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

Abstract

Regulatory recommendations for quality by design instead of quality by testing raise increasing interest in new sensor technologies. An online monitoring system for downstream processes is developed, which is based on an array of online detectors. Besides standard detectors (UV, pH, and conductivity), our chromatographic workstation is equipped with a fluorescence and a mid‐infrared spectrometer, a light scattering, and a refractive index detector. The combination of these sensors enables the prediction of specific protein concentration and various purity attributes, such as high molecular weight impurities, DNA and host cell protein content during the elution phase of a chromatographic antibody capture process. Prediction models solely based on online signals are set up providing real‐time predictions. No mechanistic models or information about the chromatographic runs is used. These predictions allow online pooling decisions replacing time‐ and labor‐intensive laboratory measurements. Different process variations, such as changes in the column load or elution buffer, are introduced to test the predictive power of the models. Extrapolation of the models worked well when the column load is changed, whereas model adjustment is necessary when the elution conditions are changed considerably.

A critical review of recent trends, and a future perspective of optical spectroscopy as PAT in biopharmaceutical downstream processing

“…Another approach, besides calculating the attributes of interest from different sensors by physically founded equations, is to fuse all data for statistical model building. This approach was applied by Walch et al [18], where fusing data from seven sensors lead to a total of 15,725 input variables. These input variables were then used for PLS model building to predict antibody concentration, high molecular weight species (HMWS), deoxyribonucleic acid (DNA), host cell protein (HCP), and monomer content by PLS regression.…”

“…Here, spectroscopic methods offer several advantages over on-line PAT methods, such as rapid and automated detection with no sample preparation, conditioning, or destruction at comparable equipment costs [17]. However, one optical spectroscopic method alone offers a limited selectivity for the structural integrity of proteins [13], but optical spectroscopic methods can be easily combined with other spectroscopic or non-spectroscopic sensors to measure a large variety of attributes [18,19]. Therefore, improved measurability and accuracy can be achieved by multiple sensors as compared to a single sensor [20,21].…”

Semi-automation of process analytics reduces operator effect

“…An IgG1 monoclonal antibody (mAb) was produced in CHO cell culture, harvested, and captured by Protein A affinity chromatography as described in [15]. IgG1 concentrations were determined by high-performance monolith affinity chromatography as described in [16].…”

Monitoring of product variants in biopharmaceutical downstream processing : Mechanistic and data-driven modeling approaches

“…Also, the PARAFAC algorithm has been implemented for USP monitoring [123][124][125][126][127]. While the potential of uorescence spectroscopy has been recognized for DSP monitoring [48], examples are rare [64,128,129]. For chromatography, PARAFAC has been used for handling retention shi s [130] or overlapping peaks [131].…”