Abstract：
In the digital transformation of Chinese pharmaceutical industry, how to efficiently govern and analyze industrial data and excavate the valuable information contained therein to guide the production of drug products has always been a research hotspot and application difficulty. Generally, the Chinese pharmaceutical technique is relatively extensive, and the consistency of drug quality needs to be improved. To address this problem, we proposed an optimization method combining advanced calculation tools(e.g., Bayesian network, convolutional neural network, and Pareto multi-objective optimization algorithm) with lean six sigma tools(e.g., Shewhart control chart and process performance index) to dig deeply into historical industrial data and guide the continuous improvement of pharmaceutical processes. Further, we employed this strategy to optimize the manufacturing process of sporoderm-removal Ganoderma lucidum spore powder. After optimization, we preliminarily obtained the possible interval combination of critical parameters to ensure the P_(pk) values of the critical quality properties including moisture, fineness, crude polysaccharide, and total triterpenes of the sporoderm-removal G. lucidum spore powder to be no less than 1.33. The results indicate that the proposed strategy has an industrial application value.
摘要：
在中国医药行业数字化转型中,如何高效地对工业数据进行治理和分析,挖掘其中蕴含的有价值的信息,指导药品生产一直是研究热点和应用难点。一般来说,中国制药技术相对广泛，药品质量的一致性有待提高。为了解决这个问题,我们提出了一种结合高级计算工具的优化方法(例如，贝叶斯网络，卷积神经网络，和Pareto多目标优化算法)与精益六西格玛工具(例如，休哈特控制图和过程性能指标），深入挖掘历史工业数据，指导制药工艺持续改进。Further,我们采用该策略优化了除孢子粒灵芝孢子粉的生产工艺。优化后,我们初步获得了关键参数的可能区间组合，以确保包括水分在内的关键质量属性的P_(pk)值，细度,粗多糖，除孢子粒灵芝孢子粉的总三萜不少于1.33。结果表明,该策略具有一定的工业应用价值。
