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From Data to Control: Process Analytics in Bioprocessing
ULRIKE RASCHE Lab Academy
Reproducibility is essential in the production of biologics. That is why understanding interdependencies between process parameters and establishing control strategies are so important in upstream bioprocess development. Thorough process analytics is a prerequisite. Joerg Schwinde, Key Segment Manager Vaccines and Monoclonal Antibodies at Eppendorf SE Bioprocess Center, shares his perspective on which strategies will help labs best optimize upstream bioprocess analytics both now and in the future.
Joerg Schwinde: Besides process parameters like pH, temperature, and dissolved oxygen, there’s the behavior of a strain or cell line – for example, the growth kinetics, the ratio between total and viable cell density, and the productivity of the cells. Metabolite concentrations are also important. Furthermore, the product needs to be characterized: is it indeed the target product? Are there undesirable byproducts that can significantly affect product quality? Monitoring all of this is quite a complex task.
What strategies is industry applying to control relevant bioprocess parameters?
JS: The monitoring of parameters can be offline, as with external analyzers in combination with sampling devices. This sampling can be automated, but the challenges here include the additional manual workload and the absence of automated feedback loops. Automated feedback loops are possible through online analyzers which provide almost real-time data and spare sampling steps. Setting them up requires a bioprocess control software which receives the sensor signals and controls the acting units inside the process control system – for example, an aeration unit or a pump. To ensure communication between the control software and the analyzer hardware, analog and digital options are available.
One communication standard in this context is what’s known as open platform communication (OPC). To reduce the workload, speed up the process, and more, the online solution with the option for automated feedback control is very attractive.
In your opinion, which developments will become more important in upstream bioprocessing in the coming years?
JS: Data acquisition, analytics, and process automation will get faster, even more precise, and more powerful. This will be supported by predictive analyses (i.e., design-of-experiment approaches) and by artificial intelligence. Those options provide tremendous opportunities to simulate processes ahead of time and predict where challenges lie and how to bypass them successfully – all of which contributes to time and cost savings as well as to safety.