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.
Keywords: Inline monitoring; Mechanistic modelling; Partial Least Squares Regression; Process Analytical Technology; Root cause investigation; Selective protein quantification.
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