[人工神经网络在缓释剂型设计中的应用]
[Application of an artificial neural network in the design of sustained-release dosage forms].
作者信息
Wei X H, Wu J J, Liang W Q
机构信息
College of Pharmacy, Zhejiang University, Hangzhou 310031, China.
出版信息
Yao Xue Xue Bao. 2001 Sep;36(9):690-4.
AIM
To use the artificial neural network (ANN) in Matlab 5.1 tool-boxes to predict the formulations of sustained-release tablets.
METHODS
The solubilities of nine drugs and various ratios of HPMC: Dextrin for 63 tablet formulations were used as the ANN model input, and in vitro accumulation released at 6 sampling times were used as output.
RESULTS
The ANN model was constructed by selecting the optimal number of iterations (25) and model structure in which there are one hidden layer and five hidden layer nodes. The optimized ANN model was used for prediction of formulation based on desired target in vitro dissolution-time profiles. ANN predicted profiles based on ANN predicted formulations were closely similar to the target profiles.
CONCLUSION
The ANN could be used for predicting the dissolution profiles of sustained release dosage form and for the design of optimal formulation.
目的
利用Matlab 5.1工具箱中的人工神经网络（ANN）预测缓释片的处方。
方法
将9种药物的溶解度以及63种片剂处方中不同比例的羟丙基甲基纤维素（HPMC）与糊精作为ANN模型的输入，6个取样时间点的体外累积释放量作为输出。
结果
通过选择最佳迭代次数（25次）和具有一个隐藏层及五个隐藏层节点的模型结构构建了ANN模型。基于期望的目标体外溶出时间曲线，使用优化后的ANN模型预测处方。基于ANN预测处方得到的ANN预测曲线与目标曲线非常相似。
结论
ANN可用于预测缓释剂型的溶出曲线以及优化处方设计。
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