Artificial Neural Networks Modeling of Release of Olanzapine From Glycerol Monooleate Matrices

ISSN: 1875-628X (Online)
ISSN: 1570-1808 (Print)


Volume 11, 10 Issues, 2014


Download PDF Flyer




Letters in Drug Design & Discovery

Aims & ScopeAbstracted/Indexed in


Submit Abstracts Online Submit Manuscripts Online

Editor-in-Chief:
Atta-ur-Rahman, FRS
Honorary Life Fellow
Kings College
University of Cambridge
Cambridge
UK
Email: lddd@benthamscience.org

View Full Editorial Board

Subscribe Purchase Articles Order Reprints

Current: 0.961
5 - Year: 0.917

Artificial Neural Networks Modeling of Release of Olanzapine From Glycerol Monooleate Matrices

Author(s): G. Bagheri, E. Vasheghani-Farahani, M. Ardjmand, H. Attar and F. A. Dorkoosh

Affiliation: Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran.

Abstract

The important aim of this research was to develop an appropriate model to predict relationships between three casual factors on the responses based on an artificial neural network (ANN). As model formulation, 28 types of gels were prepared. The weight ratio of GMO/water (w/w) and PEG 300/GMO (w/w), percentage of Olanzapine (OZ) were selected as input data. Entrapment efficacy, maximum percentage of release, particle size and viscosity were estimated as gel characterization. A set of gel characterization and input data were employed as tutorial data for ANN methodology by using neural network toolbox in Matlab.

Different topologies have been performed in order to determine the single network with good performance and accuracy. Four training algorithms (Levenberg–Marquardt, Bayesian- Regularization, BFGS Quasi-Newton, and Gradient Descent) were applied to train ANNs containing different numbers of hidden layers with various nods. The ability to predict the responses of all the algorithms were in the order of: BR > LM >BFGS> GD.


Keywords: Artificial neural network, Multilayer perceptron, Training algorithms, Olanzapine, Glycerol monooleate.

Purchase Online Rights and Permissions

  
  



Article Details

Volume: 11
Issue Number: 5
First Page: 636
Last Page: 648
Page Count: 13
DOI: 10.2174/1570180811666131227193958
Advertisement

Related Journals




Webmaster Contact: urooj@benthamscience.org Copyright © 2014 Bentham Science