A calibration-free method for biosensing in cell manufacturing

TL;DR

This paper introduces a novel calibration-free statistical framework for biosensing in cell manufacturing, effectively addressing patient variability to improve critical quality attribute recovery and process insights.

Contribution

It presents a new calibration-free method that models patient variability and constructs a patient-invariance statistic using multiple biosensors, enhancing biosensing accuracy.

Findings

Improves recovery of critical quality attributes in simulations.

Effectively recovers viable cell concentration in case study.

Provides insights into cell manufacturing process.

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 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.