机器学习增强的NMR用于单克隆抗体生产中的快速氨基酸量化
Yingting Shi1, Kerui Fang1, Sijun Wu1
1Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
概括
机器学习与1HNMR光谱学相结合,可以在单克隆抗体生产中快速,普遍量化12个关键氨基酸,优化生物过程和产品质量.
科学领域:
- 生物技术和生物工艺工程
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 氨基酸供应对单克隆抗体 (mAb) 生产至关重要,影响细胞生长,生产力和质量.
- 定量1HNMR光谱学提供了非破坏性氨基酸监测,但在复杂的生物样本分析方面面临挑战.
- 机器学习 (ML) 提出了一种新的方法,以克服生物过程中传统的1H NMR分析的耗时性.
研究的目的:
- 开发一种通用定量模型,用于分析12个关键氨基酸,使用1H NMR光谱在各种mAb生产过程中.
- 利用ML,特别是鱼优化算法 (WOA) 和拉索回归,提高氨基酸量化的准确性和效率.
- 建立一种快速高效的氨基酸分析方法,在生物制药过程优化中具有显著的应用价值.
主要方法:
- 在mAb生产中使用定量1HNMR光谱来监测氨基酸.
- 应用机器学习算法,包括部分最小平方回归 (PLSR),支向量回归 (SVR) 和拉索回归.
- 采用鱼优化算法 (WOA) 进行特征选择以提高模型性能.
- 评估模型使用回归系数 (R),根平均平方误差 (RMSE) 和剩余预测偏差 (RPD).
主要成果:
- 与单个方法相比,WOA-Lasso组合在建模1H NMR数据方面表现优越.
- 为混合生物制造工艺构建了一个通用的定量模型,实现R值≥0.90和RPD值≥2.3.
- 开发的模型在验证集上表现出理想的预测性能,RMSE范围从0.244到1.173.
结论:
- 创新地开发了一种高效和通用的快速定量1HNMR方法,用于使用ML的氨基酸.
- 基于ML的方法显著减少了mAb生产中的氨基酸量化分析时间.
- 在优化生物制药工艺和提高单克隆抗体产品质量方面证明了实质性的应用价值.
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