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计算机辅助设计空间识别用于选蛋白质A亲和染色学树脂

Steven Sachio¹, Blaž Likozar², Cleo Kontoravdi¹

¹Sargent Centre for Process Systems Engineering, Imperial College London, SW7 2AZ, UK; Department of Chemical Engineering, Imperial College London, SW7 2AZ, UK.

Journal of chromatography. A

|April 10, 2024

概括

本研究引入了一种机器学习增强的方法,以优化单克隆抗体 (mAb) 生产过程. 它确定了蛋白质A染色体的最佳设计空间,显著提高了效率和灵活性.

科学领域:

生物制药制造业生物制药制造业
化学工程是化学工程的组成部分.
生物技术是生物技术.

背景情况:

生物制药行业正在经历单克隆抗体 (mAb) 生产的快速增长.
蛋白质A亲和色谱是mAb捕获的关键过程,但过程灵活性往往被忽视.
有效和适应性的mAb生产流程对于处理料流的变化至关重要.

研究的目的:

开发一种基于模型的方法来识别蛋白质A染色体中的设计空间.
整合机器学习以提高过程设计和选的效率.
为了评估过程灵活性与性能一起用于强大的mAb生产.

主要方法:

利用基于模型的方法与机器学习相结合,用于过程设计.
选了五种工业相关的蛋白质A树脂用于mAb捕获.
将计算结果与实验数据进行比较以进行验证.

主要成果:

机器学习辅助的方法实现了计算时间的30万倍改进.
在19小时的时间框架内确定了5120个不同的设计空间.
将计算结果与实验数据进行了有利的比较.
提供了不同蛋白质A树脂性能的比较分析.

结论:

开发的方法有效地确定了蛋白质A染色体的设计空间,提高了过程效率.
机器学习显著加快了mAb净化设计空间识别过程.
这种方法为优化生物制药制造工艺提供了灵活而强大的解决方案.

关键词:

设计空间识别设计空间识别灵活性灵活性灵活性蛋白质是一种染色体学.树脂选用树脂进行选.

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