Abstract

MODELING AND OPTIMIZATION OF DRUG RELEASE FROM LEVOFLOXACIN HEMIHYDRATE FLOATING MATRIX TABLET USING ARTIFICIAL NEURAL NETWORK

*Neetu Khatri, Pauroosh Kaushal, Dr. Ajay Bilandi, Dr. Mahesh Kumar Kataria

ABSTRACT

Artificial neural network is employed for the modeling of drug release of Levofloxacin Hemihydrate floating matrix tablet. Based on the modeling of drug release, optimized formulation is estimated and compared to the experimental data. ANN is trained, tested and validated by a set of experimental data in which different concentration of HPMC K4M, Eudragit RS100, Sodium Alginate, Guar gum and release time are taken as input parameters while percent drug release is taken as target data. Multi-Layer Perceptron (MLP) with Back-Propagation learning algorithm is used to specify the model. The performance of the model having different hidden nodes and different hidden layers is evaluated using root mean square criteria. The best model is selected to predict drug release from a given input data so that optimized formulation can be selected. The results show that ANN can be used to model drug release and an optimal tablet formulation can be estimated.

Keywords: Artificial Neural Network, Modeling, Optimization, Levofloxacin Hemihydrate, Drug Release.