(787g) Data-Driven Optimization Using an Evolutionary Design of Dynamic Experiments for Biopharmaceutical Processes
AIChE Annual Meeting
2016
2016 AIChE Annual Meeting
Food, Pharmaceutical & Bioengineering Division
Innovations in Biopharmaceutical Discovery, Development, and Manufacturing II
Friday, November 18, 2016 - 5:27pm to 5:49pm
Here we apply the DoDE method in an evolutionary manner to optimize biopharmaceutical processes while satisfying budgetary and developmental time constraints on the number of experiments in two representative biopharmaceutical processes; Penicillin fermentation [6] and Hybridoma cell culture [7]. We maximize the amount of product produced by determining the optimal feeding policy and batch duration.
To minimize the number of experiments we initially consider only a few dynamic sub-factors and the simplest Response Surface Model (RSM). After the initial results are analysed and an approximate RSM is estimated, additional experiments are added sequentially to further improve the data-driven model and the process performance. We demonstrate that with a reasonable amount of evolutionary experiments one can come very close to the model-based optimum. The model-based optimization [8], however, requires a very accurate knowledge-driven model that is rarely at hand.
References
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