Bourquin J, Schmidli H, van Hoogevest P, Leuenberger H
Pharmaceutical and Analytical Development, Novartis Pharma AG, CH-4002 Basel, Switzerland.
Eur J Pharm Sci. 1998 Dec;7(1):17-28. doi: 10.1016/s0928-0987(97)10027-6.
An application of the Artificial Neural Networks (ANN) methodology was investigated using experimental data from a mixture properties study and compared to classical modelling technique (i.e. Response Surface Methodology, RSM) both graphically and numerically. The aim of this investigation was to quantitatively describe the achieved degree of data fitting and robustness of the developed models. For comparing the goodness of fit, the R2 coefficient was used, whereas for the robustness of the models an outlier measurement was integrated in the data set. Comparable results were achieved for both ANN- and RSM methodologies for data fitting. The robustness of the models towards outliers was clearly better for the RSM methodology. All determined mixture properties were mainly influenced by the concentration of silica aerogel, whereas the other factors showed very much lower effects. For that reason the physical properties of this excipient (e.g. its specific surface area) are of importance for the behaviour of the mixtures.
利用混合物性质研究的实验数据，对人工神经网络（ANN）方法的应用进行了研究，并在图形和数值方面与经典建模技术（即响应面方法，RSM）进行了比较。本研究的目的是定量描述所实现的数据拟合程度和所开发模型的稳健性。为了比较拟合优度，使用了R2系数，而对于模型的稳健性，在数据集中集成了异常值测量。对于数据拟合，ANN方法和RSM方法都取得了可比的结果。对于异常值，RSM方法的模型稳健性明显更好。所有测定的混合物性质主要受二氧化硅气凝胶浓度的影响，而其他因素的影响则非常小。因此，这种辅料的物理性质（例如其比表面积）对混合物的行为很重要。
