分子结构对吲哚美辛自乳化给药系统成乳能力的影响  

Effect of Molecular Structures on Emulsification Capability of Indomethacin-contained Self-emulsifying Drug Delivery System

作　　者：何芮[1] 尹宗宁[1] 李凡凡[1] 沈雨婷 

机构地区：[1]四川大学华西药学院靶向药物与释药系统教育部重点实验室,四川成都610041

出　　处：《中国医药工业杂志》2014年第1期35-40,共6页Chinese Journal of Pharmaceuticals

基　　金：国家自然科学基金资助项目(30973659)

摘　　要：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。Titration method was used for the research on the phase behavior of indomethacin-contained self- emulsifying drug delivery system （SEDDS）. The quantitative structure-activity relationship （QSAR） models were established to describe the correlation between the molecular descriptors, such as quantum chemical, physical chemical and topological parameters, of the components and the self-emulsifying region of SEDDS loaded with indomethacin by multiple linear regression （MLR） and artificial neural network （ANN）. The results demonstrated that both models showed good predictive ability, and MLR model was superior to ANN model. The self-emulsification capability of oil and surfactants would be calculated from their molecular descriptors, which might guide for screening the optimal formulation.

关 键 词：吲哚美辛 自乳化给药系统 自乳化面积 分子描述符 定量构效关系 多元线性回归 人工神经网络 

分 类 号：O641[理学—物理化学] R944.9[理学—化学]