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bioRxiv · 10.1101/2021.05.05.442854

Predicting T Cell Quality During Manufacturing Through an Artificial Intelligence-based Integrative Multi-Omics Analytical Platform

Odeh-Couvertier, V. Y.·Dwarshuis, N. J.·Colonna, M. B.·Levine, B. L.·Edison, A. S.·Kotanchek, T.·Roy, K.·Torres-Garcia, W.
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Abstract

Large-scale, reproducible manufacturing of therapeutic cells with consistently high quality is vital for translation to clinically effective and widely accessible cell therapies. However, the biological and logistical complexity of manufacturing a living product, including challenges associated with their inherent variability and uncertainties of process parameters, currently make it difficult to achieve predictable cell-product quality. Using a degradable microscaffold-based T cell process as an example, we developed an Artificial Intelligence (AI)-driven experimental-computational platform to identify a set of critical process parameters (CPP) and critical quality attributes (CQA) from heterogeneous, high dimensional, time-dependent multi-omics data, measurable during early stages of manufacturing and predictive of end-of-manufacturing product quality. Sequential, Design-of-Experiment (DOE)-based studies, coupled with an agnostic machine-learning framework, were used to extract feature combinations from media assessment that were highly predictive of total live CD4+ and CD8+ naive and central memory (CD63L+CCR7+) T cells and their ratio in the end-product. This computational workflow could be broadly applied to any cell therapy and provide a roadmap for discovering CQAs and CPPs in cell manufacturing.

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Odeh-Couvertier, V. Y., Dwarshuis, N. J., Colonna, M. B., Levine, B. L., Edison, A. S., Kotanchek, T., Roy, K., Torres-Garcia, W.. 2021-05-06. Predicting T Cell Quality During Manufacturing Through an Artificial Intelligence-based Integrative Multi-Omics Analytical Platform. https://doi.org/10.1101/2021.05.05.442854

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