توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Viscoelastic Properties of Dispersed Olanzapine\Glycerol Monooleate Matrices
نویسنده(ها): ،
اطلاعات انتشار: کنفرانس بین المللی پژوهش در مهندسی، علوم و تکنولوژی، سال
تعداد صفحات: ۹
The objective of this research is to develop controlled release system of water insolvable Olanzapine (OZ), using Glycerol monooleate (GMO) and Polyethylene glycol (PEG 033) and to study the effects of initial drug loading and different additives on rheological characteristics. Rheological characterization of all formulations was performed to determine rheological property and viscosity, flow and oscillatory rheological characterization of gel system were calculated and influence of different gel structure was studied to define the rheological characteristics of samples. The best model to explain flow behaviour of all formulation was Cross1 model and viscosity was obtained according to the constant shear rate.<\div>

۲Evaluation and Optimization of In–vitro Drug Release of Acyclovir Nanoparticles Using Artificial Neural Network
نویسنده(ها): ،
اطلاعات انتشار: کنفرانس بین المللی پژوهش در مهندسی، علوم و تکنولوژی، سال
تعداد صفحات: ۸
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).<\div>

۳Optimization of sustained release delivery Olanzapine system
نویسنده(ها): ،
اطلاعات انتشار: کنفرانس بین المللی علوم و مهندسی، سال
تعداد صفحات: ۸
The objective of this study is to develop controlled release formulation of water insoluble Olanzapine (OZ), using Glycerol monooleate (GMO) and Polyethylene glycol (PEG 300). A Box–Benkhen response surface methodology was applied to design gel system with 3 factors with an initial drug containing within the range of 2–4%, weight ratio of GMO\water (w\w) in the range of 2–4% and weight ratio of PEG 300\GMO (w\w) in the range of 2–6%. Dependent variables include entrapment efficacy (EE %), percentage of release at 12th and 168th hr and viscosity. A quadratic model as an appropriate equation has been selected to fit the entrapment efficacy (EE %). Percentage of release at 12th and 168th hr and for viscosity a cubic model is selected. Optimization of liquid crystalline phase was carried out based on statistical concept of experimental design. Validation test was carried out under optimum conditions of the parameters predicted by the polynomial model.Determination of liquid crystalline phases has been displayed by polarized light microscopy. In addition, entrapment efficacy was measured spectrophotometerically at 265 nm. In vitro release studies of OZ from prepared samples were conducted in PBS (pH 6.8) and drug was analyzed by spectrophotometer at 265 nm<\div>

۴Optimization of preparation of Insulin nanoparticles composed of quaternized aromatic derivatives of chitosan using artificial neural networks
نویسنده(ها): ،
اطلاعات انتشار: کنفرانس بین المللی علوم و مهندسی، سال
تعداد صفحات: ۷
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) 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<\div>
نمایش نتایج ۱ تا ۴ از میان ۴ نتیجه