High Throughput Screening for the Design and Optimization of Chromatographic Processes: Automated Optimization of Chromatographic Phase Systems

Abstract

Chromatographic processes can be optimized in various ways. However, the two most prominent approaches are either based on statistical data analysis or on experimentally validated simulation models. Both approaches heavily rely on experimental data, the generation of which usually imposes a significant bottleneck on rational process design. Hence, here a closed-loop optimization strategy is proposed in that an automated high throughput liquid handling platform is combined with a genetic algorithm. This setup enables process optimization on the mini-scale and thus saves time as well as material costs. The practicability and robustness of the proposed high throughput method is demonstrated with two exemplary optimization tasks: first, optimization of the buffer composition in the capture step for a binary protein mixture (lysozyme and cytochrome), and second, optimization of multilinear gradient elution for the separation of a ternary mixture (ribonuclease and cytochrome, and lysozyme). IntroductionChromatography is widely used as a separation technique in the biotechnological industry. High selectivity and gentle conditions have made it an essential step in current purification processes for biological macromolecules, for instance, proteins. However, due to complex and dynamic interactions between protein molecules and adsorbent materials, the design of optimal separation processes is very difficult and time consuming. Heuristic design methods that are based on previous experiences with similar separation problems require a great amount of expert knowledge and usually do not lead to the global process optimum. Furthermore, process optimization is often restricted by time-to-market requirements and must, hence, be performed as fast as possible.Most methods for process optimization that are found in the literature today divide into two classes: model-based optimization and direct process optimization. In model-based optimization, mathematical models are utilized to mimic the studied processes. Optimization is performed in-silico and thus has the clear advantage of not restricting the optimization by lab schedules. Limiting factors are only computational effort and reliability or validity of the applied simulation models. The development of mechanistic models requires good process understanding, initial experiments for parameter estimation, and independent experiments for model validation. The latter is also true for black box models (for example [1,2]). The determination of mechanistic model parameters, such as effective mass transfer coefficients and isotherm coefficients, is generally very complex and requires large amounts of material and time, especially when the interactions of realistic multi-component mixtures are considered without significant model simplifications.An alternative to the model-based approach is to directly identify process optima based on the results of experiments that are iteratively planned by an optimization algorithm, such as repeated design of experiments (DoE) or an evolutionary strat...

High Throughput Screening for the Design and Optimization of Chromatographic Processes – Miniaturization, Automation and Parallelization of Breakthrough and Elution Studies

“…A major issue in the coming years will be the use of statistical and mathematical methods for 'intelligent' parameter generation, either to determine an optimum system point or to aid model-based development [17][18][19]. The integration of more sophisticated analytical methods such as enzymatic as- --microcolumn operated on the LHS, -miniaturized column connected to ÄKTA system, ---HR 5/100 column connected to ÄKTA system.…”

“…In the present paper this system is described in detail and applied to industrially relevant separation problems. This study thus prepares the stage for work on packed bed chromatographic high throughput screening approaches and process development strategies [17,18].…”

Intelligent Automation Platform for Bioprocess Development

“…Most methods in the literature are either model-based or direct process optimization (Susanto et al 2009). …”

“…An application of EA in chromatographic phase systems was to optimize the buffer composition in the capture step for a binary protein mixture of lysozym and cytochrome and the multi-linear gradient elution for the separation of a ternary mixture of ribonuclease, cytochrome, and lysozyme (Susanto et al 2009). The research proposed a closed-loop optimization strategy that integrates data storage in Excel, data analysis by EA in Matlab, and automated devices of Tecan Evoware as shown in Figure 1.11.…”

A label‐free methodology for selective protein quantification by means of absorption measurements

“…Alongside the development of high throughput experimentation (HTE) techniques, a trend of replacing the traditional ''one factor at the time'' screenings with more sophisticated approaches such as factorial designs, intelligent search algorithms, and mechanistic modeling has evolved (Degerman et al, 2009;Oelmeier et al, 2011;Susanto et al, 2009). These approaches make process development more efficient and can result in a deeper understanding of the developed processes.…”