Scaling Up and Process Characterization
Once optimal media, pH, and temperature conditions were identified, the next step was to run the new process in 3 L bioreactors to check scalability and to characterize the improved process. Two bioreactor process runs were performed: the first used the optimal media blend and pH, while the second was identical to the first but included the temperature shift.
Growth curves and IFNg yield (Figure 4) from these two runs confirm improved performance predicted by the high-throughput MicroBioreactor experiments. Optimal media blend and pH increased maximum cell density up to 1 million cells/mL (a greater than 36% increase), while adding a temperature shift further extended culture viability up to two days and increased IVCC by 27%.
More dramatic results are seen with IFNg production as optimal blend and pH increased yield 4.8-fold, while the temperature shift further doubled yield to over 18 mg/L. Overall the yield increased by over ninefold, and specific productivity increased nearly sevenfold based on experiments conducted in less than four weeks.
Bench-scale bioreactors are a convenient tool to allow for in-depth analysis and to investigate effects of optimal conditions determined with scale-down models as there are fewer samples to analyze and the amount of data is easily manageable.
In addition to characterizing growth and protein production, metabolites and specific production rates were examined in detail on the bioreactor scale-up runs. Glucose was not fully consumed for baseline, with about 2 g/L remaining, yet under optimal conditions, glucose was not only fully consumed but the amount of lactate produced per glucose consumed decreased from about 1 to less than 0.8 mole/mole. Further, the ratio of IFNg produced per glutamine consumed increased significantly by 10-fold, from 2.8 to 28 mg/g.
Understanding dependence, sensitivity, and robustness of a cell culture bioprocess is critically important for technology transfer, production scale-up, and regulatory certification.
To be valuable, a scale-down model must be able to rapidly interrogate a wide process operating space. To improve predictability of the model, it must be able to control process variables such as temperature, pH, dissolved gases, and media composition.
With advances in miniaturization and automation in cell culture industry, PD scientists and engineers now have tools to rapidly and thoroughly optimize cell culture process space.
With statistically designed cell culture studies, extensive characterization of each design point is not necessary as process sensitivity to factors, interactions between factors, and optimal values can be determined by examining a few key metrics, thus avoiding analyzing large amounts of samples and data.
A new strategy can now be taken; one that uses high-throughput tools to rapidly screen process variables in conjunction with traditional, low-throughput tools for in-depth analysis and characterization.