整合混合建模和高吞吐量选:用于流通色谱的模块化工艺开发平台
Llian Mabardi1, Janani Ram1, Chris Gerberich2
1Department of Chemical Engineering, University of Virginia, Charlottesville, VA 22903, United States.
Journal of chromatography. A
|January 23, 2026
概括
机器学习模型使用高吞吐量选数据预测蛋白质加载和产量,简化生物过程开发. 这种方法提供了比传统方法更少的实验输入的准确预测.
科学领域:
- 生物制药制造业 生物制药制造业
- 过程开发 过程开发
- 染色体学 染色体学是什么
背景情况:
- 生物制药行业对于复杂的治疗药物要求高效的工艺.
- 流通和正面加载染色学提供了高容量和简单性.
- 目前的高吞吐量选 (HTS) 与机械建模在模型校准和捕捉复杂效应方面面临挑战.
研究的目的:
- 开发机器学习 (ML) 模型,用于预测色谱中的蛋白质负载和产量.
- 使用基于HTS板的数据作为ML模型训练的唯一输入.
- 创建一个平台,在过程开发中无集成HTS和混合建模.
主要方法:
- 开发神经网络和符号回归模型,使用一般速率模型 (GRM) 模拟数据库.
- 为理想的染色学条件创建了新的分析表达式,将传输限制视为干扰.
- 根据GRM模拟和实验数据验证了ML模型,考虑到实验不确定性.
主要成果:
- ML模型只使用可访问的HTS输入,准确地预测蛋白质负载和产量.
- 神经网络显示了最高的准确性;象征回归提供了可解释性和简单性.
- 在典型的实验不确定性下,ML模型的预测错误与机械模拟相比或小于机械模拟.
结论:
- 拟议的平台整合了HTS和混合建模,以实现高效的生物工艺开发.
- ML模型将HTS数据转化为树脂和条件选择的实际指南.
- 预测的纯度,产量和生产率与实验柱的性能保持一致.
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