The load phase in preparative Protein A capture steps is commonly not controlled in real‐time. The load volume is generally based on an offline quantification of the monoclonal antibody (mAb) prior to loading and on a conservative column capacity determined by resin‐life time studies. While this results in a reduced productivity in batch mode, the bottleneck of suitable real‐time analytics has to be overcome in order to enable continuous mAb purification. In this study, Partial Least Squares Regression (PLS) modeling on UV/Vis absorption spectra was applied to quantify mAb in the effluent of a Protein A capture step during the load phase. A PLS model based on several breakthrough curves with variable mAb titers in the HCCF was successfully calibrated. The PLS model predicted the mAb concentrations in the effluent of a validation experiment with a root mean square error (RMSE) of 0.06 mg/mL. The information was applied to automatically terminate the load phase, when a product breakthrough of 1.5 mg/mL was reached. In a second part of the study, the sensitivity of the method was further increased by only considering small mAb concentrations in the calibration and by subtracting an impurity background signal. The resulting PLS model exhibited a RMSE of prediction of 0.01 mg/mL and was successfully applied to terminate the load phase, when a product breakthrough of 0.15 mg/mL was achieved. The proposed method has hence potential for the real‐time monitoring and control of capture steps at large scale production. This might enhance the resin capacity utilization, eliminate time‐consuming offline analytics, and contribute to the realization of continuous processing. Biotechnol. Bioeng. 2017;114: 368–373. © 2016 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals, Inc.
Fourier-transform infrared spectroscopy (FTIR) is a well-established spectroscopic method in the analysis of small molecules and protein secondary structure. However, FTIR is not commonly applied for in-line monitoring of protein chromatography. Here, the potential of in-line FTIR as a process analytical technology (PAT) in downstream processing was investigated in three case studies addressing the limits of currently applied spectroscopic PAT methods. A first case study exploited the secondary structural differences of monoclonal antibodies (mAbs) and lysozyme to selectively quantify the two proteins with partial least squares regression (PLS) giving root mean square errors of cross validation (RMSECV) of 2.42 g/l and 1.67 g/l, respectively. The corresponding Q values are 0.92 and, respectively, 0.99, indicating robust models in the calibration range. Second, a process separating lysozyme and PEGylated lysozyme species was monitored giving an estimate of the PEGylation degree of currently eluting species with RMSECV of 2.35 g/l for lysozyme and 1.24 g/l for PEG with Q of 0.96 and 0.94, respectively. Finally, Triton X-100 was added to a feed of lysozyme as a typical process-related impurity. It was shown that the species could be selectively quantified from the FTIR 3D field without PLS calibration. In summary, the proposed PAT tool has the potential to be used as a versatile option for monitoring protein chromatography. It may help to achieve a more complete implementation of the PAT initiative by mitigating limitations of currently used techniques.
Selective quantification of co-eluting proteins in chromatography is usually performed by offline analytics. This is time-consuming and can lead to late detection of irregularities in chromatography processes. To overcome this analytical bottleneck, a methodology for selective protein quantification in multicomponent mixtures by means of spectral data and partial least squares regression was presented in two previous studies. In this paper, a powerful integration of software and chromatography hardware will be introduced that enables the applicability of this methodology for a selective inline quantification of co-eluting proteins in chromatography. A specific setup consisting of a conventional liquid chromatography system, a diode array detector, and a software interface to Matlab® was developed. The established tool for selective inline quantification was successfully applied for a peak deconvolution of a co-eluting ternary protein mixture consisting of lysozyme, ribonuclease A, and cytochrome c on SP Sepharose FF. Compared to common offline analytics based on collected fractions, no loss of information regarding the retention volumes and peak flanks was observed. A comparison between the mass balances of both analytical methods showed, that the inline quantification tool can be applied for a rapid determination of pool yields. Finally, the achieved inline peak deconvolution was successfully applied to make product purity-based real-time pooling decisions. This makes the established tool for selective inline quantification a valuable approach for inline monitoring and control of chromatographic purification steps and just in time reaction on process irregularities.
Pooling decisions in preparative liquid chromatography for protein purification are usually based on univariate UV absorption measurements that are not able to differentiate between product and co-eluting contaminants. This can result in inconsistent pool purities or yields, if there is a batch-to-batch variability of the feedstock. To overcome this analytical bottleneck, a tool for selective inline quantification of co-eluting model proteins using mid-UV absorption spectra and Partial Least Squares Regression (PLS) was presented in a previous study and applied for real-time pooling decisions. In this paper, a process-data-based method for the PLS model calibration will be introduced that allows the application of the tool towards chromatography steps of real-life processes. The process-data-based calibration method uses recorded inline mid-UV absorption spectra that are correlated with offline fraction analytics to calibrate PLS models. In order to generate average spectra from the inline data, a Visual Basic for Application macro was successfully developed. The process-data-based model calibration was established using a ternary model protein system. Afterwards, it was successfully demonstrated in two case studies that the calibration method is applicable towards real-life separation issues. The calibrated PLS models allowed a successful quantification of the co-eluting species in a cation-exchange-based aggregate and fraction removal during the purification of monoclonal antibodies and of co-eluting serum proteins in an anion-exchange-based purification of Cohn supernatant I. Consequently, the presented process-data-based PLS model calibration in combination with the tool for selective inline quantification has a great potential for the monitoring of future chromatography steps and may contribute to manage batch-to-batch variability by real-time pooling decisions.
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