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使用拉曼光谱和生物制药过程的深度学习模型进行持续的葡萄糖反控制.

Mohammad Rashedi1, Matthew Demers2, Hamid Khodabandehlou1

  • 1Operations Transformation and Digital Strategy, Amgen Inc., Thousand Oaks, California, USA.

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|April 2, 2025
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概括

使用深度学习和拉曼光谱学的持续血糖控制提高了生物工艺效率和产品质量. 这种先进的方法提高了葡萄糖测量精度,减少了副产品,并优化了复杂细胞培养中的产量.

关键词:
拉曼光谱法 拉曼光谱法不断监测葡萄糖的情况.反糖控制反糖控制的方法葡萄糖设定点跟踪跟踪过程分析技术 技术分析

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科学领域:

  • 生物技术是生物技术.
  • 生物工艺工程 生物工艺工程
  • 计算生物学 计算生物学

背景情况:

  • 高消耗的细胞培养过程在维持稳定的葡萄糖水平方面存在挑战,影响产品质量和产量.
  • 传统的玻尿酸养策略往往导致葡萄糖波动和低于最佳的过程性能.
  • 精确的实时监测和控制对于优化复杂的生物制造操作至关重要.

研究的目的:

  • 在高消耗细胞培养中实施和评估持续血糖控制 (CGC) 策略.
  • 利用先进的深度学习模型和拉曼光谱来精确监测和控制葡萄糖.
  • 评估CGC对关键质量属性,工艺产量和副产品形成的影响.

主要方法:

  • 利用拉曼光谱与深度学习模型 (CNN,VAE JIT学习) 结合使用,用于现场血糖监测.
  • 开发和实施连续葡萄糖计算器 (CGC) 作为拉曼光谱的可扩展替代方案.
  • 在多个细胞系的生物反应器系统中,比较了连续血糖控制策略与传统的玻尿酸养.

主要成果:

  • 深度学习衍生过程监测显著提高葡萄糖测量准确性和稳定性.
  • 持续血糖控制策略保持了设定点稳定性,降低了高曼诺 (HM) 水平,并提高了整体标位生产力.
  • 基于拉曼和CGC驱动的策略都最大限度地降低了葡萄糖波动,减少了不必要的副产品,并在不同细胞系中优化了过程产量.

结论:

  • 通过深度学习和先进的监测技术来实现持续的葡萄糖控制,为动态,高消耗的生物工艺提供了强大的解决方案.
  • 开发的CGC为制造环境提供了可扩展和有效的替代方案,提高了生物工艺效率和产品质量.
  • 这种方法可以系统地评估关键的质量属性,并解决细胞培养中的葡萄糖变异性的挑战.