Integrated Sample and Data Management for Micro-bioreactor Experiments

August 12, 2019

Presented at the Bioprocessing Summit, Boston, MA, USA

Cell line and process development groups increasingly apply miniaturized and automated cell culturing and analytical test methods. These include micro-bioreactor systems, Process Analytical Techniques (PATs) and several methods for early assessment of product quality. Therefore, the amount of data which needs to be analyzed and reviewed, has grown significantly. Thus, data structuring and curation represents a serious bottleneck to proper data analysis and valid decision making. We have designed a highly integrated data management system called Genedata Bioprocess®, which supports automated workflows and provides the foundation for an increase in throughput in cell line and process development. For scale-down bioreactor-like experiments performed either in microtiter plates, shake flasks or micro-bioreactors, such as in the ambr® systems, we implemented barcoded sampling and automated data capture workflows. All online, at-line and offline data are automatically processed, aggregated, and visualized, enabling multi-parametric assessment of any type of bioreactor data in the context of experimental settings. In addition, the applied raw materials and their quality attributes are associated to the experiments. We present concrete use cases demonstrating how the platform supports screening in scale-down models. The integration with product quality and molecule data enables a comprehensive assessment of best-producer cell lines and processes. In addition, we demonstrate how the system’s tracking capabilities for raw material lineages (e.g., media and media components) enable the monitoring of raw material batch-to-batch variation and correlation of raw material lots with process performance. We show how the platform enables the correlation of process parameters with key performance indicators of the processes (e.g., Titer, Qp) and product quality attributes (e.g., aggregation, glycosylation profiles).

 

 



Back to list