Optimization of a polishing chromatography step to robustly remove selected impurities can be a challenging task during the downstream process development of biopharmaceuticals. Since separation of impurities for these steps is driven by varying the salt concentration and pH of the mobile phase, the potential optimization space can be relatively large. Purely experimental development approaches focus the experimental work to obtain a satisfying result but they should not be expected to result in a process that is optimal in terms of productivity and robustness. This work seeks to employ mechanistic models to streamline the development process by using an evolutionary artificial intelligence algorithm to design optimal processes in silico after obtaining model parameters experimentally. A case study is presented for a hydrophobic interaction chromatography (HIC) step designed to separate monomer from aggregate. A mechanistic model was developed using the equilibrium-dispersive model along with a novel HIC isotherm to describe the binding kinetics of monomer and dimer. After fitting and validating the model, evolutionary AI was employed to design the optimal chromatographic sequence to maximize yield under purity and processing time constraints. The AI managed to make significant improvements in yield, processing time, and buffer consumption compared to the platform process. After designing the optimized process, its robustness was compared to the platform process using the Sobol method. The analysis shows that both processes are equally robust in terms of aggregate clearance, but the AI-designed process was significantly more robust in terms of yield. These results demonstrate the effectiveness of in silico process design and its advantage over the traditional, purely experimental development approaches currently used in the industry.

In silico optimization and robustness analysis of a hydrophobic interaction chromatography step for the purification of a monoclonal antibody

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.