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

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.