Ramos João Rodrigues Correia, Bissinger Thomas, Genzel Yvonne, Reichl Udo
Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany.
Institute of Process Engineering, Faculty of Process & Systems Engineering, Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany.
Metabolites. 2022 Mar 12;12(3):239. doi: 10.3390/metabo12030239.
Cell cultured-based influenza virus production is a viable option for vaccine manufacturing. In order to achieve a high concentration of viable cells, is requirement to have not only optimal process conditions, but also an active metabolism capable of intracellular synthesis of viral components. Experimental metabolic data collected in such processes are complex and difficult to interpret, for which mathematical models are an appropriate way to simulate and analyze the complex and dynamic interaction between the virus and its host cell. A dynamic model with 35 states was developed in this study to describe growth, metabolism, and influenza A virus production in shake flask cultivations of suspension Madin-Darby Canine Kidney (MDCK) cells. It considers cell growth (concentration of viable cells, mean cell diameters, volume of viable cells), concentrations of key metabolites both at the intracellular and extracellular level and virus titers. Using one set of parameters, the model accurately simulates the dynamics of mock-infected cells and correctly predicts the overall dynamics of virus-infected cells for up to 60 h post infection (hpi). The model clearly suggests that most changes observed after infection are related to cessation of cell growth and the subsequent transition to apoptosis and cell death. However, predictions do not cover late phases of infection, particularly for the extracellular concentrations of glutamate and ammonium after about 12 hpi. Results obtained from additional in silico studies performed indicated that amino acid degradation by extracellular enzymes resulting from cell lysis during late infection stages may contribute to this observed discrepancy.
基于细胞培养的流感病毒生产是疫苗制造的一种可行选择。为了获得高浓度的活细胞,不仅需要优化的工艺条件,还需要具有能够在细胞内合成病毒成分的活跃代谢。在此类过程中收集的实验代谢数据复杂且难以解释,数学模型是模拟和分析病毒与其宿主细胞之间复杂动态相互作用的合适方法。本研究开发了一个具有35个状态的动态模型,以描述悬浮型犬肾传代细胞(MDCK)在摇瓶培养中的生长、代谢和甲型流感病毒生产。它考虑了细胞生长(活细胞浓度、平均细胞直径、活细胞体积)、细胞内和细胞外关键代谢物的浓度以及病毒滴度。使用一组参数,该模型准确模拟了 mock 感染细胞的动态,并正确预测了感染后长达60小时(hpi)的病毒感染细胞的整体动态。该模型清楚地表明,感染后观察到的大多数变化与细胞生长停止以及随后向凋亡和细胞死亡的转变有关。然而,预测并不涵盖感染的后期阶段,特别是在感染后约12小时后的谷氨酸和铵的细胞外浓度。从额外的计算机模拟研究中获得的结果表明,感染后期细胞裂解产生的细胞外酶对氨基酸的降解可能导致了观察到的这种差异。