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Front. Med. | doi: 10.3389/fmed.2018.00047

Application of Raman spectroscopy and univariate modelling as a Process Analytical Technology for cell therapy bioprocessing

  • 1Cell Therapy Catapult, United Kingdom

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 authorisation, 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 behaviour, 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 modelling 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 behaviour 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 pro-active 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.

Keywords: Raman spectroscopy, cell therapy, bioprocessing, PAT, autologous, Immunotherapy

Received: 01 Dec 2017; Accepted: 08 Feb 2018.

Edited by:

Nicholas Timmins, BlueRock Therapeutics (Canada), Canada

Reviewed by:

Dan C. Wilkinson, BlueRock Therapeutics, United States
Spencer Hoover, Centre for Commercialization of Regenerative Medicine (CCRM), Canada  

Copyright: © 2018 Baradez, Biziato, Hassan and Marshall. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Damian Marshall, Cell Therapy Catapult, London, United Kingdom, damian.marshall@ct.catapult.org.uk