title: Application of spectral deconvolution and inverse mechanistic modelling as a tool for root cause investigation in protein chromatography.
abstract: In chromatographic protein purification, process variations, aging of columns, or processing errors can lead to deviations of the expected elution behavior of product and contaminants and can result in a decreased pool purity or yield. A different elution behavior of all or several involved species leads to a deviating chromatogram. The causes for deviations are however hard to identify by visual inspection and complicate the correction of a problem in the next cycle or batch. To overcome this issue, a tool for root cause investigation in protein chromatography was developed. The tool combines a spectral deconvolution with inverse mechanistic modelling. Mid-UV spectral data and Partial Least Squares Regression were first applied to deconvolute peaks to obtain the individual elution profiles of co-eluting proteins. The individual elution profiles were subsequently used to identify errors in process parameters by curve fitting to a mechanistic chromatography model. The functionality of the tool for root cause investigation was successfully demonstrated in a model protein study with lysozyme, cytochrome c, and ribonuclease A. Deviating chromatograms were generated by deliberately caused errors in the process parameters flow rate and sodium-ion concentration in loading and elution buffer according to a design of experiments. The actual values of the three process parameters and, thus, the causes of the deviations were estimated with errors of less than 4.4%. Consequently, the established tool for root cause investigation is a valuable approach to rapidly identify process variations, aging of columns, or processing errors. This might help to minimize batch rejections or contribute to an increased productivity.
authors.affiliation: Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology, Germany.
authors.forename: Nina
authors.initials: N
authors.lastname: Brestrich
authors.affiliation: Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology, Germany.
authors.forename: Tobias
authors.initials: T
authors.lastname: Hahn
authors.affiliation: Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology, Germany. Electronic address: juergen.hubbuch@kit.edu.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Hubbuch
mesh_terms: D002845:Chromatography / Q000379:methods* / Q000592:standards*; D016018:Least-Squares Analysis; D008956:Models, Chemical; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000302:isolation & purification; D012107:Research Design
chemical_list: D011506:Proteins
keywords: Inline monitoring; Mechanistic modelling; Partial Least Squares Regression; Process Analytical Technology; Root cause investigation; Selective protein quantification