High throughput chromatography strategies for potential use in the formal process characterization of a monoclonal antibody.

High throughput experimental strategies are central to the rapid optimization of biologics purification processes. In this work, we extend common high throughput technologies towards the characterization of a multi-column chromatography process for a monoclonal antibody (mAb). Scale-down strategies were first evaluated by comparing breakthrough, retention, and performance (yields and clearance of aggregates and host cell protein) across miniature and lab scale columns. The process operating space was then evaluated using several integrated formats, with batch experimentation to define process testing ranges, miniature columns to evaluate the operating space, and comparison to traditional scale columns to establish scale-up correlations and verify the determined operating space. When compared to an independent characterization study at traditional lab column scale, the high throughput approach identified the same control parameters and similar process sensitivity. Importantly, the high throughput approach significantly decreased time and material needs while improving prediction robustness. Miniature columns and manufacturing scale centerpoint data comparisons support the validity of this approach, making the high throughput strategy an attractive and appropriate scale-down tool for the formal characterization of biotherapeutic processes in the future if regulatory acceptance of the miniature column data can be achieved. Biotechnol. Bioeng. 2016;113: 1273-1283. © 2015 Wiley Periodicals, Inc.

Targeted purification development enabled by computational biophysical modeling.

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.

Resolution of heterogeneous charged antibody aggregates via multimodal chromatography: a comparison to conventional approaches.

Clearance of aggregates during protein purification is increasingly paramount as protein aggregates represent one of the major impurities in biopharmaceutical products. Aggregates, especially dimer species, represent a significant challenge for purification processing since aggregate separation coupled with high purity protein recovery can be difficult to accomplish. Biochemical characterization of the aggregate species from the hydrophobic interaction and cation exchange chromatography elution peaks revealed two different charged populations, i.e. heterogeneous charged aggregates, which led to further challenges for chromatographic removal. This paper compares multimodal versus conventional cation exchange or hydrophobic chromatography methodologies to remove heterogeneous aggregates. A full, mixed level factorial design of experiment strategy together with high throughput experimentation was employed to rapidly evaluate chromatographic parameters such as pH, conductivity, and loading. A variety of operating conditions were identified for the multimodal chromatography step, which lead to effective removal of two different charged populations of aggregate species. This multimodal chromatography step was incorporated into a monoclonal antibody purification process and successfully implemented at commercial manufacturing scale.