Application of artificial neural networks (ANN) in the development of solid dosage forms.
Application of artificial neural networks (ANN) in the development of solid dosage forms.
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DOI：
10.3109/10837459709022616
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发表时间：
1997-05-01
影响因子：
3.4
通讯作者：
Leuenberger, H
中科院分区：
文献类型：
--
作者：
Bourquin, J;Schmidli, H;Leuenberger, H
通过理论和典型的制药技术实例，对人工神经网络在制药开发中的应用进行了评估。目的是定量描述已实现的数据拟合和预测能力的模型，以期在固体剂型的发展中使用人工神经网络。利用相关系数R2的平方将人工神经网络与传统的统计（即响应面法，RSM）建模技术进行了比较。使用高度非线性任意函数，ANN模型显示出更好的拟合能力（R2 = 0.931 vs. R2 = 0.424）以及预测能力（R2 = 0.810 vs. R2 = 0.547）。来自平板电脑压缩研究的实验数据使用两种类型的人工神经网络模型（即多层感知器和由自组织特征映射加入多层感知组成的混合网络）进行拟合。三种方法的拟合结果具有可比性（MLP R2 = 0.911, SOFM-MLP R2 = 0.850, RSM R2 = 0.897）。当应用于制药技术数据集时，人工神经网络方法代表了一种很有前途的建模技术。
The application of ANN in pharmaceutical development has been assessed using theoretical as well as typical pharmaceutical technology examples. The aim was to quantitatively describe the achieved data fitting and predicting abilities of the models developed with a view to using ANN in the development of solid dosage forms. The comparison between the ANN and a traditional statistical (i.e., response surface methodology, RSM) modeling technique was carried out using the squared correlation coefficient R2. Using a highly nonlinear arbitrary function the ANN models showed better fitting (R2 = 0.931 vs. R2 = 0.424) as well as predicting (R2 = 0.810 vs. R2 = 0.547) abilities. Experimental data from a tablet compression study were fitted using two types of ANN models (i.e., multilayer perceptrons and a hybrid network composed of a self-organising feature map joined to a multilayer perception). The achieved data fitting was comparable for the three methods (MLP R2 = 0.911, SOFM-MLP R2 = 0.850, and RSM R2 = 0.897). ANN methodology represents a promising modeling technique when applied to pharmaceutical technology data sets.