使用基于机器学习的拉曼光谱模型在蛋白质A色谱过程中同时预测16个质量属性
Jiarui Wang1, Jingyi Chen1,2, Joey Studts1
1Late Stage Downstream Process Development, Boehringer Ingelheim Pharma GmbH/Co. KG, Biberach an der Riss, Germany.
Biotechnology and bioengineering
|February 29, 2024
概括
这项研究通过使用Butterworth过器和机器学习来提高生物制药制造的拉曼光谱,以实时预测16种产品质量属性. 这一突破使得连续的在线监控成为可能,改善了流程的理解和控制.
科学领域:
- 生物制药制造业 生物制药制造业
- 过程分析技术 过程分析技术
- 频谱学是一种光谱学方法.
背景情况:
- 过程分析技术 (PAT) 对于推进生物制药制造至关重要.
- 拉曼光谱是一种有前途的PAT工具,但在预测多个质量属性方面存在局限性.
- 目前的应用是有限的,因为在光谱数据解释的挑战.
研究的目的:
- 为了解决生物制药过程监测的拉曼光谱的局限性.
- 开发一种可靠的方法来预测多个产品质量属性.
- 通过不断生成高质量的数据来增强流程理解.
主要方法:
- 应用巴特沃思过器用于新型拉曼光谱预处理.
- 集成机器学习算法用于校准模型开发.
- 利用实验室自动化进行自动化数据收集和模型培训.
主要成果:
- 通过拉曼光谱学成功预测了16种不同的产品质量属性.
- 通过先进的过来证明增强的光谱特征提取.
- 实现产品质量属性的在线,持续监控.
结论:
- 开发的基于拉曼的多属性在线技术是生物过程监测的突破.
- 实施将显著提高流程理解,并使实时控制.
- 这种方法促进了过程开发,表征,验证和控制中的新工作流.
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