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, 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.
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