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Abstract

Today, human influenza vaccines are still mainly produced in embryonated henâs eggs. However, strong efforts are put into the development of cell culture-based vaccine production systems to overcome limitations and drawbacks of this production system. In cell culture-based vaccine production cellular physiology during infection is of great importance for process characterization and subsequent optimization approaches. The use of flow cytometry offers a powerful tool to monitor and correlate several physiological parameters on a single-cell level. Here, we present flow cytometric data on the replication of human influenza A virus in adherent Madin-Darby canine kidney cells in lab-scale stirred-tank bioreactors. Physiological parameters of interest are apoptosis, status of infection and viral nucleoprotein content per cell. For the quantification of cellular infection status and content of viral nucleoprotein in the host-cells we established a sensitive immunocytometric detection method. In contrast to virus titration methods, this single cell-based immunocytometry is independent of dilution series and the number of infected cells. This is of interest especially in case of vaccine production processes, which are typically initiated at low multiplicities of infection. Apoptotic cell death occurring during virus propagation was measured via DNA strand breaks and pancaspase activity. Results obtained from monitoring vaccine production processes confirm a close correlation of apoptosis and influenza virus infection. Separate analysis of adherent and detached cell populations showed strong differences regarding status of infection and degree of apoptosis. The extend of apoptosis induction was found to be strongly dependent on culture conditions during the virus propagation phase. Based on these results, options to influence cell physiology to increase overall virus yields are being investigated. Together with mathematical models using population balances a better understanding of infection dynamics and host-cell interaction should be obtained to support a quantitative prediction of process behaviour and systematic process optimization [1]. [1] Sidorenko, Y., Schulze-Horsel, J., Voigt, A., Reichl, U., Kienle, A., 2007, Stochastic population balance modeling of influenza virus replication in vaccine production processes, submitted