The biopharmaceutical industry continually seeks innovative approaches to optimize monoclonal antibody (mAb) production, aligning with patient demands, cost-effectiveness, and revenue growth. Chinese hamster ovary (CHO) cells play a pivotal role in mAb production. However, undesired massive CHO cell death can adversely impact productivity, necessitating preventive control operations. To ensure effective control and mitigate the negative impact of cell death, real-time or near real-time monitoring of cell health status is crucial. Bioprocess operators can promptly identify deviations from the desired cell status and take appropriate actions to maintain optimal conditions for mAb production. Additionally, in-process cell status information enhances process understanding, providing valuable insights into the underlying mechanisms governing cell behavior and maximizing productivity. In this study, we investigated an innovative approach, Canty TM dynamic imaging analysis (DIA), as a promising solution for real-time monitoring of cellular apoptosis, viability, and cell density. The conventional trypan blue exclusion method (Vi-CELL TM) and To-Pro-3 fluorescent dye staining (flow cytometry) were used as standard reference methods. Our findings revealed that the Canty TM DIA method combined with appropriate mathematical modeling yielded results comparable to those obtained from Vi-CELL TM and flow cytometry methods. Canty TM DIA successfully traced the trajectory of cell death progress, detecting the onset of apoptosis earlier than the following cell death event. This early detection capability would allow timely intervention to prevent further cell death and maintain optimal productivity. Furthermore, Canty TM DIA demonstrated superior performance in distinguishing images of aggregated cells, providing a more accurate measure of total cell density. This improved accuracy is crucial for calculating viable cell density, which reflects the overall health status of the cells. The results highlight the potential of Canty TM DIA as a powerful tool for real-time monitoring of cell status throughout mAb production processes.

Previous work developed a quantitative model using capacitance spectroscopy in an at-line setup to predict the dying cell percentage measured from a flow cytometer. This work aimed to transfer the at-line model to monitor lab-scale bioreactors in real-time, waiving the need for frequent sampling and enabling precise controls. Due to the difference between the at-line and in-line capacitance probes, direct application of the at-line model resulted in poor accuracy and high prediction bias. A new model with a variable range that had similar spectra shape across all probes was first constructed, which improved the prediction accuracy. Moreover, the global calibration method included the variance of different probes and scales into the model, reducing the prediction bias. External parameter orthogonalization also mitigated the interference from feeding, which further improved the model performance. The culture evolution trajectory predicted by the in-line model captured the cell death and alarmed cell death onset earlier than the trypan blue exclusion test. In addition, incorporation of at-line spectra following orthogonal design into the calibration set is more likely to generate robust calibration models than the calibration models constructed using the in-line spectra only. This is advantageous, as at-line spectra collection is easier, faster, and more material-sparing than in-line spectra collection. The root-mean-square error of prediction of the final model was 6.56% (8.42% of the prediction range) with an R2 of 92.4%.