  Chimeric antigen receptor T cell therapy has demonstrated innovative
therapeutic effectiveness in fighting cancers; however, it is extremely
expensive due to the intrinsic patient-to-patient variability in cell
manufacturing. We propose in this work a novel calibration-free statistical
framework to effectively recover critical quality attributes under the
patient-to-patient variability. Specifically, we model this variability via a
patient-specific calibration parameter, and use readings from multiple
biosensors to construct a patient-invariance statistic, thereby alleviating the
effect of the calibration parameter. A carefully formulated optimization
problem and an algorithmic framework are presented to find the best
patient-invariance statistic and the model parameters. Using the
patient-invariance statistic, we can recover the critical quality attribute of
interest, free from the calibration parameter. We demonstrate improvements of
the proposed calibration-free method in different simulation experiments. In
the cell manufacturing case study, our method not only effectively recovers
viable cell concentration for monitoring, but also reveals insights for the
cell manufacturing process.
