在线拉曼光谱作为SARS-CoV-2 VLP生产过程分析技术
Felipe Moura Dias1,2, Milena Miyu Teruya1, Samanta Omae Camalhonte2
1Laboratório de Engenharia de Bioprocessos. Escola de Artes, Ciências E Humanidades (EACH), Universidade de São Paulo, Rua Arlindo Béttio, 1000, São Paulo, SP, CEP 03828-000, Brazil.
Bioprocess and biosystems engineering
|October 9, 2024
概括
在线拉曼光谱有效监测SARS-CoV-2病毒样粒子 (VLP) 生产. 化学测量模型,特别是人工神经网络 (ANN) 和部分最小方形 (PLS),可以准确预测生物反应器中的关键生化参数.
科学领域:
- 生物技术是生物技术.
- 过程分析技术 (PAT) 是一种分析技术.
- 频谱学是一种光谱学.
背景情况:
- 监测像SARS-CoV-2 VLP生产这样的生物过程对于优化产量和质量至关重要.
- 监测生化参数的传统离线方法耗时且劳动密集.
- 在线过程分析技术提供了对生物过程动态的实时洞察.
研究的目的:
- 开发和验证用于实时监测SARS-CoV-2 VLP生产的线内拉曼光谱法.
- 为了比较化学模型的性能,包括部分最小方形 (PLS) 和人工神经网络 (ANN),用于预测生化参数.
- 评估这些模型在不同文化媒介中的适用性.
主要方法:
- 在SARS-CoV-2 VLP生产过程中,用于实时数据采集的线性拉曼光谱法.
- 化学测量模型,包括线性,PLS和ANN,被开发用于将光谱数据与生物化学参数相关联.
- 监测的关键参数包括可活细胞密度,细胞活力,葡萄糖,乳酸,谷氨酸,谷氨酸,氨和病毒标位.
主要成果:
- 对于大多数生物化学参数,ANN模型通常更适合,而PLS模型更适合可活细胞密度和葡萄糖.
- 开发的模型在量化范围内的参数预测方面表现出很好的准确性,平均绝对误差很低.
- 这些模型在两个不同的培养媒介中显示出强大的性能,表明其广泛适用性.
结论:
- 在线拉曼光谱,加上化学建模,是实时监测SARS-CoV-2 VLP生产的强大工具.
- ANN和PLS模型提供了有效的策略来预测关键的生物化学参数,从而实现更好的过程控制和优化.
- 这种方法促进了高效的生物过程监测,并且可以适应各种细胞宿主系统和培养条件.
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