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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 semi-automation, 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.

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 model-based approach for scaling up chromatographic capture step was developed. The purification of human basic fibroblast growth factor protein 2 (FGF2) from an E. coli homogenate on a cation exchange resin was selected as a case study. Non-ideal effects accompanying the capture operation were examined, including: reduction in the protein diffusivity in the presence of the homogenate, competitive adsorption between FGF2 and undefined impurities, and flow behavior in external column volumes. The viscosity of the homogenate was measured as a function of dilution degree and shear stress, and its contribution to the diffusivity reduction was quantified. A dynamic model was formulated which accounted for underlying kinetic and thermodynamic dependencies. The model parameters were determined for a lab scale system using a small 2-mL column. The model was successfully used to scale up the capture operation from the lab scale column to a preparative bench scale column of about 1 L volume.

Basic fibroblast growth factor (FGF-2) is a labile protein which degrades over time or forms aggregates. A purification process combining a cation ion exchange chromatography (CIEX) capture step with a polishing step by hydrophobic interaction chromatography (HIC) efficiently depleted process-related impurities such as host cell proteins (HCPs), dsDNA and endotoxins from the clarified E. coli homogenate containing the overexpressed FGF-2. Fresh homogenates did not contain verifiable amounts of process-related impurities such as degraded product variants. However, the storage of the capture eluates for longer than two weeks at 4 °C led to product degradation up to 49%. Mass spectrometry showed that the truncated FGF-2 was devoid of the nine N-terminal amino acids. Removal of such product-related impurities is included in state of the art purifications because these byproducts bear the risk of having different biological activities. Therefore, the established purification process has been evaluated for its potential to deplete such truncated FGF-2 variants. The HIC polishing step using Toyopearl® Hexyl-650C with a linear salt gradient over 4 column volumes from 1.65 M trisodium citrate to 0 M separated this variant from the full-length FGF-2. The truncated form eluted before the full length product due to the loss of 9 hydrophobic amino acids. The developed ion exchange HPLC method enabled us to quantify the truncated variant and full-length FGF-2. A purity of > 98% full-length FGF-2 with a step yield of up 86% is achieved. As long as the degree of truncation does not exceed 25% of the stored eluates, the purification process delivers the product in the desired quality of > 98% full-length FGF-2, HCP content below 10 ppm/dose, dsDNA content below 10 ng/mL/dose and endotoxin level below 400 EU/dose. This purification sequence allowed the production of FGF-2 which meets distinctive quality standards defined by regulatory bodies with respect to the depletion of both - process- and product-related impurities.

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 semi-automation, 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. Electronic supplementary material The online version of this article (10.1007/s00449-019-02254-y) contains supplementary material, which is available to authorized users.

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.