可解释的AI用于CHO细胞培养基的优化和预测关键质量属性的预测
Neelesh Gangwar1, Keerthiveena Balraj2, Anurag S Rathore3,4
1School of Interdisciplinary Research, Indian Institute of Technology, Delhi, New Delhi, 110016, India.
Applied microbiology and biotechnology
|April 24, 2024
概括
机器学习通过预测关键质量属性 (CQA) 来优化细胞培养基. 该框架确定了影响电荷变体的铁 (Fe) 和 (Zn) 等关键成分,有助于生物制药开发和生物相似性评估.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 生物制药制造业 生物制药制造业
背景情况:
- 细胞培养基对于细胞生长和传播至关重要,影响关键质量属性 (CQA).
- 介质组件的复杂性使得了解它们对细胞生长和CQAs的特定影响具有挑战性.
- 优化介质成分对于持续的生物制药生产和生物类似药开发至关重要.
研究的目的:
- 开发一个端到端的机器学习框架来选择最佳的媒体组件.
- 预测细胞培养中的关键质量属性 (CQAs),特别是酸性和基本性电荷变体.
- 为创新和生物类似生物制药制造业的媒体发展提供见解.
主要方法:
- 使用的中国仓鼠卵巢-GS (CHO-GS) 细胞培养物,含有不同度的金属离子.
- 采用皮尔森的相关性和随机森林来选择影响电荷变异的介质组件的特征.
- 应用了SHapley添加式解释 (SHAP) 进行全球解释,并使用了15个回归模型与交叉验证进行预测.
主要成果:
- 确定了铁 (Fe) 和 (Zn) 作为影响细胞培养基中充电变异特征的重要因素.
- 机器学习框架成功预测了影响CQAs的媒介组合.
- SHAP分析提供了整体的解释性,突出了个体特征的贡献.
结论:
- 开发的机器学习框架使得有效的媒体组件选择和CQA预测成为可能.
- 这种方法有助于为生物制药创新者建立强大的媒体开发管道.
- 这些发现支持生物类似药制造商证明分析和功能生物相似性.
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