Application of Raman Spectroscopy and Univariate Modelling As a Process Analytical Technology for Cell Therapy Bioprocessing

Abstract

Cell therapies offer unquestionable promises for the treatment, and in some cases even the cure, of complex diseases. As we start to see more of these therapies gaining market authorization, attention is turning to the bioprocesses used for their manufacture, in particular the challenge of gaining higher levels of process control to help regulate cell behavior, manage process variability, and deliver product of a consistent quality. Many processes already incorporate the measurement of key markers such as nutrient consumption, metabolite production, and cell concentration, but these are often performed off-line and only at set time points in the process. Having the ability to monitor these markers in real-time using in-line sensors would offer significant advantages, allowing faster decision-making and a finer level of process control. In this study, we use Raman spectroscopy as an in-line optical sensor for bioprocess monitoring of an autologous T-cell immunotherapy model produced in a stirred tank bioreactor system. Using reference datasets generated on a standard bioanalyzer, we develop chemometric models from the Raman spectra for glucose, glutamine, lactate, and ammonia. These chemometric models can accurately monitor donor-specific increases in nutrient consumption and metabolite production as the primary T-cell transition from a recovery phase and begin proliferating. Using a univariate modeling approach, we then show how changes in peak intensity within the Raman spectra can be correlated with cell concentration and viability. These models, which act as surrogate markers, can be used to monitor cell behavior including cell proliferation rates, proliferative capacity, and transition of the cells to a quiescent phenotype. Finally, using the univariate models, we also demonstrate how Raman spectroscopy can be applied for real-time monitoring. The ability to measure these key parameters using an in-line Raman optical sensor makes it possible to have immediate feedback on process performance. This could help significantly improve cell therapy bioprocessing by allowing proactive decision-making based on real-time process data. Going forward, these types of in-line sensors also open up opportunities to improve bioprocesses further through concepts such as adaptive manufacturing.

Lymphocyte expansion in bioreactors: upgrading adoptive cell therapy

“… Raman spectroscopy Metabolite monitoring (glucose, lactate, amino acids). On-line [ 46 ] Total Cell Concentration. On-line [ 46 ] Cell identity determination (phenotype & activation).…”

“… On-line [ 46 ] Total Cell Concentration. On-line [ 46 ] Cell identity determination (phenotype & activation). At-line [ 47 , 48 ] Sequential injection capillary electrophoresis Metabolite monitoring (glucose, lactate, amino acids).…”

“…Pierson et al [ 81 ] tested on-line laser turbidity measurement that reportedly correlated well with cell counts. Recently [ 46 ], T-cells cultured in stirred vessels fitted with Raman probes were used to develop chemometric models for glucose ( R = 0.987), lactate ( R = 0.986), ammonia ( R = 0.936), glutamine ( R = 0.922), and glutamate ( R = 0.829). Univariate Raman modeling for non-targeted analysis of the culture media was found useful to track the nutrient depletion (glucose and glutamine) and metabolite production (glutamate and lactate), with similar accuracy to the chemometric models.…”

Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective

“…Spectroscopic methods (e.g., NIR, FTIR, mid-IR, Raman, Fluorescence) have attracted an increasing interest for their potential capacity of simultaneous in situ measurement of nutrients, metabolites and cell concentrations. These techniques have been successfully integrated into real-time pharmaceutical manufacturing to provide a "molecular fingerprint" of samples enabling inprocess correction needed to ensure the acquisition of a specific quality product 18,19 . However, the capacity to systematically extract accurate quantitative data is, for the moment, limited to a handful number of metabolites (typically, glucose, lactate, glutamine, glutamate, and ammonium).…”

Automated Cell Expansion: Trends & Outlook of Critical Technologies

“…In contrast to the present bioprocess context where cells release the therapeutic products into the cell culture, cells are the target product in CTP manufacturing. Thus, optical spectroscopic techniques have potential advantages over contact-based sensors as they are capable of probing a wide range of chemical and biological variables within the cells that have molecular specificities via fluorescence [40] and Raman [43,46]. The technical difficulties of implementing optical spectroscopy techniques, such as noise from the culture media and high equipment cost, may be offset by the data-driven approach in chemometrics.…”