A predictive high‐throughput scale‐down model of monoclonal antibody production in CHO cells

Multi-factorial experimentation is essential in understanding the link between mammalian cell culture conditions and the glycoprotein product of any biomanu- facturing process. This understanding is increasingly demanded as bioprocess development is influenced by the Quality by Design paradigm. We have developed a system that allows hundreds of micro-bioreactors to be run in parallel under controlled conditions, enabling factorial experiments of much larger scope than is possible with traditional systems. A high-throughput analytics workflow was also developed using commercially available instru- ments to obtain product quality information for each cell culture condition. The micro-bioreactor system was tested by executing a factorial experiment varying four process parameters: pH, dissolved oxygen, feed supplement rate, and reduced glutathione level. A total of 180 micro-bioreactors were run for 2 weeks during this DOE experiment to assess this scaled down micro-bioreactor system as a high- throughput tool for process development. Online measure- ments of pH, dissolved oxygen, and optical density were complemented by offline measurements of glucose, viability, titer, and product quality. Model accuracy was assessed by regressing the micro-bioreactor results with those obtained in conventional 3 L bioreactors. Excellent agreement was observed between the micro-bioreactor and the bench-top bioreactor. The micro-bioreactor results were further ana- lyzed to link parameter manipulations to process outcomes via leverage plots, and to examine the interactions between process parameters. The results show that feed supplement rate has a significant effect (P < 0.05) on all performance metrics with higher feed rates resulting in greater cell mass and product titer. Culture pH impacted terminal integrated viable cell concentration, titer and intact immunoglobulin G titer, with better results obtained at the lower pH set point. The results demonstrate that a micro-scale system can be an excellent model of larger scale systems, while providing data sets broader and deeper than are available by traditional methods. Biotechnol. Bioeng. 2009;104: 1107-1120.