Genedata Bioprocess

Bioreactor Module

Genedata Bioprocess™ provides an integrated framework to process, analyze, visualize, and interpret bioreactor cell-culture data. The system is particularly useful for the analysis of online and offline data produced by micro-bioreactor systems, such as ambr®. The increasing use of micro-bioreactors, which emulate large-scale manufacturing bioreactors at a fraction of the cost, has resulted in a significant increase of such experiments.

Online and offline data derived from bioreactor runs (e.g., pH, oxygen, metabolic data) and automatically calculated aggregations (e.g., Integrated Viable Cell Density, specific productivity, consumption rates) can be captured and visualized. The system enables multi-parametric assessment of any type of time-series bioreactor data in the context of experimental protocol data (e.g., process parameters, feeds).

Genedata Bioprocess enables an integrated, parallel assessment of large panels of clones and upstream processes in order to identify the best producer cell lines and the best combination of process parameters. Cell culture growth characteristics, such as product titer, specific productivity (Qp/SPR), viable cell density (VCD), lactate and other metabolite data, are automatically calculated and analyzed, providing a comprehensive view on clone behavior under simulated manufacturing conditions.

Built-in tools enable a systematic analysis of all factors needed to guide upstream process development, including process optimization, characterization, and validation. Typical applications include the optimization of media and feeding strategies or the extrapolation to larger bioreactor scales to guide upscaling campaigns; the system helps to extrapolate from scale-down bioreactors to real-life fermentation manufacturing at full manufacturing scale. These findings can in turn be compared with data from the cone selection process and data from downstream processes. This information can then be used to make an integrated overall process development assessment.