Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia.
Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia.
Int J Pharm. 2021 May 15;601:120507. doi: 10.1016/j.ijpharm.2021.120507. Epub 2021 Mar 23.
The aim of this study was to apply artificial neural networks as deep learning tools in establishing a model for understanding and prediction of diazepam release from fused deposition modeling (FDM) printed tablets. Diazepam printed tablets of various shapes were created by a computer-aided design (CAD) program and prepared by fused deposition modeling using previously prepared polyvinyl alcohol/diazepam filaments via hot-melt extrusion. The surface to volume ratio (SA/V) for each shape was calculated. Printing parameters were varied including infill density (20%, 70% and 100%) and infill pattern (line and zigzag). Influence of tablet SA/V ratio and printing parameters (infill density and infill pattern) on the release of diazepam from printed tablets were modeled using self-organizing maps (SOM) and multi-layer perceptron (MLP). SOM as an unsupervised neural network was used for visualizing interrelation among the data, whereas MLP was used for the prediction of drug release properties. MLP had three layers (with structure 2-3-5) and was trained using back propagation algorithm. Input parameters for the modeling were: infill density and SA/V ratio; while output parameters were percent of drug release in five time points. The data set for network training was divided into training, validation and test sets. The dissolution rate increased with higher SA/V ratio, lower infill density (less than 50%) and zigzag infill pattern. The established ANN model was tested; calculated f 2 factors for two tested formulations (70.24 and 77.44) showed similarity between experimentally observed and predicted drug release profiles. Trained MLP network was able to predict drug release behavior as a function of infill density and SA/Vol ratio, as established design space for formulated 3D printed diazepam tablets.
本研究旨在应用人工神经网络作为深度学习工具，建立一个理解和预测融合沉积建模（FDM）打印片剂中地西泮释放的模型。通过计算机辅助设计（CAD）程序创建各种形状的地西泮打印片剂，并使用先前制备的聚乙烯醇/地西泮长丝通过热熔挤出通过 FDM 进行制备。计算每个形状的表面积与体积比（SA/V）。改变打印参数，包括填充密度（20%、70%和 100%）和填充图案（线和之字形）。使用自组织映射（SOM）和多层感知器（MLP）对片剂 SA/V 比和打印参数（填充密度和填充图案）对地西泮从打印片剂中释放的影响进行建模。SOM 作为一种无监督神经网络用于可视化数据之间的相互关系，而 MLP 用于预测药物释放特性。MLP 具有三个层（结构为 2-3-5），并使用反向传播算法进行训练。建模的输入参数为：填充密度和 SA/V 比；而输出参数为五个时间点的药物释放百分比。网络训练数据集分为训练集、验证集和测试集。溶解速率随 SA/V 比增加、填充密度降低（小于 50%）和之字形填充图案而增加。所建立的 ANN 模型进行了测试；两个测试配方（70.24 和 77.44）的计算 f 2 因子表明实验观察到的和预测的药物释放曲线之间具有相似性。经过训练的 MLP 网络能够预测作为填充密度和 SA/Vol 比的函数的药物释放行为，作为已制定的 3D 打印地西泮片剂的设计空间。
