Raman spectroscopy for adaptive process control of adoptive T-cell immunotherapy manufacture

Abstract

Background & Aim The majority of adoptive T-cell immunotherapies currently in clinical trials are autologous in nature. Their manufacture is constrained by fixed processes that typically allow for little, if any, adaptive control. Manufacturing platforms also have few real-time sensing technologies (biomass, glucose, pH and DO) and provide read-outs which are weak surrogate predictors of culture behaviour and quality. In this study we demonstrate the potential of Raman spectroscopy as a Process Analytical Technology (PAT). Methods, Results & Conclusion Method: A total of 6 healthy donor leukapheresis samples were processed to isolate T-cells. These were then activated and expanded in a stirred tank bioreactor over a period of 10 days. Raman spectroscopy was performed in situ in real-time, every hour and was supplemented with off-line characterisation of cell counts, viability, immunophenotying, and metabolomics. For 2 donors, full transcriptomics analysis, mass cytometry and mitochondrial potential were also performed. Results Unlike the outputs of current inline sensors, the multidimensional nature of Raman spectra allowed the development of chemometric models for cell concentration, cell viability, and a range of metabolites which correlated to cell quality. These models could be applied hourly to get a real-time snapshot of the culture status, thus providing the ability to make rapid process decisions. Together with the multivariate characterisation of the cells and medium, this approach provided a comprehensive description of the system's behaviour, paving the way for the design of informed specifications and tolerances which could be used as part of an adaptive control strategy. Conclusion The use of Raman spectroscopy for adaptive control could compensate for inherent patient-to-patient variability, providing a robust manufacturing process that can consistently meet release acceptance criteria. It could also help reduce product cycle times through optimal bioprocessing and have significant impact on the overall cost of autologous T-cell manufacturing.