Real‐time monitoring and model‐based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2

Abstract

Process analytical technology combines understanding and control of the process with real‐time monitoring of critical quality and performance attributes. The goal is to ensure the quality of the final product. Currently, chromatographic processes in biopharmaceutical production are predominantly monitored with UV/Vis absorbance and a direct correlation with purity and quantity is limited. In this study, a chromatographic workstation was equipped with additional online sensors, such as multi‐angle light scattering, refractive index, attenuated total reflection Fourier‐transform infrared, and fluorescence spectroscopy. Models to predict quantity, host cell proteins (HCP), and double‐stranded DNA (dsDNA) content simultaneously were developed and exemplified by a cation exchange capture step for fibroblast growth factor 2 expressed in Escherichia coliOnline data and corresponding offline data for product quantity and co‐eluting impurities, such as dsDNA and HCP, were analyzed using boosted structured additive regression. Different sensor combinations were used to achieve the best prediction performance for each quality attribute. Quantity can be adequately predicted by applying a small predictor set of the typical chromatographic workstation sensor signals with a test error of 0.85 mg/ml (range in training data: 0.1–28 mg/ml). For HCP and dsDNA additional fluorescence and/or attenuated total reflection Fourier‐transform infrared spectral information was important to achieve prediction errors of 200 (2–6579 ppm) and 340 ppm (8–3773 ppm), respectively.

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

“…Sauer et al [19] used the same experimental setup as Walch et al [18] but chose to use the statistical framework of STructured Additive Regression (STAR), which provides means to include a wide range of nonlinear effects into model building, e.g., by including bivariate interaction terms [80]. However, the authors chose to exclude bivariate interaction terms for all spectroscopy sensors due to the required computational power.…”

“…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].…”

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].…”

Fiberoptic-Coupled Spectrofluorometer with Array Detection as a Process Analytical Chemistry Tool for Continuous Flow Monitoring of Fluoroquinolone Antibiotics

“…PAT has many applications in the pharmaceutical and antibiotics manufacturing [12][13][14][15][16][17][18][19][20][21][22][23][24][25], chemical [26,27], petrochemical [28], and food industries [29][30][31][32][33]. In addition, there are many recent progress in realtime monitoring of cultivations in bioreactors and cell culture process [34][35][36][37][38][39][40][41], fermentation [42,43] and biological process [44], and electrochemical [45,46] and protein purification [47]. Using PAT enables us to get a deeper understanding of the process.…”