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 two-step purification process for human basic fibroblast growth factor (FGF-2) from clarified E. coli homogenate has been developed in which the impurity level after the second step is below the limit of quantification. Endotoxin content is cleared to 0.02 EU/μg FGF-2 and the overall yield is 67%. The performance of the cation exchanger Carboxymethyl-Sepharose Fast Flow (CM-SFF) was compared to the affinity resin Heparin-SFF regarding the impurity profile and product quality in the elution peak. The CM-SFF eluate was further purified using hydrophobic interaction resin Toyopearl-Hexyl-650C. The relative amounts of target product, host cell proteins (HCPs), dsDNA, endotoxin, monomer content, and high molecular weight impurities differed along the elution peak depending on the applied method. The bioactive monomer (>99%) was obtained with a yield of 48% for CM-SFF and 68% for Heparin-SFF. A half-load reduction in CM-SFF increased the yield up to 67% without deterioration of the impurity content. Assuming a dose of 400 μg FGF-2, endotoxin was reduced to 188 EU/dose, dsDNA <10 ng/dose, and HCP <2 ppm/dose using the cation exchanger. In the pooled eluate fractions, dsDNA was removed 4-fold (291 ng/mL) and endotoxin 14-fold (0.47 EU/μg FGF-2) more efficiently by CM-SFF than by affinity chromatography. In contrast, HCP clearance was 3-fold (13 ppm) more efficient with Heparin-SFF than CM-SFF. In contrast to process monitoring by UV or SDS-PAGE, this characterization is the basis for a Process Analytical Technology attempt when correlated with online monitored signals, as it enables knowledge-based pooling according to defined quality criteria.

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The aim of this study was to semi-automate process analytics for the quantification of common impurities in downstream processing such as host cell DNA, host cell proteins and endotoxins using a commercial liquid handling station. By semiautomation, the work load to fully analyze the elution peak of a purification run was reduced by at least 2.41 h. The relative standard deviation of results among different operators over a time span of up to 6 months was at the best reduced by half, e.g. from 13.7 to 7.1% in dsDNA analysis. Automation did not improve the reproducibility of results produced by one operator but released time for data evaluation and interpretation or planning of experiments. Overall, semi-automation of process analytics reduced operator-specific influence on test results. Such robust and reproducible analytics is fundamental to establish process analytical technology and get downstream processing ready for Quality by Design approaches.

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