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

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.