Dynamic estimation of metabolic state during CAR T cell production.

We present a modeling framework that can perform real-time estimation of per-cell metabolic rates of T cells expanded ex vivo in a reactor. We validate our estimated rates using metabolic assays, show how average rates can be deconvoluted to rates of individual T cell phenotypes, and demonstrate applicability to different reactor types. Applying our tool to the expansion of both healthy and patient-derived cells in a perfusion-based microbioreactor, we offer proof-of-principle to show that correlations exist between early metabolic rates of T cells in culture and cellular attributes related to growth, differentiation, and exhaustion of the final product. Given the biological variation that exists in the growth and dynamics of patient-derived cells in culture, such modeling contributes to the overarching goal of improving the consistency of cell therapy through adaptive process control (APC).