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.

摘要

嵌合抗原受体 T 细胞疗法在抗癌方面表现出创新的治疗效果；然而，由于细胞制造中患者与患者之间固有的可变性，它非常昂贵。我们在这项工作中提出了一种新的无校准统计框架，以有效地推断患者与患者变异下的关键质量属性。具体来说，我们通过患者特定的校准参数对这种可变性进行建模，并使用来自多个生物传感器的读数来构建患者不变性统计数据，从而减轻校准参数的影响。提出了精心制定的优化问题和算法框架，以找到最佳的患者不变性统计量和模型参数。使用患者不变性统计量，我们可以推导出感兴趣的关键质量属性，而无需校准参数。我们在不同的模拟实验中展示了所提出的免校准方法的改进。在细胞制造案例研究中，我们的方法不仅有效地推导出用于监测的活细胞浓度，而且揭示了对细胞制造过程的见解。