Real‐time monitoring and control of the load phase of a protein A capture step

Abstract

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.

Prediction of the Quantity and Purity of an Antibody Capture Process in Real Time

“…value 112 g L −1 ). Rüdt et al, on the other hand, predicted antibody breakthrough during capture resulting in an RMSE of prediction of antibody quantity as low as 0.01 g L −1 (max. value 3 g L −1 ) using a UV detector.…”

“…Other studies dealing with online monitoring of an antibody capture process have been published using only one online sensor. [11,12,14] Großhans et al [23] separated an antibody from lysozyme with a cation exchanger and predicted the elution behavior via an FTIR detector yielding an RMSE of prediction of the antibody [19] on the other hand, predicted antibody breakthrough during capture resulting in an RMSE of prediction of antibody quantity as low as 0.01 g L −1 (max. value 3 g L −1 ) using a UV detector.…”

“…This limits the application to certain detectors, which fulfill the required specifications. Brestrich et al as well as Rüdt et al demonstrated that multivariate UV/VIS spectroscopy in combination with partial least‐squares regression (PLS) modeling is well suited for the prediction of different quality parameters in chromatographic applications. In the recent work of Brestrich et al, oversaturation of the sensor was prevented by using a flow cell with a variable pathlength.…”

“…To relate the measured online signals to the respective quality attribute of interest, we used PLS regression models. This type of multivariate statistical tool is commonly used for data‐based modeling . It is a linear model relating one (or more) response variables such as quantity or monomer content to several predictor variables such as spectral information from single wavelengths.…”

“…This type of multivariate statistical tool is commonly used for data-based modeling. [17][18][19]22,24] It is a linear model relating one (or more) response variables such as quantity or monomer content to several predictor variables such as spectral information from single wavelengths. Typically, the data set is split into training, validation, and test sets in a ratio of 2:1:1.…”