使用拉曼光谱和机器学习进行细胞培养特征的全面建模
Hiroki Tanemura1, Ryunosuke Kitamura2, Yasuko Yamada3
1Biologics Technology Research Laboratories I, Biologics Division, Daiichi Sankyo Co., Ltd., 2716-1, Aza Kurakake, Oaza Akaiwa, Chiyoda-Machi, Oura-Gun, Gunma, 370-0503, Japan. tanemura.hiroki.ij@daiichisankyo.co.jp.
Scientific reports
|December 9, 2023
概括
这项研究引入了一种自动机器学习方法,用于构建用于中国仓鼠卵巢 (CHO) 细胞培养监测的拉曼光谱模型. 这加快了许多代谢物的分析,改善了抗体药物生产.
科学领域:
- 生物技术是生物技术.
- 过程分析技术 (PAT) 是一种分析技术.
- 机器学习在生物处理中的应用
背景情况:
- 中国仓鼠卵巢 (CHO) 细胞对于生产基于抗体的治疗药物至关重要.
- 全面的代谢物监测对于优化CHO细胞培养和确保产品质量至关重要.
- 目前用于CHO细胞培养的拉曼光谱方法需要耗时的模型构建,阻碍了快速过程的发展.
研究的目的:
- 开发一种简单的,自动化的方法,用于构建用于CHO细胞培养的精确拉曼光谱模型.
- 通过机器学习和Python加速对众多代谢物的分析.
- 为了实现有效的过程优化和监控在制药制造业.
主要方法:
- 开发了一个使用Python和机器学习用于拉曼模型构建的自动化工作流.
- 采用贝叶斯优化来自动化和加速数据预处理和光谱范围选择.
- 利用各种机器学习算法 (线性回归,回归,XGBoost,神经网络) 来构建模型,将它们与部分最小平方 (PLS) 回归进行比较.
主要成果:
- 与传统的PLS回归相比,实现了更好的模型准确性.
- 展示了一种自动化方法,用于为100多个组件构建拉曼模型.
- 成功地促进了CHO细胞培养中的各种参数的持续监测.
结论:
- 自动机器学习方法显著提高了拉曼光谱模型开发的效率和准确性.
- 这种方法可以全面实时监测CHO细胞培养,支持更快的过程优化和强大的制造.
- 该研究为生物制药行业提供了一种有价值的工具,以改善抗体生产过程.
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