Multi-factorial experimentation is essential in understanding the link between cell culture conditions and the glycoprotein product of any biomanufacturing process. This knowledge is increasingly demanded as bioprocess development and clone screening will soon be driven by the quality by design (QbD) initiatives. While maximizing product titer remains important in clone selection and process optimization, there is a significant effort in identifying cell lines and processes that result in better product quality. Therefore, the development of scale-down models that accurately predict large scale product quality profiles is the only way to investigate more of the design space surrounding optimal manufacturing processes.
We have developed an automated high-throughput micro-bioreactor scale-down platform with a working volume of less than 1mL. These disposable micro-bioreactors are capable of performing complex fed-batch processes with pH, DO and glucose feed-back control and can accurately predict the performance of culture conditions at scales more than 1000x larger than the model. Simple models, such as shake flasks and well plates, do not provide the measurements and controls required to accurately simulate the full-scale manufacturing process.  As a result, they offer limited applicability for exploring the knowledge space and identifying critical process parameters.  Two case studies will be presented demonstrating an improved workflow combining clone screening with process optimization generating extensive kinetic data in CHO cell culture cultivations. The first study details “dynamic” ranking of clones and provides valuable insight into process robustness and clone-specific interactions.  The second presents process optimization with scale-up comparisons to bench-top bioreactors.