Hydrophobic interaction chromatography (HIC) is a widely used separation method in biopharmaceutical downstream processing. For process development, mechanistic modeling can be used to reduce timelines by simulating protein transport and adsorption during chromatography. Accuracy of the parameters used in the model is essential for successful deployment. This work compares three isotherm parameter determination methods for a simplified linear HIC isotherm: the Parente and Wetlaufer method, the Yamamoto method, and the inverse method. These methods were tested for two proteins, using the same linear gradient elution (LGE) experiments. Accuracy of the obtained parameters was determined via cross-validation using three LGEs. Finally, the obtained parameters were tested for alternative linear gradients with varying initial and final salt concentrations. While all results were comparable, parameters obtained by the inverse method showed the greatest accuracy. This method requires high quality chromatograms, while the other methods only need retention volumes. Therefore, it is less suitable when signal quality is compromised. The Yamamoto method showed similar robustness as the inverse method while outperforming the Parente and Wetlaufer method. Therefore, the Yamamoto method is a good alternative for parameter determination. This comparison offers practical guidance for method selection for isotherm determination, thereby enabling reliable mechanistic modeling of HIC processes.

BACKGROUND: Selecting an optimal chromatography resin during biopharmaceutical downstream process development is a great challenge. This is especially the case for recombinant subunit vaccines, where product properties vary greatly and recovery often involves cell lysis, which yields a complex mixture of different host cell materials. Host cell protein (HCP) impurities may remain similar for platform processes, but their critical impact on separation efficiency is relative to specific product properties. Therefore, every process needs to be designed per product. Prior knowledge on the elution behavior of HCPs would support the identification of critical compounds. However, determining chromatographic behavior of HCPs experimentally is a time-consuming approach. RESULTS: In this work, we leverage quantitative structure–property relationship (QSPR) models calibrated with retention data of 13 commercial proteins, collected at pH 7, 8, 9 and 10 to predict the anion-exchange retention of Escherichia coli HCPs. These models use features calculated from the molecular structure to describe protein behavior, like chromatographic retention. A multilinear regression model containing two features (isoelectric point and sum of negative surface electrostatics) was able to predict the retention times of 288 HCPs accurately (error ≤ 5%). Moreover, we identified the key attributes missing in the training dataset, which is important to increase model performance in the future. CONCLUSION: This work showcases how chromatographic data obtained using commercial proteins can be translated to a clarified E. coli lysate to accelerate chromatography resin selection for new products.

Protein-based biopharmaceuticals require high purity before final formulation to ensure product safety, making process development time consuming. Implementation of computational approaches at the initial stages of process development offers a significant reduction in development efforts. By preselecting process conditions, experimental screening can be limited to only a subset. One such computational selection approach is the application of Quantitative Structure Property Relationship (QSPR) models that describe the properties exploited during purification. This work presents a novel open-source Python tool capable of extracting a range of features from protein 3D models on a local computer allowing total transparency of the calculations. As open-source tool, it also impacts initial investments in constructing a QSPR workflow for protein property prediction for third parties, making it widely applicable within the field of bioprocess development. The focus of current calculated molecular features is projection onto the protein surface by constructing surface grid representations. Linear regression models were trained with the calculated features to predict chromatographic retention times/volumes. Model validation shows a high accuracy for anion and cation exchange chromatography data (cross-validated R² of 0.87 and 0.95). Hence, these models demonstrate the potential of the use of QSPR to accelerate process design.