A calibration-free method for biosensing 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

Jialei Chen; Zhaonan Liu; Kan Wang; Chen Jiang; Chuck Zhang; Ben Wang

2020

Scholarcy highlights

  • Cell therapy is one of the most promising new treatment approaches over the last decades, demonstrating great potential in treating cancers, including leukemia and lymphoma
  • To achieve high quality and acceptable vein-to-vein cost, we present in this work a statistical framework for online monitoring in cell manufacturing, which can alleviate the negative effect of the intrinsic patient-to-patient variability
  • The mean error of the five experiments by the proposed method is 0.282, which is almost two times smaller than that of 0.501 by SameCal. This is due to the fact that the calibration parameter, which models the patient-to-patient variability, is not a constant in cell manufacturing; the proposed calibration-free method properly addresses this variability via the construction of the patient-invariance statistic
  • We propose a new calibration-free method for monitoring viable cell concentration in cell manufacturing, which is a critical component in the promising chimeric antigen receptor T cell therapy
  • In the online monitoring stage, viable cell concentrations can be recovered via the invariance statistic, free from the patient-specific calibration parameter
  • We believe the proposed calibration-free method can play an essential role in cell manufacturing and reduce the cost of the promising CAR T cell therapy

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