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

除了监测关键氨基酸浓度外，哺乳动物培养物的代谢谱还能更好地了解生物反应器运行过程中细胞的生理状态。这些信息对于开发定制的氨基酸饲料是非常有价值的，这种饲料可以针对单个细胞系进行定制，以提高生产率并将产品异质性降至最低。然而，代谢图谱的一个主要挑战是为大量细胞培养生成时程代谢图谱的成本和挑战。该项目旨在通过将下一代高通量(HT)拉曼光谱设备的能力与先进的微生物反应器系统相结合来克服这一障碍。这种高温拉曼光谱装置只需要相当于25-100微米L的小体积，就能产生高分辨率的拉曼光谱。这些光谱能够预测氨基酸浓度，以及所有相关的生物反应器离线测量，包括葡萄糖、乳酸、细胞密度和产品浓度，这为开发更好的控制策略提供了重要机会。这项工作将产生一个数据集，以训练和应用先进的多变量数据分析(MVDA)和机器学习(ML)模型，以建立拉曼光谱和所有感兴趣的可用离线测量之间的关联。这些测量可纳入控制策略，实现对必需氨基酸浓度的在线控制，并为定制饲料的开发提供基础，以提高生物反应器的性能并保持所需的产品质量规格。这项研究的主要目标之一是开发先进的算法，以自主生成健壮的数学模型，从而能够近乎实时地预测关键工艺参数，以支持先进的控制决策和增强制造操作。这些新的计算模型将能够在电子计算机中预测生物制药过程的性能和质量，无论规模、设施或生物反应器系统。这项研究的最终目标是取代传统的分析技术，这些技术可能费力且成本高昂，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。这场数字革命将导致更短的开发时间、更好的过程控制和更高的过程产量，最终降低改变生活的治疗药物的成本。

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.