High-Throughput Raman Spectroscopy Combined with Innovate Data Analysis Workflow to Enhance Biopharmaceutical Process Development

Abstract

Raman spectroscopy has the potential to revolutionise many aspects of biopharmaceutical process development. The widespread adoption of this promising technology has been hindered by the high cost associated with individual probes and the challenge of measuring low sample volumes. To address these issues, this paper investigates the potential of an emerging new high-throughput (HT) Raman spectroscopy microscope combined with a novel data analysis workflow to replace off-line analytics for upstream and downstream operations. On the upstream front, the case study involved the at-line monitoring of an HT micro-bioreactor system cultivating two mammalian cell cultures expressing two different therapeutic proteins. The spectra generated were analysed using a partial least squares (PLS) model. This enabled the successful prediction of the glucose, lactate, antibody, and viable cell density concentrations directly from the Raman spectra without reliance on multiple off-line analytical devices and using only a single low-volume sample (50–300 μL). However, upon the subsequent investigation of these models, only the glucose and lactate models appeared to be robust based upon their model coefficients containing the expected Raman vibrational signatures. On the downstream front, the HT Raman device was incorporated into the development of a cation exchange chromatography step for an Fc-fusion protein to compare different elution conditions. PLS models were derived from the spectra and were found to predict accurately monomer purity and concentration. The low molecular weight (LMW) and high molecular weight (HMW) species concentrations were found to be too low to be predicted accurately by the Raman device. However, the method enabled the classification of samples based on protein concentration and monomer purity, allowing a prioritisation and reduction in samples analysed using A280 UV absorbance and high-performance liquid chromatography (HPLC). The flexibility and highly configurable nature of this HT Raman spectroscopy microscope makes it an ideal tool for bioprocess research and development, and is a cost-effective solution based on its ability to support a large range of unit operations in both upstream and downstream process operations.

Advanced control strategies for bioprocess chromatography: Challenges and opportunities for intensified processes and next generation products

“…Methods for constructing and optimising PCA and PLS models can be found in the comprehensive review by Rolinger at al. [21], and elsewhere in the literature [52][53][54][55].…”

The role of Raman spectroscopy in biopharmaceuticals from development to manufacturing

“…While most of upstream Raman papers discuss in-line Raman spectroscopy, collected directly in the bioreactor, there have been a few reports using Raman microscopy. These include a Raman microscope coupled to an automated plate reader to support early-stage scale-down conditions [ 70 ], an academic study that used in-line viscosity and off-line Raman measurements as input into a Monad model of CHO cell metabolism, [ 71 ] and an off-line time-gated Raman/SERS microscopy approach coupled to a custom microwell plate for E. coli fermentations [ 20 ]. Additional work in the laboratory, using shake-flask or lab-scale bioreactors, compares the functionality of Raman and NIR for measuring individual parameters such as glucose, lactate, cell viability, ammonium, and glutamine [ 46 , 72 ].…”

“…Rapid at-line measurements may be used to support scale-down experiments. In 2020, Goldrick et al [ 70 ] described the use of a standard Raman microscope for at-line measurements in a 96-well plate to support scale-down upstream and downstream studies. Raman microscopy is used to provide spatial and chemical information on a material on the microscopic level.…”

A decade in review: use of data analytics within the biopharmaceutical sector

“…The process of data analytics can be described as the analysis of raw data to make useful conclusions about the information provided. The application of these tools enables the true potential of data to be fully harnessed for improved process understanding and more informed decision-making [ 5 ] facilitating optimisation of existing biomanufacturing processes and hence increased product quantity and quality. The increased use of these advanced sensors and automated bioreactors will play a key role in the advancement of the sector towards the core principles of Industry 4.0 [ 6 ].…”