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Optimization of protein refolding parameters by automated, miniaturized, and parallelized high throughput screening is a powerful approach to meet the demand for fast process development with low material consumption. In this study, we validated methods applicable on a standard liquid handling robot for screening of refolding process parameters by dilution of denatured lysozyme in refolding buffer systems. Different approaches for the estimation of protein solubility and folding were validated concerning resolution and compatibility with the robotic system and with the complex buffer and protein structure composition. We established an indirect method to assess soluble lysozyme concentration independent of matrix effects and protein structure varieties by automated separation of aggregated protein, resolubilization, and measurement of absorption at 280 nm. Using this nonspecific solubility assays the correlation between favorable parameters for high active and soluble lysozyme yields were evaluated. An overlap of good refolding buffer compositions was found provided that the redox environment was controlled with redox reagents. In addition, the need to control unfolding conditions like time, temperature, lysozyme, and dithiothreitol concentration was pointed out as different feedstocks resulted in different refolding yields.

Compared to traditional strategies, application of high-throughput experiments combined with optimization methods can potentially speed up downstream process development and increase our understanding of processes. In contrast to the method of Design of Experiments in combination with response surface analysis (RSA), optimization approaches like genetic algorithms (GAs) can be applied to identify optimal parameter settings in multidimensional optimizations tasks. In this article the performance of a GA was investigated applying parameters applicable in high-throughput downstream process development. The influence of population size, the design of the initial generation and selection pressure on the optimization results was studied. To mimic typical experimental data, four mathematical functions were used for an in silico evaluation. The influence of GA parameters was minor on landscapes with only one optimum. On landscapes with several optima, parameters had a significant impact on GA performance and success in finding the global optimum. Premature convergence increased as the number of parameters and noise increased. RSA was shown to be comparable or superior for simple systems and low to moderate noise. For complex systems or high noise levels, RSA failed, while GA optimization represented a robust tool for process optimization. Finally, the effect of different objective functions is shown exemplarily for a refolding optimization of lysozyme.

Continuous chromatography is increasingly being used across the biotechnology industry due to its economic advantages. For adoption in commercial manufacturing, also models for virus clearance studies must be available. It is demonstrated how for a virus clearance study for a multispecific antibody, the continuous protein A capture chromatography process, being run on multiple interconnected columns, can be mimicked with only a single column. With this mimicking small‐scale model, resources and complexity can be minimized, when conducting virus clearance studies at a contract research organization (CRO) lab. Obtained log reduction values (LRV) for murine leukemia virus (xMuLV) and minute virus of mice (MVM) virus, used as model viruses, are comparable to batch protein A chromatography and results described by other groups. The feasibility of this mimicking small‐scale model helps to further reduce barriers to adoption when implementing continuous chromatography.

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