  Development and optimization of biopharmaceutical production processes with
cell cultures is cost- and time-consuming and often performed rather
empirically. Efficient optimization of multiple-objectives like process time,
viable cell density, number of operating steps & cultivation scales, required
medium, amount of product as well as product quality depicts a promising
approach. This contribution presents a workflow which couples uncertainty-based
upstream simulation and Bayes optimization using Gaussian processes. Its
application is demonstrated in a simulation case study for a relevant
industrial task in process development, the design of a robust cell culture
expansion process (seed train), meaning that despite uncertainties and
variabilities concerning cell growth, low variations of viable cell density
during the seed train are obtained. Compared to a non-optimized reference seed
train, the optimized process showed much lower deviation rates regarding viable
cell densities (<~10% instead of 41.7%) using 5 or 4 shake flask scales and
seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it
is shown that applying Bayes optimization allows for optimization of a
multi-objective optimization function with several optimizable input variables
and 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 further
optimization tasks within process development.
