Selective protein quantification for preparative chromatography using variable pathlength UV/Vis spectroscopy and partial least squares regression

Abstract In preparative protein chromatography, broad dynamic ranges of protein concentrations as well as co-elution of product and impurities are common. Despite being the standard in biopharmaceutical production, monitoring of preparative chromatography is generally limited to surrogate signals, e.g. UV absorbance at 280 nm. To address this problem, variable pathlength (VP) spectroscopy in conjunction with Partial Least Squares regression (PLS) was used to monitor preparative chromatography. While VP spectroscopy enabled the acquisition of absorbance data for a broad concentration range, PLS modelling allowed for the differentiation between the protein species. The approach was first implemented for monitoring the separation of lysozyme from cytochrome c at an overall loading density of 92 g/l. The same method was then applied to the polishing step of a monoclonal antibody (mAb) at 40 g/l loading density. For PLS model prediction of the mAb monomer and the high molecular weight variants (HMWs), the root mean square error (RMSE) was 1.07 g/l and 0.42 g/l respectively. To demonstrate the usability of the approach for in-line control, pooling decisions for both separation problems were subsequently taken based on the computed concentrations or thereof derived purities. In summary, VP spectroscopy in conjunction with PLS modelling is a promising option for in-line monitoring and control of future chromatography steps at large scale.

[1]  Aline Zimmer,et al.  Mid‐infrared spectroscopy‐based analysis of mammalian cell culture Parameters , 2015, Biotechnology progress.

[2]  Jürgen Hubbuch,et al.  A label‐free methodology for selective protein quantification by means of absorption measurements , 2011, Biotechnology and bioengineering.

[3]  Yuefeng Lu,et al.  Risk-benefit evaluation of on-line high-performance liquid chromatography analysis for pooling decisions in large-scale chromatography. , 2012, Journal of chromatography. A.

[4]  Daan J.A. Crommelin,et al.  Methods for structural analysis of protein pharmaceuticals , 2005 .

[5]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[6]  V. Menoux,et al.  Variable path-length, low-temperature cells for absorption spectroscopy , 1985 .

[7]  L. Eriksson Multi- and megavariate data analysis , 2006 .

[8]  Jürgen Hubbuch,et al.  Real‐time monitoring and control of the load phase of a protein A capture step , 2016, Biotechnology and bioengineering.

[9]  Alois Jungbauer,et al.  Protein Chromatography: Process Development and Scale-Up , 2010 .

[10]  A S Rathore,et al.  Process analytical technology (PAT) for biopharmaceutical products , 2010, Analytical and bioanalytical chemistry.

[11]  A. Höskuldsson PLS regression methods , 1988 .

[12]  J. Hubbuch,et al.  Advances in downstream processing of biologics - Spectroscopy: An emerging process analytical technology. , 2017, Journal of chromatography. A.

[13]  J. Hubbuch,et al.  Application of spectral deconvolution and inverse mechanistic modelling as a tool for root cause investigation in protein chromatography. , 2016, Journal of chromatography. A.

[14]  Anurag S. Rathore,et al.  Application of process analytical technology for downstream purification of biotherapeutics , 2015 .

[15]  P. Flowers,et al.  Variable path length transmittance cell for ultraviolet, visible, and infrared spectroscopy and spectroelectrochemistry. , 1996, Analytical chemistry.

[16]  Baisheng Chen,et al.  Development of variable pathlength UV-vis spectroscopy combined with partial-least-squares regression for wastewater chemical oxygen demand (COD) monitoring. , 2014, Talanta.

[17]  J. Hubbuch,et al.  A tool for selective inline quantification of co‐eluting proteins in chromatography using spectral analysis and partial least squares regression , 2014, Biotechnology and bioengineering.

[18]  A. Osberghaus,et al.  Deconvolution of high‐throughput multicomponent isotherms using multivariate data analysis of protein spectra , 2016 .

[19]  R L Fahrner,et al.  Real-time control of purified product collection during chromatography of recombinant human insulin-like growth factor-I using an on-line assay. , 1998, Journal of chromatography. A.

[20]  Anurag S Rathore,et al.  Large scale demonstration of a process analytical technology application in bioprocessing: Use of on‐line high performance liquid chromatography for making real time pooling decisions for process chromatography , 2009, Biotechnology progress.

[21]  Santosh V. Thakkar,et al.  An application of ultraviolet spectroscopy to study interactions in proteins solutions at high concentrations. , 2012, Journal of pharmaceutical sciences.

[22]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[23]  Jürgen Hubbuch,et al.  Advances in inline quantification of co‐eluting proteins in chromatography: Process‐data‐based model calibration and application towards real‐life separation issues , 2015, Biotechnology and bioengineering.