Application of artificial neural network model in predicting physicochemical characteristics of pharmaceutically developed wafers of loratadine

Abstract

This study aimed to apply the simultaneous optimization method incorporating artificial neural network (ANN) using multiâ€layer perceptron (MLP) model to develop buccoadhesive pharmaceutical wafers containing loratadine with an optimized physicochemical property and drug release. The amount of sodium carboxymethyl cellulose and lactose monohydrate at three levels (−1, 0, +1) for each was selected as casual factors. Bioadhesive strength, disintegration time, percent swelling index and t70% as wafer properties were selected as output variables. Nine buccoadhesive wafers were prepared according to a 32 factorial design and their physicochemical property and dissolution tests were performed. Commercially available Statistica Neural Network Software (Stat Soft, Inc., Tulsa, OK, USA) was used throughout the study. The training process of MLP was completed until a satisfactory value of root mean square for the test data was obtained using back
propagation, conjugate gradient descent method. This work exemplifies the probability for an ANN with MLP, to support
in development of buccoadhesive wafers with enviable characteristics.

Key words: Artificial neural network, buccoadhesive, loratadine, multilayer perceptron, pharmaceutical wafers