Rapid development of chromatographic processes relies on effective high-throughput screening (HTS) methods. This article describes the development of pseudo-linear gradient elution for resin selectivity screening using RoboColumns . It gives guidelines for the implementation of this HTS method on a Tecan Freedom EVO robotic platform, addressing fundamental aspects of scale down and liquid handling. The creation of a flexible script for buffer preparation and column operation plus efficient data processing provided the basis for this work. Based on the concept of discretization, linear gradient elution was transformed into multistep gradients. The impact of column size, flow rate, multistep gradient design, and fractionation scheme on separation efficiency was systematically investigated, using a ternary model protein mixture. We identified key parameters and defined optimal settings for effective column performance. For proof of concept, we examined the selectivity of several cation exchange resins using various buffer conditions. The final protocol enabled a clear differentiation of resin selectivity on miniature chromatography column (MCC) scale. Distinct differences in separation behavior of individual resins and the influence of buffer conditions could be demonstrated. Results obtained with the robotic platform were representative and consistent with data generated on a conventional chromatography system. A study on antibody monomer/high molecular weight separation comparing MCC and lab scale under higher loading conditions provided evidence of the applicability of the miniaturized approach to practically relevant feedstocks with challenging separation tasks as well as of the predictive quality for larger scale. A comparison of varying degrees of robotic method complexity with corresponding effort (analysis time and labware consumption) and output quality highlights tradeoffs to select a method appropriate for a given separation challenge or analytical constraints. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1503-1519, 2016.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6585662PMC
http://dx.doi.org/10.1002/btpr.2363DOI Listing

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