Mathematical modeling as a tool to improve influenza vaccine production processes. Issue 19 (2018)

• Record Type: Journal Article
• Authors: Duvigneau, Stefanie; Dürr, Robert; Laske, Tanja; Bachmann, Mandy; Dostert, Melanie; Reichl, Udo; Kienle, Achim
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 19(2018)
• Page Start: 1
• Page End: 4
• Publication Date: 2018
• Subjects: population balance modeling -- influenza vaccine production -- multiscale modeling -- cell-to-cell variability -- genetically engineered cell lines; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.09.004
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Cell culture-based production of influenza vaccines is emerging as a promising alternative to conventional production in embryonated chicken eggs. Development and establishment of high-yield producer cell lines represents a major challenge to manufacture sufficient amounts of low-cost vaccines. One possible option to optimize vaccine production is to manipulate the expression of host cell factors relevant for virus replication. Lentiviral transduction is a gene editing method that allows to modify the expression of single or multiple host cell genes. However, due to different copy numbers and integration sites of the gene constructs the expression level shows a large cell-to-cell variability within the cell population. In this study, we will investigate the impact of genetic modifications on virus yield with the help of a structured population balance model. Therein, cell-to-cell variability is represented in terms of distributed kinetic parameter sets obtained after bootstrapping for five cell lines overexpressing a single gene. Moreover, we evaluate four different strategies to predict distributed parameter sets for cell lines overexpressing multiple genes based on the parameter distributions of the underlying single gene modifications. Furthermore, we will apply the most suitable prediction strategy to find a combination of gene modifications that leads to the highest virus productivity.