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Drug Res (Stuttg) 2014; 64(3): 151-158
DOI: 10.1055/s-0033-1354372
Original Article
© Georg Thieme Verlag KG Stuttgart · New York

Application of Artificial Neural Networks for Optimization of Preparation of Insulin Nanoparticles Composed of Quaternized Aromatic Derivatives of Chitosan

Sh. Shahsavari
1   Department of Chemical Engineering, Varamin-Pishva Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
,
G. Bagheri
2   Department of Chemical Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
,
R. Mahjub
3   Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
,
R. Bagheri
4   Department of Computer Engineering, Graduate Faculty, Islamic Azad University, Tehran, Islamic Republic of Iran
,
M. Radmehr
3   Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
,
M. Rafiee-Tehrani
3   Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
,
F. A. Dorkoosh
3   Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
› Author Affiliations
Further Information

Publication History

received 15 June 2013

accepted 10 August 2013

Publication Date:
03 September 2013 (online)

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Abstract

The aim of this research was to develop an artificial neural network (ANN) in order to design a nanoparticulate oral drug delivery system for insulin. The pH of polymer solution (X1), concentration ratio of polymer/insulin (X2) and polymer type (X3) in 3 level including methylated N-(4-N,N- dimethyl aminobenzyl) chitosan, methylated N-(4-pyridinyl) chitosan, and methylated N-(benzyl) chitosan are considered as the input values and the particle size, zeta potential, PdI, and entrapment efficiency (EE %) as output data. ANNs are employed to generate the best model to determining the relationships between input and response values. In this research, a multi-layer percepteron with different topologies has been tested in order to define the one with the best accuracy and performance. The optimization was used by minimizing the error between the predicted and observed values. Three training algorithms (Levenberg-Marquardt (LM), Bayesian-Regularization (BR), and Gradient Descent (GD)) were employed to train ANNs with various numbers of nodes, hidden layers and transfer functions by random selection. The accuracy of prediction data were assayed by the mean squared error (MSE).The ability of all algorithms was in the order: BR>LM>GD. Thus, BR was selected as the best algorithm.

Key words

artifical neural network (ANN) - Pharmaceutius - nanoparticles - Levenberg-Marquadt algorithm - Bayesian-Regulation (BR) algorithm - Gardient-Descent algorithm
 

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