数据知识驱动的建模和操作调整,用于通过湿颗粒制造的药物片制造工艺
Zhengsong Wang1,2, Shengnan Tang1,2, Yanqiu Yang3
1School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.
ACS omega
|July 17, 2023
概括
这项研究在Pharma 4.0.0中引入了制药质量控制 (PQC) 的新框架. 它整合了贝叶斯网络和基于案例的推理,以提高药物片制造工艺 (PTMP) 并确保关键质量属性 (CQA).
科学领域:
- 制药制造业 制药制造业 制药制造业
- 质量控制 质量控制 质量控制
- 工艺工程的过程工程.
背景情况:
- 药品质量控制 (PQC) 在Pharma 4.0中面临挑战,原因是药品平板制造工艺 (PTMP) 中的材料属性变化和复杂的工艺合.
- 及时调整操作变量对于保持PTMP的质量至关重要.
研究的目的:
- 为PTMP提出一个新的数据知识驱动的建模和操作调整框架.
- 解决制药制造中的不确定性和可变合,以改善PQC.
主要方法:
- 贝叶斯网络 (BN) 和基于案例的推理 (CBR) 的整合,以实现基于数据知识的方法.
- 为每个PTMP子单位开发分布式的BN模型,并将其整合到全球BN模型中.
- 通过使用预期的关键质量属性 (CQA) 作为证据,通过全球BN推理实现的运营调整.
主要成果:
- 在以喷涂液化床颗粒化为基础的PTMP中证明了拟议框架的可行性和有效性.
- 在制造的平板电脑的终端CQAs中表现出改善.
- 对比分析强调了该方法的有效性和优点,与现有方法相比.
结论:
- 集成的BN和CBR框架为复杂的制药制造中PQC提供了强大的解决方案.
- 该方法通过及时进行运营调整,有效提高终端CQAs.
- 这种方法为制药生产中的Pharma 4.0计划提供了显著的优势.
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