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Development and optimization of biopharmaceutical production processes withcell cultures is cost- and time-consuming and often performed ratherempirically. Efficient optimization of multiple-objectives like process time,viable cell density, number of operating steps & cultivation scales, requiredmedium, amount of product as well as product quality depicts a promisingapproach. This contribution presents a workflow which couples uncertainty-basedupstream simulation and Bayes optimization using Gaussian processes. Itsapplication is demonstrated in a simulation case study for a relevantindustrial task in process development, the design of a robust cell cultureexpansion process (seed train), meaning that despite uncertainties andvariabilities concerning cell growth, low variations of viable cell densityduring the seed train are obtained. Compared to a non-optimized reference seedtrain, the optimized process showed much lower deviation rates regarding viablecell densities (<~10% instead of 41.7%) using 5 or 4 shake flask scales andseed train duration could be reduced by 56 h from 576 h to 520 h. Overall, itis shown that applying Bayes optimization allows for optimization of amulti-objective optimization function with several optimizable input variablesand under a considerable amount of constraints with a low computational effort.This approach provides the potential to be used in form of a decision tool,e.g. for the choice of an optimal and robust seed train design or for furtheroptimization tasks within process development.

processes

Designing Robust Biotechnological Processes Regarding Variabilities  using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train  Design