In situ microscopy as online tool for detecting microbial contaminations in cell culture

Prediction of antibody production performance change in Chinese hamster ovary cells using morphological profiling

Monoclonal antibodies (mAbs) represent a significant segment of biopharmaceuticals, with the market for mAb therapeutics expected to reach $200 billion in 2021. Chinese Hamster Ovary (CHO) cells are the industry standard for large-scale mAb production owing to their adaptability and genetic engineering capabilities. However, maintaining consistent product quality is challenging, primarily because of the inherent genetic instability of CHO cells. In this study, we address the need for advanced technologies for quality monitoring of host cells in biopharmaceuticals. We highlight the limitations of traditional cell assessment techniques such as flow cytometry and propose a noninvasive, label-free image-based analysis method. By utilizing advanced image processing and machine learning, this technique aims to non-invasively and quantitatively evaluate subtle quality changes in suspension cells. The research aims to investigate the use of morphological analysis for identifying subtle alterations in mAb productivity of CHO cells, employing cells stimulated by compounds as a model for this study. Our results show that the mAb productivity of CHO cells (day 8) can be predicted only from their early morphological profile (day 3). Our study also discusses the importance of strategic methods for forecasting host cell mAb productivity using morphological profiles, as inferred from our machine learning models specialized in predictive score prediction and anomaly prediction.

Realization of user‐friendly bioanalytical tools to quantify and monitor critical components in bio‐industrial processes through conceptual design

This minireview suggests a conceptual and user‐oriented approach for the design of process monitoring systems in bioprocessing. Advancement of process analytical techniques for quantification of critical analytes can take advantage of basic conceptual process design to support reasoning, reconsidering and ranking solutions. Issues on analysis in complex bio‐industrial media, sensitivity and selectivity are highlighted from users’ perspectives. Meeting challenging analytical demands for understanding the critical interplay between the emerging bioprocesses, their biomolecular complexity and the needs for user‐friendly analytical tools are discussed. By that, a thorough design approach is suggested based on a holistic design thinking in the quest for better analytical opportunities to solve established and emerging analytical needs.