Fluorescence based real time monitoring of fouling in process chromatography

L-Arginine sulfate reduces irreversible protein binding in immobilized metal affinity chromatography

Immobilized metal affinity chromatography (IMAC) is a powerful technique for capture and purification of relevant biopharmaceuticals in complex biological matrices. However, protein recovery can be drastically compromised due to surface induced spreading and unfolding of the analyte, leading to fouling of the stationary phase. Here, we report on the kinetics of irreversible adsorption of a protease on an IMAC resin in a time span ranging from minutes to several hours. This trend correlated with the thermal data measured by nano differential scanning calorimetry, and showed a time-dependent change in protein unfolding temperature. Our results highlight that 'soft' proteins show a strong time dependent increase in irreversible adsorption. Furthermore, commonly used co-solvents for preservation of the native protein conformation are tested for their ability to reduce fouling. Thermal data suggests that the amino acid l-arginine is beneficial in preventing unfolding, which was confirmed in batch adsorption experiments. The choice of counter-ions has to be considered when using this amino acid. These results show that l-arginine sulfate decelerates the irreversible adsorption kinetics of proteins on the IMAC stationary phase to a greater extent than l-arginine chloride.

Exploring features in chromatographic profiles as a tool for monitoring column performance

In the biopharmaceutical industry, chromatography resins have a finite number of uses before they start to age and degrade, typically due to losses of ligand integrity and/or density. The "health" of a column is predicted and validated by running multiple cycles on representative scale-down models and can be followed by real-time on-going validation during commercial production. Principal Component Analysis (PCA), Partial Least Square (PLS), Similarity Scores and Single One Point-MultiParameter Technique (SOP-MPT) along with machine learning principles were applied to explore the hypothesis that there is predictive capability of latent variables in chromatography absorbance profiles for process performance (step yield) and product quality (aggregates, fragments, host cell proteins (HCP) and DNA, and Protein A ligand). The first stage of this study is described in this paper: a MabSelect SuRe™ chromatography column was cycled with a method to establish the "normal" baseline for process performance and product quality, followed by runs using a harsher NaOH Cleaning in Place (CIP) procedure (with a higher NaOH concentration than that recommended by the vendor) to accelerate resin degradation. The different mathematical analytical tools correlated with resin degradation of the column (reflected in decreasing step yield and binding capacity with increasing running cycle), specifically when using the Wash, Elution and Strip phases of the chromatography method. Monomer, HCP and DNA content were not significantly impacted and therefore a correlation with product quality was inconsequential. Importantly, this work shows proof-of-concept that while more traditional methods of measuring resin integrity such as the height equivalent to a theoretical place (HETP) and Asymmetry (As) measurements could not detect changes in the integrity of the resin, PCA, PLS, Similarity Scores and SOP-MPT (to a lesser extent) applied to the absorbance data were capable of anticipating issues in the chromatography bed by identifying atypical outcomes.