On-line monitoring of physiological parameters of insect cell cultures during the growth and infection process.

On-line monitoring of insect cell cultures used for the production of recombinant proteins with the baculovirus expression vector system (BEVS) provides valuable tools for the optimization, operation, and control of the production process. The relative permittivity (epsilon') and CO(2) evolution rates (CER) were measured on-line using the biomass monitor and the infrared CO(2) analyzer, respectively. The growth and infection phases of two different cell lines, Spodoptera frugiperda (Sf-9) and Trichoplusia ni(High-5), were monitored using the above measurements. These in turn were correlated to the progress of the culture by using the off-line measurements of protein produced, virus titer, and biovolume, which is the product of viable cell density and mean cell volume. The epsilon', CER, and the biovolume profiles were closely matched during the growth phase of cells when grown in a batch or fed batch culture. The relationship became more complex when the cultures were either in stationary phase or in the postinfection phase. The epsilon' profile was found to be a good indicator of the process of synchronous baculoviral infection, showing a plateau between 18 and 24 h postinfection (hpi), the period during which budded virus is produced, and a peak at approximately 48 hpi correlated to the onset of accelerated cell lysis. The CER profile continues to increase after the growth period with a peak around the 24 hpi period, after which there is a decline in the profile corresponding to release of virus as seen from virus titer determinations. This was examined for Sf-9 cultures under conditions of cell densities from 3 to 50 x 10(6) cells/mL and MOI values ranging from 0.001 to 1000. The profiles were found to be similar also in the case of the High-5 cells. Thus both measurements give reliable information regarding the physiological status of the cells as seen from their correlation to virus and protein production. A further combination of these with the off-line measured parameters such as the biovolume and metabolite concentrations can give a more detailed understanding of the process and help in the better design and automation of these processes.