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随机模拟在预测混合丸剂均匀度中的应用 被引量:1

Application of stochastic simulation in predicting the uniformity of mixed pills
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摘要 目的:建立预测复合丸剂混合后分装成单剂量的药丸比例和抽样RSD概率分布的数学模型。方法:通过随机模拟(蒙特卡罗)方法,采用Python 3.8程序和random库中随机函数,编写程序代码实现了两种药丸混合后单剂量分装的随机抽样模拟。结果:随机模拟抽样次数为10万次时,样品分布与二项分布特征基本一致,样本相对含量的概率分布随单剂量丸数增加变窄,RSD<6.8%的概率增加。结论:本研究建立的模型可预测复合丸剂混合后分装成单剂量的含量分布概率特征和含量均匀度抽检的最小失败概率。 Objective:To establish a mathematical model that can predict the distribution probability of the proportion of pills and the sampled RSD distribution probability in single doses after the compound pills are mixed and divided into a single dose.Method:By using the random simulation(Monte Carlo)method,Python 3.8 program,and random function in the random library,the program code was written to realize the random sampling simulation of dividing into single doses after mixing two kinds of pills.Results:When the number of random simulated sampling is 100,000 times,the sample distribution was basically consistent with the binomial distribution characteristics.The probability distribution of the relative content of the sample narrows with the increase of the number of single-dose pills,and the accumulated probability of RSD<6.8% increased.Conclusion:The model established in this study can predict the characteristics of the content distribution probability of dividing into single doses after mixing of compound pills,and the minimum failure probability of the content uniformity.
作者 霍志鹏 寿纪纲 王玉 何毅 Huo Zhipeng;Shou Jigang;Wang Yu;He Yi(Research Institute of Tasly Pharmaceutical Group Co Ltd,State Key Laboratory of Critical Technology of Innovative Chinese Medicine,Tianjin 300410;School of Chinese Materia Medica,Tianjin University of Traditional Chinese Medicine,Tianjin 301617;School of Traditional Chinese Pharmacy,China Pharmaceutical University,Jiangsu 210009;School of Pharmaceutical Science and Technology,Tianjin University,Tianjin 300072)
出处 《天津药学》 2022年第1期14-19,共6页 Tianjin Pharmacy
基金 国家“重大新药创制”科技重大专项(No.2017ZX09301005)。
关键词 丸剂 混合 均匀度 随机模拟 蒙特卡罗 pills mixing uniformity stochastic simulation Monte Carlo
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