Examination of a genetic algorithm for the application in high‐throughput downstream process development

Abstract

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.

Recent advances in technology supporting biopharmaceutical production from mammalian cells

“…The performance of a downstream process could be improved by a reduced number of stages of purification or the implementation of a single-use system (Laukel et al 2011). Currently, strategies based on the quality by design, design of experiments (DoE), and process analytical technology have led to increased yields and a better understanding of such processes to allow their optimization (Pieracci et al 2010;Treier et al 2012). Different unit operations such as centrifugation, filtration, precipitation, and chromatography may be considered to exploit the physicochemical features of the compound of interest to enable the separation from its impurities.…”

Mixing at the microscale: Power input in shaken microtiter plates

“…Often, microtiter plates are used at the microscale, and they have radically different mixing properties compared to even the smallest stirred tank reactor. In the last few years there has been a paradigm shift toward high‐throughput screening (HTS) for process development, especially in biopharmaceutical quality by design , and this has increased the need for reliable scale‐up criteria and process understanding. HTS systems have been established for many production steps in upstream and downstream processing, and the scale of screening methods has been shifted from the L to the mL and µL scales.…”

“…(A) Images of clay flocks after 24 h of shaking in six-, 24-, and 96-well microtiter plates on orbital shakers with three different shaking diameters (3, 25, and 50 mm). (B-E) Mean equilibrium end flock size d FV in (B) six-, (C) 24-, and (D) 96-well microtiter plates after shaking for 24 h on orbital shakers with three different shaking diameters(3,25, and 50 mm) related to the critical shaking frequency n crit , and (E) after stirring for 24 h in the laboratory-and pilot-scale stirred tank reactors. All data shown are obtained from at least three experiments.…”

High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research

“…[ 260 ] Compared to conventional strategies, optimization methods can speed up and down streaming HTS processes and genetic algorithms can be applied to optimize parameters in multidimensional optimization tasks. [ 261 ]…”