Modeling of Drug Released from Acyclovir Nanoparticles Based on Artificial Neural Networks

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 affecting drug 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. Criterion to stop training was the divergence of the RMSE (root mean squared error) between target and output values. Both methods including gradient descent and Levenberg-marquardt have showed similar results in the data formulation. 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). The next aim of this research was to investigate the performance of predictive algorithms. 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.