优化药物制剂工艺的多种数据处理方法的研究进展  

Advance in Research on Data Processing Methods of Optimizing Drug Preparation Process

作　　者：涂颖秋[1] 朱孟夏[1] 刘会[1] 黄绳武[1] 

机构地区：[1]浙江中医药大学药学院,杭州310053

出　　处：《中国药学杂志》2013年第16期1333-1337,共5页Chinese Pharmaceutical Journal

摘　　要：目的综述优化药物制剂工艺的多种数据处理方法的研究进展。方法通过查阅国内外相关文献,在单指标数据处理方法的基础上,对多种数据处理方法进行比较、分析和总结。结果方差分析-多指标综合加权评分法、多元回归分析-效应面法、人工神经网络、多维空间三角形面积法等多指标的数据处理方法在优化药物制剂工艺中已得到广泛应用及有一定的适用范围。结论方差分析-多指标综合加权评分法、多元回归分析-效应面法、人工神经网络、多维空间三角形面积法、代谢动态数学模型等多指标数据处理方法都能揭示多因素多水平之间的规律,为优化药物制剂工艺提供可借鉴的参考。OBJECTIVE To summarize the advance in research on a variety of data processing methods of optimizing drug preparation process. METHODS On the basis of data processing methods of single index, this paper compares, analyzes and summarizes a variety of data processing methods in relevant literature. RESULTS Multiple indicator data processing method, such as analysis of variance-comprehensive weighted of multi-index, multiple regression analysis-response surface methodology, artificial neural network, and multi-dimensional space triangle area, has been widely used in the optimization of drug preparation process in a certain scope. CONCLUSION Analysis of variance-comprehensive weighted of multi-index, multiple regression analysis-response surface methodology, artificial neural networks, and multidimensional spatial triangular area can reveal the principles among multi-factors of multi-levels, thus can provide reference for optimizing drug preparation technology.

关 键 词：方差分析-多指标综合加权评分法 多元回归分析·效应面法 人工神经网络 多维空间三角形面积法 

分 类 号：R944[医药卫生—药剂学]