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The aim of this research was to develop an artificial neural network (ANN) in order to design a nanoparticulate oral drug delivery system for insulin. The pH of polymer solution (X1), concentration ratio of polymer/insulin (X2) and polymer type (X3) in 3 level including methylated N-(4-N,N- dimethyl aminobenzyl) chitosan, methylated N-(4-pyridinyl) chitosan, and methylated N-(benzyl) chitosan are considered as the input values and the particle size, zeta potential, PdI, and entrapment efficiency (EE %) as output data. ANNs are employed to generate the best model to determining the relationships between input and response values. In this research, a multi-layer percepteron with different topologies has been tested in order to define the one with the best accuracy and performance. The optimization was used by minimizing the error between the predicted and observed values. Three training algorithms (Levenberg-Marquardt (LM), Bayesian-Regularization (BR), and Gradient Descent (GD)) were employed to train ANNs with various numbers of nodes, hidden layers and transfer functions by random selection. The accuracy of prediction data were assayed by the mean squared error (MSE).The ability of all algorithms was in the order: BR>LM>GD. Thus, BR was selected as the best algorithm.

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).

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There is an increasing level of various pollutants and their persistence in aquatic environments. The improper use of antibiotics and their inefficient metabolism in organisms result in their release into aquatic environments. Antibiotic abuse has led to hazardous effects on human health. Thereby, efficient removal of pharmaceuticals, particularly antibiotics, from wastewater and contaminated water bodies is greatly interested in international research communities. Metal‐organic framework (MOF) materials, as a hybrid group of material containing metallic center and organic linkers, offer a porous structure that is highly efficient for removing different pollutants from contaminated water and wastewater streams. This article aims to review the recent advancement in using MOF‐based adsorbents and catalysts for the removal of pharmaceuticals, especially antibiotics, from polluted water. Applying MOFs‐based structures for removing antibiotics using photocatalytic removal and adsorptive removal techniques will be discussed and evaluated in this review paper. Various MOF‐based materials such as functionalized MOFs, MOF‐based composites, magnetic MOF‐based composites, MOFs templated‐metal oxide catalysts for removing pharmaceuticals, personal care products, and antibiotics from contaminated aqueous media are discussed. Furthermore, effective operational parameters on the adsorption, adsorption mechanisms, adsorption isotherms, and thermodynamic parameters are explained and discussed. Finally, in the concluding remarks, the challenges and future outlooks of using MOFs‐based adsorbents and catalysts for removing antibiotics are summarized.