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- Challenges and Chances: A Review of the 1st Stem Cell Community Day
- Summertime, and the Livin’ Is Easy…
- Follow-on-Biologics – More than Simple Generics
- Bacteria Versus Body Cells: A 1:1 Tie
- Behind the Crime Scene: How Biological Traces Can Help to Convict Offenders
- Every 3 Seconds Someone in the World Is Affected by Alzheimer's
- HIV – It’s Still Not Under Control…
- How Many Will Be Convicted This Time?
- Malaria – the Battle is Not Lost
- Physicians on Standby: The Annual Flu Season Can Be Serious
- At the Forefront in Fighting Cancer
- Molecular Motors: Think Small and yet Smaller Again…
- Liquid Biopsy: Novel Methods May Ease Cancer Detection and Therapy
- They Are Invisible, Sneaky and Disgusting – But Today It’s Their Special Day!
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- Why Do Old Men Have Big Ears?
- The Condemned Live Longer: A Potential Paradigm Shift in Genetics
- From Research to Commerce
- Chronobiology – How the Cold Seasons Influence Our Biorhythms
- Taskforce Microbots: Targeted Treatment from Inside the Body
- Eyes on Cancer Therapy
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JP | JPY
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.
This article appeared first in BioNews, Eppendorf’s customer magazine since 1993.
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What are important parameters in biologics upstream bioprocessing?
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.
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.
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表示を減らす