A calibration-free method for biosensing in cell manufacturing

Abstract 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 deduce 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 deduce 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 deduces viable cell concentration for monitoring, but also reveals insights for the cell manufacturing process.

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