Application of Artificial Neural Networks in the Modeling of Drug Release from Acyclovir Nanoparticles

Formulation of controlled release acyclovir loaded chitosan nanoparticles was optimized based on the optimization technique using response surface method (RSM) and artificial neural network (ANN) simultaneously to develop a model to identify relationships between variables affectingdrug nanoparticles. In this research, the goal was to create a representation of three irregular factors, i.e. concentration of acyclovir, concentration ratio of chitosan/ Tripolyphosphate (TPP) and pH on response variables. ANN was used to create a fit model of formulations via these four training algorithms including: Levenberg–Marquardt (LM), Gradient Descent (GD), Bayesian– Regularization (BR) and BFGS Quasi-Newton (BFG) were applied to train ANN containing a various hidden layer, applying the testable data as the training set.Corresponding to batch back propagation (BBP)-ANN performance, a gain in pH of polymer solution reduced the size and polydispersity index (PdI) of nanoparticles. Moreover, decreases in the concentration ratio of chitosan/TPP consequently cause an increase in entrapment efficiency (%EE).For this reason each training algorithm in order to consider the accuracy of predictive ability was evaluated and the result was as follow: LM > BFGs > GD > BR.

Keywords:

Acyclovir , Artificial neural network (ANN) , Backpropagation , Drug delivery , Response surface methodology (RSM) , Training algorithms