Five tools to optimize bioreactor production and shorten vaccine development timelines
Immunotherapy for cancer, autoimmune, or neurological diseases requires manufacturing antibodies that bind to and label diseased cells for the body to destroy. Developing the seed strains to grow these antibodies in cell cultures and optimizing the bioreactors and culture growth conditions in the lab can easily take 40+ weeks – and these projects are subject to shifts in priorities, manpower, and budget.
Once the appropriate strains are identified, the process must be scaled and validated, eventually making its way to clinical trials, all of which add more weeks (or, realistically, years) to the amount of time it takes for treatments to get from the lab to the people in need.
Here, we look at five basic (but underutilized) means of optimizing bioreactor yields, shortening the timelines for scaling processes and producing the biologic or antibody treatments.
1. Use DOE and statistical analysis
The proteins required for different treatments each have their own optimal growth conditions, to ensure correct protein folding and assembly. Growth conditions must be further optimized to maximize protein yields.
Growing antibodies for immunotherapies from Chinese hamster ovarian (CHO) cells therefore requires specific culture conditions, including: nutrient mix, temperature and pH, dissolved oxygen levels, bioreactor type, and feed control strategy. Effectively optimizing these conditions is the primary determinant of the overall health of the culture and the efficiency of its growth.
But independently optimizing each variable is unrealistic due to the interdependence of the variables (time constraints and resource limitations don’t help, either). Traditionally, optimization was done by trial and error – changing various parameters based on the researcher’s understanding and experience with the system. Design of experiments (DOE) and statistical analysis, however, are quantitative tools that can dramatically simplify the complicated task of process optimization.
A common lab setup involves using cultures as small as 10 mL to optimize basic process parameters including temperature, pH, and dissolved oxygen. Culturing about 50 samples at a time allows researchers to experiment with different growing conditions. DOE is a tool to use proactively, so researchers can design these small-scale systems to manipulate multiple variables simultaneously and efficiently determine the optimal set of critical process parameters.
DOE is also useful for troubleshooting in scaled-down systems ranging in size from 500 mL to 10 L. It allows system designers to move from problem solving through trial and error to solutions based on st1atistically-relevant data accounting for a large number of interacting process variables.
2. Implement PAT
Process analytical technology (PAT) simply refers to adding on-line sensors and other means of data collection, to gather information from pharmaceutical manufacturing processes. This leads to an increased understanding of what is happening in the system and how it responds to any changes – and means that system designers can adjust controllers based on real-time data.
While PAT is typically discussed in the context of GMP production environments, the concept of increased sensorization is equally beneficial in labs and pilot facilities. The new information gives researchers, system designers, and even automated control systems the ability to tweak critical process parameters in real-time.
Plus, when PAT-style sensorization is implemented in the lab, it enables a much simpler transition of a fully optimized system into a GMP, production-scale environment.
3. Automate testing
Monitoring the growth of 10-15 mL cultures is of course critical to a researcher’s understanding of the influence of various growth conditions. However, since the culture volume is so small, removing a sample to take to an analyzer can change the culture conditions enough to effect the overall growth.
To solve this problem, scientists are switching to benchtop devices that automatically take samples from the culture and analyze parameters including pH, cell density, nutrient levels, and toxic waste levels. This process uses a smaller sample than a traditional analyzer requires, mitigating the effects of removing the sample from the culture. It also enables more frequent testing, without requiring the researcher to constantly attend to the system.
4. Build a closed-loop control system
When combined with DOE-based software, automatic testing systems can create a feedback loop from the cell culture to the reactor controls, offering an unprecedented level of real-time system control.
This type of tight process control ensures that scale-up parameters such as oxygen transfer rate are accurately measured, which then allows for the controlled development of pilot and production processes from benchtop-scale cultures. Close control of these parameters means that fewer scale-down cycles will be required to fully troubleshoot the pilot system, and it can be more rapidly transferred to a production environment.
5. Process intensification
The goal of process intensification is to develop engineering methods that increase efficiency and yield without increasing overall system costs.
In bioreactors, a key goal of process intensification is to increase the cell density in the reactor medium. This is primarily done by transitioning from 2D to 3D growth areas: bioreactors which grow cultures on microbeads or fibers are able to support a much larger number of cells/mL of culture medium (20 million cells/mL compared to over 100 million cells/mL).
Increasing yields in seed and production bioreactors without increasing manufacturing footprint leads to significant savings. “Scale-out” presents an interesting example: when cell densities can be increased to the extent that 2,000 L single-use bioreactors are sufficient to meet production demands (rather than transitioning to larger multiuse reactors), there are huge cost and time savings.
Another example of process intensification is using a perfusion strategy to grow a seed bioreactor, which enables the manufacturer to inoculate the production bioreactor at a much higher cell density. In this example, using optimized continuous bioreactors (a key piece of the Pharma 4.0 landscape) at a small scale enables much faster culture growth in the production bioreactor – and delivery of drug therapies to patients in record time.