3D‐liquid chromatography as a complex mixture characterization tool for knowledge‐based downstream process development. (5th August 2016)

• Record Type: Journal Article
• Authors: Hanke, Alexander T.; Tsintavi, Eleni; Ramirez Vazquez, Maria del Pilar; van der Wielen, Luuk A. M.; Verhaert, Peter D. E. M.; Eppink, Michel H. M.; van de Sandt, Emile J. A. X.; Ottens, Marcel
• Is Part Of: Biotechnology progress. Volume 32:Number 5(2016:Sep./Oct.)
• Page Start: 1283
• Page End: 1291
• Publication Date: 2016-08-05
• Subjects: multi‐dimensional chromatography -- host cell proteins -- process development -- feedstock characterization; Biotechnology -- Periodicals; Food industry and trade -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/btpr.2320
• Languages: English
• ISSNs: 8756-7938

Abstract

Abstract : Knowledge‐based development of chromatographic separation processes requires efficient techniques to determine the physicochemical properties of the product and the impurities to be removed. These characterization techniques are usually divided into approaches that determine molecular properties, such as charge, hydrophobicity and size, or molecular interactions with auxiliary materials, commonly in the form of adsorption isotherms. In this study we demonstrate the application of a three‐dimensional liquid chromatography approach to a clarified cell homogenate containing a therapeutic enzyme. Each separation dimension determines a molecular property relevant to the chromatographic behavior of each component. Matching of the peaks across the different separation dimensions and against a high‐resolution reference chromatogram allows to assign the determined parameters to pseudo‐components, allowing to determine the most promising technique for the removal of each impurity. More detailed process design using mechanistic models requires isotherm parameters. For this purpose, the second dimension consists of multiple linear gradient separations on columns in a high‐throughput screening compatible format, that allow regression of isotherm parameters with an average standard error of 8%. © 2016 American Institute of Chemical Engineers Biotechnol. Prog ., 32:1283–1291, 2016