(665d) Using Machine Learning Tools in Bioprocess Scale up When the Number of Batches Is Small
AIChE Annual Meeting
2017
2017 Annual Meeting
Pharmaceutical Discovery, Development and Manufacturing Forum
Innovations in Biopharmaceutical Discovery, Development, and Manufacturing
Thursday, November 2, 2017 - 9:06am to 9:28am
Viktor Konakovsky1, Graham McCreath1 and Jarka Glassey2
Modelling efforts in biotechnology historically focused around building and validating data using mechanistic, first principle models. During scale up from laboratory (2L), intermediate (10L), demonstration (200L) to manufacturing scale (2000L) there is limited time to build such models with the required diligence and care. For this reason, in the absence of an established platform process, historical small scale screening runs were analysed with machine learning tools to support scale up activities with exploratory and predictive modelling. However, there is a significant caveat by using such datasets; the data generated in scale down models may not necessarily be predictive enough as scale is changed, and a final validation of the model is not possible before the actual manufacturing scale run. We are aware of these challenges, and will present our approaches as well as recommendations, using a mAb-producing CHO cell process as case study.
Acknowledgements: This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under the Marie SkÅodowska-Curie grant agreement No 643056.
[1] Jason Brownlee, Machine Learning With R (2016)
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