Behavior of Water Near Multimodal Chromatography Ligands and Its Consequences for Modulating Protein-Ligand Interactions

DOE-based process optimization for development of efficient methods for purification of recombinant hepatitis B surface antigen from Pichia pastoris feedstock using Capto adhere resin

Background

The multimodal chromatography resins, such as Capto adhere, are considered good candidates to be utilized in downstream processing due to their high capacity and selectivity; however, their multimodal interactions lead to an intricacy in the adsorption-desorption patterns and systematic characterization of conditions for process steps is necessary.

Methods

Capto adhere, a strong ion exchanger with multimodal functionality, was used in this study for the final aim of recombinant hepatitis B surface antigen (rHBsAg) purification from Pichia pastoris (P. pastoris) industrial feedstock. Optimization of various parameters was done using the design of experiments (DOE) approach to determine the best binding and non-binding conditions.

Results

Maximum rHBsAg binding on Capto adhere occurred in 20 mM sodium acetate, pH 4.5, and a binding capacity of about 0.75 mg/ml was achieved, which was much higher than rHBsAg binding capacity of other resins reported so far. In elution optimization investigations, it was revealed that 1 M arginine (buffered in 50 mM sodium phosphate, pH 6.5) was the most efficient eluting agent. The binding and elution optimal conditions were utilized for further purification of rHBsAg from P. pastoris industrial feedstock in bind-elute mode, and the recovery and purity of the obtained rHBsAg were about 60% and 100%, respectively. Following optimization in the flow-through purification mode, the target protein recovery was significantly increased (up to 97%) and the target protein purity of more than 95% was achievable. SEC-HPLC analysis showed that the obtained retention times for the purified rHBsAg were similar to those reported previously.

Conclusions

These results suggest that Capto adhere under such optimized conditions can be considered as a good candidate for efficient purification of rHBsAg from P. pastoris industrial feedstock in downstream processing.

Antibody sequence-based prediction of pH gradient elution in multimodal chromatography

Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its capacity to selectively capture and separate target molecules. However, the optimization of chromatography parameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue, efficient predictive tools are essential for the development and optimization of multimodal chromatography processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multimodal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies, including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312 antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key structural features of the multimodal antibody interaction, which were correlated with the elution behavior, emphasizing the antibody variable region. The results show that our methodology can predict pH gradient elution for a diverse range of antibodies and antibody formats, with a test set R² of 0.898. The developed model can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial and error during process optimization. Furthermore, the model holds the potential to enable an in silico manufacturability assessment by screening target antibodies that adhere to standardized purification conditions. In conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purification in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process development while increasing process understanding.