Evaluation and Optimization of In-vitro Drug Release of Acyclovir Nanoparticles Using Artificial Neural Network

Optimization of controlled release acyclovir-chitosan nanoparticles was optimized based on the artificial neural network (ANN) to develop a model to identify relationships between variables affecting drug nanoparticles. In this research, the aim was to create a representation of three irregular factors, i.e. concentration of acyclovir, concentration ratio of chitosan to tripolyphosphate (TPP) and pH on response variables. ANN was used to create a perfect 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 de-scent and Levenberg-marquardt have showed similar results in the data formulation. Corresponding to batch back propaga-tion (BBP)-ANN performance, a gain in pH of polymer solution reduced the size and polydispersity index (PdI) of nanopar-ticles. Moreover, decreases in the concentration ratio of chitosan/TPP consequently cause an increase in entrapment efficien-cy (%EE).

کلیدواژه ها:

Acyclovir ، Artificial neural network (ANN) ، Training algorithms ، Drug delivery