Application of Artificial Neural Networks in the Design and Optimization of a Nanoparticulate Fingolimod Delivery System Based on Biodegradable Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate).
Formulation of a nanoparticulate Fingolimod delivery system based on biodegradable poly(3-hydroxybutyrate-co-3-hydroxyvalerate) was optimized according to artificial neural networks (ANNs). Concentration of poly(3-hydroxybutyrate-co-3-hydroxyvalerate), PVA and amount of Fingolimod is considered as the input value, and the particle size, polydispersity index, loading capacity, and entrapment efficacy as output data in experimental design study. In vitro release study was carried out for best formulation according to statistical analysis. ANNs are employed to generate the best model to determine the relationships between various values. In order to specify the model with the best accuracy and proficiency for the in vitro release, a multilayer percepteron with different training algorithm has been examined. Three training model formulations including Levenberg-Marquardt (LM), gradient descent, and Bayesian regularization were employed for training the ANN models. It is demonstrated that the predictive ability of each training algorithm is in the order of LM > gradient descent > Bayesian regularization. Also, optimum formulation was achieved by LM training function with 15 hidden layers and 20 neurons. The transfer function of the hidden layer for this formulation and the output layer were tansig and purlin, respectively. Also, the optimization process was developed by minimizing the error among the predicted and observed values of training algorithm (about 0.0341).
Department of Chemical Engineering, Varamin-Pishva Branch, Islamic Azad University, Varamin, Iran.
Shadab
S
Shahsavari
Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran; Department of Pharmaceutics, Faculty of Pharmacy, Ardebil University of Medical Science, Ardebil, Iran.
Leila
L
Rezaie Shirmard
Department of Medicinal Chemistry, Drug Design and Development Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Mohsen
M
Amini
Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran; Medical Biomaterials Research Center (MBRC), Tehran University of Medical Sciences, Tehran, Iran. Electronic address: dorkoosh@tums.ac.ir.
Farid
F
Abedin Dokoosh
D000465:Algorithms; D001499:Bayes Theorem; D004337:Drug Carriers / Q000737:chemistry*; D065546:Drug Liberation; D000068876:Fingolimod Hydrochloride / Q000008:administration & dosage* / Q000737:chemistry; D007166:Immunosuppressive Agents / Q000008:administration & dosage* / Q000737:chemistry; D008956:Models, Chemical; D053758:Nanoparticles / Q000737:chemistry*; D016571:Neural Networks, Computer; D010316:Particle Size; D011091:Polyesters / Q000737:chemistry*; D000094122:Polyhydroxybutyrates
D004337:Drug Carriers; D000068876:Fingolimod Hydrochloride; D007166:Immunosuppressive Agents; D011091:Polyesters; C052620:poly(3-hydroxybutyrate)-co-(3-hydroxyvalerate); D000094122:Polyhydroxybutyrates
Fingolimod; artificial neural network; drug delivery; poly(3-hydroxybutyrate-co-3-hydroxyvalerate); response surface methodology; training algorithms