Department of Textile Engineering, Textile Excellence & Research Centers, Amirkabir University of Technology (Tehran Polytechnic), Hafez Avenue, 1591634311, Tehran, Iran; Institute of Inorganic Chemistry, Inorganic and Materials Chemistry, University of Cologne, Greinstraße 6, D-50939 Cologne, Germany.
Comput Methods Programs Biomed. 2014;113(1):92-100. doi: 10.1016/j.cmpb.2013.09.003. Epub 2013 Sep 14.
Release profile of drug constituent encapsulated in electrospun core-shell nanofibrous mats was modeled by Peppas equation and artificial neural network. Core-shell fibers were fabricated by co-axial electrospinning process using tetracycline hydrochloride (TCH) as the core and poly(l-lactide-co-glycolide) (PLGA) or polycaprolactone (PCL) as the shell materials. The density and hydrophilicity of the shell polymers, feed rates and concentrations of core and shell phases, the contribution of TCH in core material and electrical field were the parameters fed to the perceptron network to predict Peppas constants in order to derive release pattern. This study demonstrated the viability of the prediction tool in determining drug release profile of electrospun core-shell nanofibrous scaffolds.
采用 Peppas 方程和人工神经网络对包埋在电纺核壳纳米纤维垫中的药物成分的释放情况进行了建模。采用同轴电纺工艺制备核壳纤维，以盐酸四环素（TCH）为芯材，聚（L-丙交酯-共-乙交酯）（PLGA）或聚己内酯（PCL）为壳材。壳聚合物的密度和亲水性、芯和壳相的进料速率和浓度、芯材料中 TCH 的贡献以及电场是输入到感知器网络中的参数，用于预测 Peppas 常数，以得出释放模式。本研究表明，该预测工具可用于确定电纺核壳纳米纤维支架的药物释放情况。
