[11-13] and diagnostics devices[14, 15].
For pharmaceutical use, NPs can be of polymeric nature or be made of lipophilic components plus surfactants, i.e., liposomes, niosomes, and solid lipid NPs. Other materials investigated for NPs preparation are albumin [16], gelatin[17] ,or calcium alginate[18]. There are also several methods available to size-reduce drug substances into the nanometer range, with the produced NPs being stabilized by surfactants[19, 20].
Albumin can form layers on NP, which are stabilized by denaturation of the protein. This denaturation can be introduced by cross-linking agents. Albumin NP have been extensively investigated in our previous study plus in other research teams as suitable carrier for drug delivery vehicles, since they are non-toxic,biodegradable, and non-antigenic[6, 21-23]. Due to their defined primary and high content of charged amino acids (i.e. Lysine) the albumin based NPs could allow electrostatic adsorption of positively or negatively charged molecules without the requirements of other compound …show more content…
However, in some cases, those models do not apply due to the lack of experimental information or to the complexity of system. Artificial neural networks are being increasingly applied to complement and replace the classical approach whenever necessary [24, 25]. In last two decades, artificial neural network (ANN) has emerged as an attractive tool for non-linear multivariate modeling [26]. Even if the mechanism of reaction is not described clearly, this technique can still predict outputs accurately for a new input data set [27, 28]. The ability of ANNs to learn and generalize the behavior of any complex and non-linear process makes it a powerful modeling tool
Recently, NP has been concerned large attention in the basic research and their application in different fields, where they can be used as electrochemical sensors [7], catalyst [8] , lubricating oils[9], degradation of pollutants[10], drug delivery[11-13] and diagnostics devices[14, 15].
For pharmaceutical use, NPs can be of polymeric nature or be made of lipophilic components plus surfactants, i.e., liposomes, niosomes, and solid lipid NPs. Other materials investigated for NPs preparation are albumin [16], gelatin[17] ,or calcium alginate[18]. There are also several methods available to size-reduce drug substances into the nanometer range, with the produced NPs being stabilized by surfactants[19, 20].
Albumin can form layers on NP, which are stabilized by denaturation of the protein. This denaturation can be introduced by cross-linking agents. Albumin NP have been extensively investigated in our previous study plus in other research teams as suitable carrier for drug delivery vehicles, since they are non-toxic,biodegradable, and non-antigenic[6, 21-23]. Due to their defined primary and high content of charged amino acids (i.e. Lysine) the albumin based NPs could allow electrostatic adsorption of positively or negatively charged molecules without the requirements of other compound …show more content…
However, in some cases, those models do not apply due to the lack of experimental information or to the complexity of system. Artificial neural networks are being increasingly applied to complement and replace the classical approach whenever necessary [24, 25]. In last two decades, artificial neural network (ANN) has emerged as an attractive tool for non-linear multivariate modeling [26]. Even if the mechanism of reaction is not described clearly, this technique can still predict outputs accurately for a new input data set [27, 28]. The ability of ANNs to learn and generalize the behavior of any complex and non-linear process makes it a powerful modeling tool