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.
Highlights
The past few years has seen significant growth in the cell and gene therapy field with an increasing number of products receiving market authorization and approximately 900 active clinical trials [1]
To demonstrate the variability commonly encountered with primary cell material, T-cell counts were performed on leukapheresis material from four independent donors (Figure 1B) and counts of the CD4 and CD8 population made before and after T-cell isolation (Figure 1C)
During the first process run, the samples from all four donors initially underwent a lag phase in growth lasting approximately 5 days as the cell recovered and adapted to the bioreactor environment. This was followed by a growth phase which occurred in a donor-dependant manner, with difference in the time at which the cells enter proliferation, their proliferation rates and their overall proliferative capacity
Summary
The past few years has seen significant growth in the cell and gene therapy field with an increasing number of products receiving market authorization and approximately 900 active clinical trials [1]. Despite this growth, cell therapy developers still face many challenges. Cell therapy developers still face many challenges Among these is the need to develop robust manufacturing processes that can accommodate the complexity associated with live cell therapies in order to make products to a consistent quality. The intent of QbD is to establish acceptable operating envelopes for a manufacturing process within which a product will be made to a consistently high quality. These operating envelopes are established by understanding and measuring the link between the critical process parameters and critical quality attributes of the therapy
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