Targeted purification development enabled by computational biophysical modeling

Abstract

Chromatographic and non-chromatographic purification of biopharmaceuticals depend on the interactions between protein molecules and a solid-liquid interface. These interactions are dominated by the protein-surface properties, which are a function of protein sequence, structure, and dynamics. In addition, protein-surface properties are critical for in vivo recognition and activation, thus, purification strategies should strive to preserve structural integrity and retain desired pharmacological efficacy. Other factors such as surface diffusion, pore diffusion, and film mass transfer can impact chromatographic separation and resin design. The key factors that impact non-chromatographic separations (e.g., solubility, ligand affinity, charges and hydrophobic clusters, and molecular dynamics) are readily amenable to computational modeling and can enhance the understanding of protein chromatographic. Previously published studies have used computational methods such as quantitative structure-activity relationship (QSAR) or quantitative structure-property relationship (QSPR) to identify and rank order affinity ligands based on their potential to effectively bind and separate a desired biopharmaceutical from host cell protein (HCP) and other impurities. The challenge in the application of such an approach is to discern key yet subtle differences in ligands and proteins that influence biologics purification. Using a relatively small molecular weight protein (insulin), this research overcame limitations of previous modeling efforts by utilizing atomic level detail for the modeling of protein-ligand interactions, effectively leveraging and extending previous research on drug target discovery. These principles were applied to the purification of different commercially available insulin variants. The ability of these computational models to correlate directionally with empirical observation is demonstrated for several insulin systems over a range of purification challenges including resolution of subtle product variants (amino acid misincorporations). Broader application of this methodology in bioprocess development may enhance and speed the development of a robust purification platform.

Cation exchange as a single polishing step for conjugated peptides

“…This data indicates that computational biophysical modeling could be a valuable tool during initial chromatographic process development to direct modality selection prior to broad screening, as has been previously shown with other peptides. 23 Summary of the biophysical properties that were compiled for the target peptides and their respective impurities.…”

Biopharmaceutical Informatics: supporting biologic drug development via molecular modelling and informatics

“…Chemical degradation of proteins not only represents problems in formulation and pharmacological properties of biologic drug products, but it is also associated with several ageing‐related diseases in humans . Although this review has focused on chemical degradation liabilities, similar computational approaches are also being used to study other challenges faced in development of biologic drugs including aggregation, purification and high viscosity . Computation can also be used to understand and estimate pharmacokinetics, pharmacodynamics and immunogenicity of biologics in vivo .…”

Reduce, Reuse and Recycle in Protein Chromatography: Development of an Affinity Adsorbent from Waste Paper and Its Application for the Purification of Proteases from Fish By-Products

“…However, protein purification cost remains high, and constitutes a substantial proportion of the overall production cost [2,3]. Purification methods that are designed based on specific, effective and robust materials are expected to guide the future of the protein purification area [6,7]. Affinity chromatography is the most specialized method for the effective purification of proteins, compared to other separation methods [2][3][4][8][9][10][11][12][13].…”