通过机器学习来优化CHO细胞培养过程
Jannik Richter1, Qimin Wang2, Ferdinand Lange1
1Institute of Technical Chemistry, Faculty of Natural Sciences, Leibniz University Hannover, Hannover, Germany.
Biotechnology and bioengineering
|January 31, 2025
概括
机器学习优化了用于治疗性蛋白质生产的中国仓鼠卵巢 (CHO) 细胞培养. 这种人工智能方法在生物过程中显著增加了多达48%的单克隆抗体 (mAb) 标位.
科学领域:
- 生物技术和生物加工
- 细胞培养优化细胞培养优化
- 再组合蛋白质的生产生产.
背景情况:
- 中国仓鼠卵巢 (CHO) 细胞对于制造重组治疗性蛋白质至关重要,包括单克隆抗体 (mAbs).
- 优化CHO细胞培养是复杂的,因为有很多影响因素,影响工艺效率和蛋白质产量.
- 已建立的工业CHO细胞培养需要复杂的方法来提高生产率.
研究的目的:
- 研究机器学习 (ML) 算法的应用,以优化工业CHO细胞培养过程.
- 利用人工智能 (AI) 识别改善的培养条件,以增强细胞生长和mAb生产.
- 验证ML在显著提高生物工艺生产率方面的有效性.
主要方法:
- 利用人工神经网络 (ANN),一种ML算法,在历史和新的CHO细胞培养数据上进行训练.
- 雇佣受过训练的ANN来预测和建议优化种植环境和新条件组合.
- 进行了验证实验,以确认预测的细胞生长和mAb标位的改善.
主要成果:
- 机器学习算法成功识别了优化的培养参数,从而改善了细胞生长.
- 验证实验证实了单克隆抗体 (mAb) 生产的显著增加.
- 最好的实验结果显示,最终的mAb标位增加了48%.
结论:
- 机器学习算法是优化复杂生物过程的强大而有前途的工具,如CHO细胞培养.
- 由人工智能驱动的优化可以导致重组治疗性蛋白质产量的大幅改善.
- 这种方法为提高生物制药制造业的效率和经济可行性提供了明确的途径.
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