神经网络用于口服缓释制剂的处方设计

目的　用反向传播(backpropagation ,BP)神经网络,从药物的溶解度设计符合一定释放度要求的缓释制剂处方。方法　选取9种药物(异烟肼、利巴韦林、盐酸地尔硫,盐酸雷尼替丁、盐酸环丙沙星、茶碱、替硝唑、丙基硫氧嘧啶、磺胺甲唑)作为模型药物,按HPMC∶糊精=(5 - 0.2 )∶1配比制成不同释放度的缓释片,测定各个处方的释放度,其释放度数据用于BP神经网络的建模、训练。结果　得到隐含层为一层、结点数为5个和迭代次数为25次的最佳神经网络,并成功拟定了4个制剂处方,按此处方制备的缓释片的实测释放值与神经网络预测值相符。结论由MATLAB 5.1中的BP神经网络和优化工具箱编写的程序可根据药物溶解度设计符合某一释放度要求的缓释制剂处方

APPLICATION OF AN ARTIFICIAL NEURAL NETWORK IN THE DESIGN OF SUSTAINED-RELEASE DOSAGE FORMS

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.