Optimization of process conditions for mammalian fed-batch cell culture in automated micro-bioreactor system using genetic algorithm

• Published in: Journal of biotechnology 2019-07, Vol.300, p.40-47
• Main Authors: Brinc, Matjaž, Belič, Aleš
• Format: Article
• Language: English
• Subjects: Bioprocess; CHO; Experimental design; Genetic algorithm; Mammalian cells; Recombinant protein
• ISSN: 0168-1656; 1873-4863
• DOI: 10.1016/j.jbiotec.2019.05.001

•GAs are a viable method of experimental design for mammalian bioprocess optimization.•Product concentration was improved up to 87% within 132 experiments.•Convergence of parameter values confirmed the directed action of GAs.•Despite stochastic nature of GAs, valuable information can be extracted by MVDA. Recombinant proteins produced by mammalian cell culture technology represent an important segment of therapeutic molecules. Development of their manufacturing processes is a time- and resource-consuming task. A wide array of process conditions, e.g. physico-chemical parameters, medium composition, feeding strategy, needs to be optimized to design a commercially feasible process with the desired productivity and product characteristics. Traditionally, statistical experimental designs, i.e. design-of-experiments methodology, have been used for such optimizations. However, statistical design approach has several limitations related to high dimensionality of the explored parameter space originating from the complexity of the mammalian cell culture processes. An alternative is therefore desired to overcome these limitations. In this study, we have successfully used a simple genetic algorithm as a method of experimental design for optimization of mammalian cell culture processes for two recombinant cell lines, one expressing a monoclonal antibody and one an Fc-fusion protein. Harnessing the automation capability of a robotically driven micro-bioreactor system to execute the genetic algorithm-derived experiments, a set of 14 process parameters was optimized within 132 experiments per cell line (six generations of 22 experiments), showing the feasibility of this approach as an alternative to classical statistical experimental designs.