The production of viruses or virus-like particles on an industrial scale using cell culture is a necessary prerequisite for gene therapy and the treatment of tumors with oncolytic viruses. Both scenarios require complex, multi-step processes; however, low viral titers in batch cultures and the temperature sensitivity of viral particles limit the production scale. To meet commercial and regulatory requirements, every process must be scalable and reproducible, and high viral titers must be achieved. These requirements can be met by establishing cell culture processes that match the characteristics of the virus/host cell system and by using serum-free cell culture media.
Initial viral production technologies were developed for vaccine manufacturing but are now becoming increasingly important in other areas of the biopharmaceutical industry. The scope of therapeutic strategies has expanded significantly to include vectors for recombinant protein expression, gene therapy, and cancer treatment. Viruses used for in vivoapplications have limited affinity for their target cells; they are often unstable and require large doses of infectious viral particles to achieve therapeutic effects. Therefore, efficient upstream production must be combined with optimized downstream processing. The production of viruses for clinical application also raises significant product safety concerns. Consequently, the design of bioprocesses for the production of viruses or viral vectors depends largely on virus/host cell interactions and the kinetics of viral particle synthesis and release. There is no universally optimal “off-the-shelf” process for viral production; each system must be optimized on a case-by-case basis.
Selection of Process Mode
Selecting the process mode is one of the most challenging aspects of process design, as all advantages and disadvantages must be properly evaluated. Upstream viral production typically consists of a two-step process: host cell expansion followed by the production phase. An in-depth understanding of the interactions between the host cell and the virus is crucial. Studies have identified several key parameters, including the Cell Concentration at Infection (CCI) and the Multiplicity of Infection (MOI). These factors can be used to develop optimal strategies for process control.
Cell Concentration at Infection
Host cells are clearly a critical factor in any viral production process. For the vast majority of processes, the quality of viral particles correlates linearly with the number of host cells producing them. While this holds true for continuous production using packaging cell lines, in other cases, increasing cell density eventually leads to mitosis-dependent density inhibition, thereby reducing viral yield. The survival and growth of each cell line, as well as viral production, also depend on specific and individual minimum cell concentrations. This interdependence becomes more complex when considering factors such as viral infection kinetics and cytopathic effects. Typically, high viral yields require an optimal cell concentration at infection. However, studies have shown that CCI has little effect on measles virus yield because measles virus production requires a continuous supply of fresh, uninfected host cells to compensate for cell loss due to lysis. Furthermore, some viruses inhibit the cell cycle, affecting cell growth. This also impacts product yield; for example, in baculovirus-infected insect cell systems, the yield of virus-like particles decreases when cells are infected at later stages of growth.
Multiplicity of Infection (MOI)
MOI, defined as the ratio of infectious virus particles to host cells, also affects the infection process. MOI is a key parameter when optimizing the production of lytic viruses for recombinant protein synthesis. Theoretically, under ideal conditions, the termination of protein synthesis would coincide with the depletion of the medium just before cell lysis. This is also necessary during the production of lytic therapeutic viruses to recover active viral particles. Depending on the specific virus/host cell system, two infection strategies can be employed to optimize the process. If synchronous infection is the goal, a high number of infectious viral particles (MOI >> 1) should be used. However, lower titers of infectious virus (MOI < 1) lead to multi-step viral amplification due to secondary infection of cells. When deployed correctly, both strategies can increase viral yield by several hundred-fold. Studies have shown increased viral yields using lower MOIs, but the characteristics of the viral strain must always be kept in mind, as results depend heavily on the biological system. For instance, in measles virus production, higher MOIs reduce the yield of active viral particles. However, the behavior of each host cell/virus combination must always be studied within the context of the culture system. In static culture experiments conducted in T-flasks, MOI showed a significant impact on viral yield, whereas in stirred-tank bioreactors, titers might remain within the same order of magnitude even with large variations in MOI.
Process Control
Reproducible, high-titer processes require appropriate monitoring and control systems, including established parameters such as pH, temperature, and oxygen concentration. However, there is currently a lack of real-time or online/inline monitoring and control systems for virus quantification, particularly for dynamic infection processes characterized by lytic viral production. Offline measurement methods have been established and validated for decades but suffer from two major drawbacks. On one hand, intervention in the operating system carries a risk of contamination; on the other hand, there is a lack of real-time data. Therefore, online or inline measurement methods are preferable. Process Analytical Technology (PAT) is an area that industry and regulatory authorities have been striving to advance. To meet these higher requirements, studies have experimented with equipping bioreactor systems with multiple monitoring systems, including near-infrared spectroscopy and in situmicroscopy. Multivariate data analysis was then applied to establish a benchmark known as the “golden batch.” Subsequent batches are evaluated against this standard, and if signals fall outside certain tolerance ranges, the process is aborted. Such strategies often lack opportunities for process control and result in a high number of failed batches. Consequently, inline measurements of critical media components, as well as parameters for the online assessment of process reproducibility and stability, remain unsolved challenges in industrial biotechnology.
An example is the measurement of cell count in static or semi-dynamic culture systems. Offline, cell count can be determined via membrane leakage assays (e.g., Trypan Blue staining) or mitochondrial activity assays. In small-scale operations, microscope-based cell counting devices for static culture systems can also be used. Such data can be collected indirectly through inline measurements, for instance, by estimating cell count based on the oxygen demand or glucose consumption rate of adherent cells grown in semi-dynamic systems. In fixed-bed bioreactors, this is achieved by simultaneously measuring oxygen concentrations at the inlet and outlet. Since oxygen demand is closely linked to cell metabolism and strongly influenced by viral infection, estimates are often inaccurate. Dielectric spectroscopy is a promising alternative for the inline quantification of host cells because the measurement is independent of metabolism and relies on the passive dielectric properties of cells in a conductive medium. When an alternating electric field is applied to a cell suspension, the cell membrane acts as a small capacitor, leading to charge accumulation (polarization). Thus, the total capacitance depends on the frequency of the alternating electric field, as well as cell size, morphology, and concentration. For suspensions with uniform cell size, this technique is applicable for host cell quantification as a correlation exists between the recorded permittivity and the cell count. Dielectric spectroscopy can also be used for the online quantification of adherent cells, which typically require cell detachment and offline counting. However, the dielectric spectroscopy signal is disrupted almost immediately after cell infection. Therefore, dielectric spectroscopy readings are strongly influenced by the cytopathic effects of the virus on the host cell (e.g., syncytia formation) and subsequent virus release. These morphological changes also affect the dielectric signal.
By considering the requirements of the host cell and the product, as well as the advantages and limitations of each bioreactor and process control strategy, different production processes can be established for each case. It is evident, however, that there is no “perfect” process; the optimal procedure for each host cell/product combination can be determined by considering as many parameters as possible to maximize product yield. For products based on various viruses, much research is still required to meet the demands of large-scale viral production. A detailed understanding of the virus/host cell system is essential to facilitate the development of appropriate production processes that allow for the reproducible manufacture of high-quality viral particles in sufficient quantities.
