کاربرد ANN در طراحی و اپتیمم کردن نانوپارتیکلهای فینگولیمود با استفاده از پلیمر زیست تخریب پذیر پلی هیدروکسی بوتیرات والرات(2017)

شهسواری, شاداب and رضایی شیرمرد, لیلا and امینی, محسن and عابدین درکوش, فرید (1395) کاربرد ANN در طراحی و اپتیمم کردن نانوپارتیکلهای فینگولیمود با استفاده از پلیمر زیست تخریب پذیر پلی هیدروکسی بوتیرات والرات(2017). Journal of Pharmaceutical Sciences ــ 106 (1). pp. 176-182. شاپا 0022-3549

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Official URL: http://www.sciencedirect.com/science/journal/00223...


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)

English Abstract

Formulation of a nanoparticulate Fingolimod delivery system based on biodegradable poly(3hydroxybutyrate-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 accordingtostatistical analysis. ANNs areemployed togenerate the best model todetermine 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 formulationwas achieved byLM trainingfunctionwith 15 hiddenlayers 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).

Item Type:Article
زبان سند : انگلیسی
نویسنده اول :شاداب شهسواری
نویسنده :لیلا رضایی شیرمرد
نویسنده مسئول :فرید عابدین درکوش
Additional Information:Impact Factor (2015) 2.641 Indexed in: ISI, Pubmed/Medline/Index Medicus, Scopus, Reaxys, Current Chemical Reactions, Current Contents/Life Sciences, International Pharmaceutical Abstracts, EMBASE, Protozoological Abstracts, Biological Abstracts, BIOSIS Previews
کلیدواژه ها (انگلیسی):artificial neural network , drug delivery , Fingolimod , poly(3-hydroxybutyrate-co-3-hydroxyvalerate) , response surface methodology , training algorithms
Subjects:QV pharmacology > QV 704 Pharmaceutics
Divisions:School of Pharmacy > Department of Pharmaceutics
ID Code:8564
Deposited On:14 Jan 1396 07:28
Last Modified:06 Jun 1397 13:36

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