Abstract
The insect cell-baculovirus vector system has become one of the favorite platforms for the expression of viral vectors for vaccination and gene therapy purposes. As it is a lytic system, it is essential to balance maximum recombinant product expression with harvest time, minimizing product exposure to detrimental proteases. With this purpose, new bioprocess monitoring solutions are needed to accurately estimate culture progression. Herein, we used online digital holographic microscopy (DHM) to monitor bioreactor cultures of Sf9 insect cells. Batches of baculovirus-infected Sf9 cells producing recombinant adeno-associated virus (AAV) and non-infected cells were used to evaluate DHM prediction capabilities for viable cell concentration, culture viability and AAV titer. Over 30 cell-related optical attributes were quantified using DHM, followed by a forward stepwise regression to select the most significant (p < 0.05) parameters for each variable. We then applied multiple linear regression to obtain models which were able to predict culture variables with root mean squared errors (RMSE) of 7 × 105 cells/mL, 3% for cell viability and 2 × 103 AAV/cell for 3-fold cross-validation. Overall, this work shows that DHM can be implemented for online monitoring of Sf9 concentration and viability, also permitting to monitor product titer, namely AAV, or culture progression in lytic systems, making it a valuable tool to support the time of harvest decision and for the establishment of controlled feeding strategies.
Highlights
Most cell culture monitoring methods employing label-free methodologies are based on spectroscopic techniques, which have been widely used for cell culture process monitoring
Examples include the use of dielectric spectroscopy and turbidimetry/light scattering probes for the determination of cell concentration [4,5], as well as the use of Raman [6,7], infrared [8] and fluorescence [9] spectroscopy, which allow the quantification of metabolites based on direct spectra quantification, and the indirect determination of cell concentration and product formation based on chemometric analysis
Since DDHM can be used to detect infected cells, we further explored this capability for monitoring the associated virus (AAV) titer in our cultures along with the development of predictive models for viable cell concentration and viability
Summary
After the FDA launched the Process Analytical Technology (PAT) initiative in 2004 [1], an increased effort was put in place by the manufacturers of biological products to comply with PAT requirements.The PAT initiative is a guidance for the pharmaceutical industry for the development of new products and production processes, with the main focuses on: (i) increasing product and process knowledge through the identification of the product critical quality attributes and the process parameters affecting it; and (ii) monitoring in real-time the identified critical process parameters and the product quality characteristics, ensuring manufacturing robustness and an increased quality assurance to achieve the required levels of compliance [1,2,3].Processes 2020, 8, 487; doi:10.3390/pr8040487 www.mdpi.com/journal/processesLabel-free methodologies are preferred, especially in biopharmaceutical processes, since they allow the monitoring of cell culture without adding any compounds which would influence cellular behavior.Most cell culture monitoring methods employing label-free methodologies are based on spectroscopic techniques, which have been widely used for cell culture process monitoring. A label-free alternative to spectroscopic techniques is imaging-based cell culture monitoring. DHM provides quantitative phase imaging (QPI), quantifying the phase shift of the light after it has passed through the object of focus, such as cells. This light phase difference is encoded in a hologram which is used to construct high-resolution intensity and quantitative-phase images of the cell while providing quantitative parameters related with light phase and intensity [11,13]. As demonstrated by Ugele and colleagues, DHM-based detection of the intracellular composition of infected erythrocytes even allowed to distinguish between different infection phases in the malaria P. falciparum life cycle [17]. The ability to detect infected cells as well as cell concentration and viability makes DHM inherently attractive to monitor the progress of infection-based biopharmaceutical production systems, such as the insect cell-baculovirus system [19]
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