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, we developed an artificial intelligence (AI)-driven experimental-computational platform to identify a set of critical process parameters and critical quality attributes from heterogeneous, high-dimensional, time-dependent multiomics data, measurable during early stages of manufacturing and predictive of end-of-manufacturing product quality. Sequential, design-of-experiment-based studies, coupled with an agnostic machine-learning framework, were used to extract feature combinations from early in-culture media assessment that were highly predictive of the end-product CD4/CD8 ratio and total live CD4+ and CD8+ naïve and central memory T cells (CD63L+CCR7+). Our results demonstrate a broadly applicable platform tool to predict end-product quality and composition from early time point in-process measurements during therapeutic cell manufacturing.
CITATION STYLE
Odeh-Couvertier, V. Y., Dwarshuis, N. J., Colonna, M. B., Levine, B. L., Edison, A. S., Kotanchek, T., … Torres-Garcia, W. (2022). Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform. Bioengineering and Translational Medicine, 7(2). https://doi.org/10.1002/btm2.10282
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