Genetic Engineering & Biotechnology News

NOV1 2018

Genetic Engineering & Biotechnology News (GEN) is the world's most widely read biotech publication. It provides the R&D community with critical information on the tools, technologies, and trends that drive the biotech industry.

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Page 54 of 57 | SEPTEMBER 1, 2018 | 17 growth in single-use bioprocessing presents new challenges in how to le- verage flexibility, optimize change- overs, and incorporate automation and its associated data to gain efficiency. When a transition from a stainless- steel environment occurs, the simplicity of a single-use process can be stunning. Gone are the hundreds of feet of stain- less piping, transfer panel jumpers, and numerous automated ported isolation valves. Connecting one bioreactor to another is a single piece of disposable tubing. However, unlike stainless- steel–based bioproduction, single-use bioprocessing requires operators to route and secure tubing, make proper connections (such as sterile connector assemblies and tube welds), remove tubing clamps, and complete a host of other actions. Automation in single-use becomes a combination of automated process control augmented by guiding the op- erator with their manual operations. Automated platforms simplify interac- tions between process equipment and reduce the possibility that human error will influence product quality. Equip- ment behaves predictably, reliably, and consistently. Automated operator reminders and confirmations can im- prove the consistency of the required manual operations. The real-time con- trol of equipment through automa- tion allows the system to dynamically adjust for detected variability, assuring that the process performs optimally and that the end product is consistent. These improvements lead to better yields, less waste, and higher quality in the final product. Bioprocessing Joins the World of Big Data The benefits of automated control are well understood, but we are just scratching the surface of utilizing the data that automated systems provide. big data analytics is a transformative, Single-Use Technologies for Bioprocessing Figure. Using proprietary machine learning (ML) models, GE Healthcare Life Sciences data scientists showed that specific dialable feed parameters interact to affect monoclonal antibody (mAb) batch yields in large mammalian cell bioreactors, where specific values of Parameter 1 (feed input volume) and Parameter 2 (a feed media quality parameter) interact to affect mAb yield. Such ML models evince biopharma's commitment to the Industry 4.0 paradigm, which entails the use of cyber-physical systems. The big leap forward is the utilization of big data to develop and validate models that can predict and optimize process parameters and sequencing.

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