In the era of Biopharma 4.0, process digitalization fundamentally requires accurate and timely monitoring of critical process parameters (CPPs) and quality attributes. Bioreactor systems are equipped with a variety of sensors to ensure process robustness and product quality. However, during the biphasic production of viral vectors or replication‐competent viruses for gene and cell therapies and vaccination, current monitoring techniques relying on a single working sensor can be affected by the physiological state change of the cells due to infection/transduction/transfection step required to initiate production. To address this limitation, a multisensor (MS) monitoring system, which includes dual‐wavelength fluorescence spectroscopy, dielectric signals, and a set of CPPs, such as oxygen uptake rate and pH control outputs, was employed to monitor the upstream process of adenovirus production in HEK293 cells in bioreactor. This system successfully identified characteristic responses to infection by comparing variations in these signals, and the correlation between signals and target critical variables was analyzed mechanistically and statistically. The predictive performance of several target CPPs using different multivariate data analysis (MVDA) methods on data from a single sensor/source or fused from multiple sensors were compared. An MS regression model can accurately predict viable cell density with a relative root mean squared error (rRMSE) as low as 8.3% regardless of the changes occurring over the infection phase. This is a significant improvement over the 12% rRMSE achieved with models based on a single source. The MS models also provide the best predictions for glucose, glutamine, lactate, and ammonium. These results demonstrate the potential of using MVDA on MS systems as a real‐time monitoring approach for biphasic bioproduction processes. Yet, models based solely on the multiplicity and timing of infection outperformed both single‐sensor and MS models, emphasizing the need for a deeper mechanistic understanding in virus production prediction.

中文翻译：

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HEK293 细胞中腺病毒生产在线过程监测的多传感器测量的多变量数据分析

在生物制药4.0时代，工艺数字化从根本上需要准确、及时地监控关键工艺参数（CPP）和质量属性。生物反应器系统配备了各种传感器，以确保过程的稳健性和产品质量。然而，在用于基因和细胞治疗和疫苗接种的病毒载体或具有复制能力的病毒的双相生产过程中，目前依赖于单个工作传感器的监测技术可能会受到感染/转导/转染步骤导致的细胞生理状态变化的影响需要开始生产。为了解决这一限制，采用多传感器（MS）监测系统来监测腺病毒生产的上游过程，该系统包括双波长荧光光谱、介电信号和一组CPP（例如摄氧率和pH控制输出）在生物反应器中的 HEK293 细胞中。该系统通过比较这些信号的变化，成功地识别了对感染的特征反应，并对信号与目标关键变量之间的相关性进行了机械和统计分析。对来自单个传感器/源或多个传感器融合的数据使用不同的多变量数据分析 (MVDA) 方法的几个目标 CPP 的预测性能进行了比较。无论感染阶段发生什么变化，MS 回归模型都可以准确预测活细胞密度，相对均方根误差 (rRMSE) 低至 8.3%。与基于单一来源的模型实现的 12% rRMSE 相比，这是一个显着的改进。 MS 模型还提供了对葡萄糖、谷氨酰胺、乳酸和铵的最佳预测。这些结果证明了在 MS 系统上使用 MVDA 作为双相生物生产过程的实时监测方法的潜力。然而，仅基于感染的多样性和时间的模型优于单传感器和 MS 模型，这强调了在病毒产生预测中需要更深入的机制理解。