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Application of Artificial Neural Networks in Controlled Drug Delivery Systems Publisher



Rafienia M1 ; Amiri M2 ; Janmaleki M3 ; Sadeghian A4
Authors
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Authors Affiliations
  1. 1. Medical Physics and BioMedical Engineering Department, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81744-176, Iran
  2. 2. Medical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
  3. 3. Nanomedicine and Tissue Engineering Research Center, Shahid Beheshti University (M.C.), Tehran, Iran
  4. 4. Department of Computer Science, Ryerson University, Toronto, ON, Canada

Source: Applied Artificial Intelligence Published:2010


Abstract

Estimation of release profiles of drugs normally requires time-consuming trial-and-error experiments. Feed-forward neural networks including multilayer perceptron (MLP), radial basis function network (RBFN), and generalized regression neural network (GRNN) are used to predict the release profile of betamethasone (BTM) and betamethasone acetate (BTMA) where in situ forming systems consist of poly (lactide-co-glycolide), N-methyl-1-2-pyrolidon, and ethyl heptanoat as a polymer, solvent, and additive, respectively. The input vectors of the artificial neural networks (ANNs) include drug concentration, gamma irradiation, additive substance, and type of drug. As the outputs of the ANNs, three features are extracted using the nonlinear principal component analysis technique. Leave-one-out cross-validation approach is used to train each ANN. We show that for estimation of BTM and BTMA release profiles, MLP outperforms GRNN and RBF networks in terms of reliability and efficiency. Copyright © 2010 Taylor & Francis Group, LLC.
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