Abstract
Chimeric antigen receptor (CAR) T cell therapy has tremendous potential for the treatment of cancer and other diseases. To manufacture cells of the desired quantity and quality, it is important to expand the CAR T cells ex vivo for an optimal duration. However, identifying the optimal harvest time requires knowledge of the cell concentration during the expansion period. To address this challenge, we have developed a digital shadow of CAR T cell expansion that provides a soft sensor of cell concentration in real-time. Specifically, a novel mechanistic mathematical model of cell growth within a proportional-integral-derivative (PID) controlled perfusion bioreactor has been developed using nonlinear ordinary differential equations. The model is fitted to data generated via bioreactor runs of the Aglaris FACER, in which both donor and patient cells have been expanded in two different media. Off-line data includes the initial and final cell concentrations, and online data includes the glucose and lactate concentrations as well as the perfusion rate. Training the digital shadow utilizes all the off-line and online data for each run. In contrast, real-time testing utilizes only the initial cell concentration and the available online data at the time of model fitting. Real-time testing shows that with at least 2.5 days of online data, the final cell concentration up to 2.5 days later is predicted with a mean relative error of 13% (standard deviation ≈ 6%). Informative real-time predictions of cell concentration via the digital shadow can guide decisions regarding the optimal harvest time of CAR T cells.