Effect of Various Parameters on Encapsulation Efficiency of Mpeg-Plga Nanoparticles: Artificial Neural Network

Effect of Various Parameters on Encapsulation Efficiency of Mpeg-Plga Nanoparticles: Artificial Neural Network

Malekpour MR¹ ; Naghibzadeh M² ; Najafabadi MRH¹ ; Esnaashari SS¹ ; Adabi M³ ; Mujokoro B¹ ; Khosravani M¹ ; Adabi M³

Show Affiliations

1. Department of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
2. Department of Nanotechnology, Research and Clinical Centre for Infertility, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
3. Young Researchers and Elite Club, Roudehen Branch, Islamic Azad University, Roudehen, Iran

Source: Biointerface Research in Applied Chemistry Published:2018

Abstract

In this work we prepared curcumin loaded mPEG-PLGA nanoparticles using precipitation technique and investigated the effect of various parameters such as polyvinyl alcohol (PVA), curcumin concentrations and stirrer time on encapsulation efficiency (EE) of curcumin into mPEG-PLGA nanoparticles. Artificial neural networks (ANN) were used to model the data in order to find an ideal model which can fit the data and predict the EE with the lowest error and highest linear regression. The different samples of nanoparticles were prepared as training and testing datasets using the k-fold cross validation procedure. The best ANN design comprised 2 hidden layers with 8 and 1 nodes in each layer, respectively. Levenberg-Marquardt back propagation with log-sigmoid transfer function was the best model for our datasets. The mean square error and correlation coefficient between the observed and the predicted EE of curcumin into mPEG-PLGA nanoparticles were 0.1609 and 0.9209, respectively. In addition, three-dimensional correlation graphs showed that the most important pairs of variables which had a greater impact on EE were mPEG-PLGA/curcumin concentration and mPEG-PLGA/PVA concentration. © 2018 by the authors.

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Mahdi Adabi (10)

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Style	Citing Format
MLA	Malekpour MR, et al.. "Effect of Various Parameters on Encapsulation Efficiency of Mpeg-Plga Nanoparticles: Artificial Neural Network." Biointerface Research in Applied Chemistry, vol. 8, no. 3, 2018, pp. 3267-3272.
APA	Malekpour MR, Naghibzadeh M, Najafabadi MRH, Esnaashari SS, Adabi M, Mujokoro B, Khosravani M, Adabi M (2018). Effect of Various Parameters on Encapsulation Efficiency of Mpeg-Plga Nanoparticles: Artificial Neural Network. Biointerface Research in Applied Chemistry, 8(3), 3267-3272.
Chicago	Malekpour MR, Naghibzadeh M, Najafabadi MRH, Esnaashari SS, Adabi M, Mujokoro B, Khosravani M, Adabi M. "Effect of Various Parameters on Encapsulation Efficiency of Mpeg-Plga Nanoparticles: Artificial Neural Network." Biointerface Research in Applied Chemistry 8, no. 3 (2018): 3267-3272.
Harvard	Malekpour MR et al. (2018) 'Effect of Various Parameters on Encapsulation Efficiency of Mpeg-Plga Nanoparticles: Artificial Neural Network', Biointerface Research in Applied Chemistry, 8(3), pp. 3267-3272.
Vancouver	Malekpour MR, Naghibzadeh M, Najafabadi MRH, Esnaashari SS, Adabi M, Mujokoro B, et al.. Effect of Various Parameters on Encapsulation Efficiency of Mpeg-Plga Nanoparticles: Artificial Neural Network. Biointerface Research in Applied Chemistry. 2018;8(3):3267-3272.
BibTex	@article{ author = {Malekpour MR and Naghibzadeh M and Najafabadi MRH and Esnaashari SS and Adabi M and Mujokoro B and Khosravani M and Adabi M}, title = {Effect of Various Parameters on Encapsulation Efficiency of Mpeg-Plga Nanoparticles: Artificial Neural Network}, journal = {Biointerface Research in Applied Chemistry}, volume = {8}, number = {3}, pages = {3267-3272}, year = {2018} }
RIS	TY - JOUR AU - Malekpour MR AU - Naghibzadeh M AU - Najafabadi MRH AU - Esnaashari SS AU - Adabi M AU - Mujokoro B AU - Khosravani M AU - Adabi M TI - Effect of Various Parameters on Encapsulation Efficiency of Mpeg-Plga Nanoparticles: Artificial Neural Network JO - Biointerface Research in Applied Chemistry VL - 8 IS - 3 SP - 3267 EP - 3272 PY - 2018 ER -

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