Many treatments such as immunotherapy for cancer, autoimmune and neurological diseases need to create antibodies that bind to and label diseased cells for the body to target and destroy. Optimising bioreactors, developing seed strains for the growth of the antibodies, and improving cell cultures can often take a lab 40+ weeks and priorities for these projects often shift within that time. When the appropriate strains are identified, the process needs to be scaled and validated, eventually making its way to clinical trials. This can add weeks or even years to the amount of time it takes for treatments to get from the lab to the people in need.
Here are five underutilised ways to optimise the bioreactor yields, shorten timelines for scaling processes and producing biologic or antibody treatments.
Proteins for different treatments have their own optimal growth conditions that ensure correct protein folding and assembly. Growth conditions must be further optimized to maximize protein yields.
The growing of antibodies for immunotherapies from Chinese Hamster Ovarian Cells have specific cell culture conditions that include: 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.
It is unrealistic to independently enhance each variable due to the interdependence of variables, time constraints and resource limitations. 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 and statistical analysis are quantitative tools that can simplify the complicated task of process optimization.
The setup in a common lab involves using cell cultures as small as 10 mL to enhance 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, that can help researchers design these small-scale systems to manipulate multiple variables simultaneously and efficiently determine the optimal set of critical process parameters.
Design of experiments can also useful for troubleshooting in scaled-down systems ranging in size from 500 mL to 10 L. This allows the system designers to move from problem solving through trial and error to having solutions based on statistically relevant data accounting for a number of interacting process variables.
Process analytical technology (PAT) simply refers to adding on-line sensors and other means of data collection, that can gather information from pharmaceutical manufacturing processes. This can increase the understanding of what is happening in the bioreactor and how it responds to any changes – and allows the bioreactor designers to adjust controllers based on real-time data.
While PAT is typically discussed in the context of GMP production environments, the concept of increased sensorisation can be equally as beneficial in labs and pilot facilities. The new information gives researchers, bioreactor designers, and even automated control systems the ability to tweak critical process parameters in real-time which can improve the development timeline.
Plus, when PAT-style sensorisation is implemented in the lab, it allows for a much simpler transition of a fully optimized bioreactor into a GMP, production-scale environment.
The monitoring of growth in cultures of 10-15mL is vital to the researchers understanding over the influence of various growth conditions. However, as this culture volume is so small removing a sample to take to an analyser can change the culture conditions enough to effect the overall growth.
To overcome the problem scientists making a switch to benchtop bioreactors that automatically take samples from the culture and analyse parameters including pH, cell density, nutrient levels, and toxic waste levels. This process uses a smaller sample than a traditional bioreactor requires, mitigating the effects of removing the sample from the culture. This also allows for more frequent testing without requiring the researcher to constantly attend to the system which saves time in the lab.
When combined with DOE-based software, automatic testing bioreactor systems can create a feedback loop from the cell culture to the reactor controls. This offers an unprecedented level of real-time system control.
This type of tight process control guarantees that scale-up parameters such as oxygen transfer rates are accurately measured, which then allows for the controlled development of pilot and production processes from benchtop-scale cultures. Keeping close control of these parameters allows fewer scale-down cycles to be required fully troubleshoot the pilot bioreactor system, and it can be more rapidly transferred to a production environment.
Process intensifications outcome should be to develop engineering methods that improve efficiency and yield without increasing overall bioreactor system costs.
In bioreactors, a key goal of process intensification is to increase the cell density in the reactor medium. The process of increasing cell density in the reactor is done by transitioning from 2D to 3D growth areas: bioreactors that grow cultures on microbeads or fibres have the ability to support a much larger number of cells/mL of culture medium (20 million cells/mL compared to over 100 million cells/mL).
The increase of yields in both seed and bioreactor production without growing the manufacturing footprint leads to significant savings. An example of ‘scaling out’ is when cell densities can be increased to the extent that 2,000 L single-use bioreactors are efficient enough to meet production demands which eliminates the need to buy a bigger bioreactor system, therefore saving time and money in the lab.
Process intensification can also be demonstrated by using a perfusion strategy to grow a seed bioreactor. This 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 allows for faster culture growth in the production bioreactor – and delivery of drug therapies to patients in record time.