Hernandez Rodriguez, Tanja Nadine, Sekulic, Anton, Lange-Hegermann, Markus and Frahm, Björn (2022) Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design. MDPI Processes, 10 (5). p. 883.

Abstract

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 demonstrate in a simulation case study for a relevant industrial task in process development, the design of a robust cell culture expansion process (so called 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.

Item Type:	Article
Keywords:	Gaussian processes; Bayes optimization; Pareto optimization; multi-objective; cell culture; seed train
Date Deposited:	14 Jan 2023 00:45
Last Modified:	14 Jan 2023 00:45
URI:	https://oak.novartis.com/id/eprint/47143