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通过滴定法研究吲哚美辛在自乳化处方中的相行为,利用多元线性回归(MLR)与人工神经网络(ANN)分别建立自乳化面积与处方中各组分的分子描述符(如量子化学参数、物理化学参数和分子拓扑学参数)之间的定量构效关系(QSAR)模型。研究表明,MLR模型与ANN模型对处方自乳化面积均具有较好的预测能力,且MLR模型的预测能力优于ANN模型。通过油相、乳化剂等组分的分子结构计算其在系统中的自乳化能力,有助于进行处方筛选,提示利用计算的方法可提高试验效率。
The phase behavior of indometacin in self-emulsifying formulations was studied by titration. The molecular descriptors of self-emulsifying area and prescription of each component were established by multivariate linear regression (MLR) and artificial neural network (ANN) Parameters, physicochemical parameters and molecular topological parameters) of the quantitative structure-activity relationship (QSAR) model. The results show that the MLR model and the ANN model have better predictive ability for prescription self-emulsifying area, and the prediction ability of MLR model is better than the ANN model. Through the oil phase, emulsifier and other components of the molecular structure of its self-emulsifying ability in the system to help prescribe screening, suggesting that the use of computational methods can improve the test efficiency.