Raman analysis of in process bioreactors to develop enhanced bioprocess control strategies

对过程中的生物反应器进行拉曼分析，以开发增强的生物过程控制策略

• 批准号: 2407516
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2407516
• 关键词: Raman; analysis; process; bioreactors; develop

Metabolic profiling of mammalian cultures enables a greater understanding of the physiological state of cells throughout a bioreactor run in addition to monitoring the key amino acid concentrations. This information is invaluable for the development of bespoke amino acid feeds that can be tailored to individual cell lines to enhance productivity and minimise product heterogeneities. However, a major challenge of metabolic profiling is the cost and challenge of generating time-course metabolic profiles for a high number of cell cultures.This project aims to overcome this hurdle by combining the power of the next generation high-throughput (HT) Raman spectroscopy device with an advanced micro-bioreactor system. This HT Raman spectroscopy device requires only a small volume equal to 25-100 micro-L and generates a high-resolution Raman spectra. These spectra enable the prediction of amino acid concentrations in addition to all relevant bioreactor off-line measurements including glucose, lactate, cell density and product concentration which opens up significant opportunities to develop better control strategies. The work will generate a data set to train and apply advanced multivariate data analysis (MVDA) and Machine Learning (ML) models to build correlations between the Raman spectra and all available off-line measurements of interest. These measurements can be incorporated into a control strategy enabling at-line control of essential amino acid concentrations and provides the foundation for the development of customised feeds to enhance bioreactor performance and maintain desired product quality specifications. One of the primary objectives of this research is to develop advanced algorithms to autonomously generate robust mathematical models enabling near real-time predictions of key process parameters to support advanced control decisions and enhance manufacturing operations. These novel computational models will be capable of predicting in silico the performance and quality of biopharmaceutical processes regardless of scale, facility or bioreactor system.This research ultimately aims to replace traditional analysis techniques that can be laborious and costly and play a pivotal role in the modernisation and digitalisation of commercial biomanufacturing operations. This digital revolution will lead to shorter development timelines, improved process control and higher process yields, ultimately reducing the cost of life-changing therapeutic drugs.

哺乳动物培养物的代谢谱除了监测关键氨基酸浓度之外，还能够更好地了解整个生物反应器运行期间细胞的生理状态。这些信息对于开发定制的氨基酸补料是非常宝贵的，这些补料可以针对单个细胞系进行定制，以提高生产率并最大限度地减少产品异质性。然而，代谢谱的一个主要挑战是为大量细胞培养物生成时程代谢谱的成本和挑战。本项目旨在通过将下一代高通量（HT）拉曼光谱设备与先进的微生物反应器系统相结合来克服这一障碍。该HT拉曼光谱装置仅需要等于25-100微升的小体积，并产生高分辨率的拉曼光谱。除了所有相关的生物反应器离线测量（包括葡萄糖、乳酸盐、细胞密度和产物浓度）之外，这些光谱还能够预测氨基酸浓度，这为开发更好的控制策略开辟了重要的机会。这项工作将生成一个数据集来训练和应用高级多变量数据分析（MVDA）和机器学习（ML）模型，以建立拉曼光谱与所有可用的离线测量值之间的相关性。这些测量可以被纳入控制策略，使得能够在线控制必需氨基酸浓度，并为定制饲料的开发提供基础，以提高生物反应器性能并保持所需的产品质量标准。这项研究的主要目标之一是开发先进的算法，自主生成强大的数学模型，使近实时的关键过程参数的预测，以支持先进的控制决策和提高制造业务。这些新的计算模型将能够在计算机上预测生物制药过程的性能和质量，而不受规模、设施或生物反应器系统的影响。这项研究的最终目标是取代传统的分析技术，这些技术可能是费力和昂贵的，并在商业生物制造操作的现代化和数字化中发挥关键作用。这场数字革命将缩短开发时间，改善过程控制，提高工艺产量，最终降低改变生活的治疗药物的成本。