Mechanistic Modeling for Enhanced Chromatographic Productivity

This project will address key gaps in downstream process development space by providing a mechanistic model and supporting laboratory workflows for accurately predicting challenging and complex chromatography applications.

Project status

100% Completed

Industry Need

Development of preparative chromatography, an essential process step in most biological pharmaceuticals, is one industry element that is ready for digitization. Process understanding is at point where in silico mechanistic models of many types of chromatographic separations can be made. The application of these mechanistic models to biopharmaceutical development has been limited to only few institutions with highly skilled expertise. The challenges in generating a mechanistic model with enough predictive power to be used for medicinal products or in a GMP setting can prohibitive.

Solution

The significance of this project is to directly address some of the challenges preventing a wider adoption of mechanistic chromatography modeling in industry.

Outputs/Deliverables

The intention of the team is to provide the raw data and models so that they can be consumed directly by other NIIMBL members and the interested public. The team is putting together guidance for different approaches to mechanistic modeling that would take into account technical capabilities and intended use of the model. They are also planning a series of publications that will go into further detail of the most significant findings in the collaboration.

Impacts

Models for non-linear multicomponent systems and multimodal models for product- and process- related impurities.

Reduction in downstream process development time through a mechanistic modeling platform that can accurately account for product heterogeneity, multicomponent effects and multiple modes of interaction.

Publications

Altern, S. H., Lyall, J. Y., Welsh, J. P., Burgess, S., Kumar, V., Williams, C., Lenhoff, A. M., & Cramer, S. M. (2024). High-throughput in silico workflow for optimization and characterization of multimodal chromatographic processes. Biotechnology Progress. https://doi.org/10.1002/btpr.3483

Altern, S. H., Welsh, J. P., Lyall, J. Y., Kocot, A. J., Burgess, S., Kumar, V., Williams, C., Lenhoff, A. M., & Cramer, S. M. (2023). Isotherm model discrimination for multimodal chromatography using mechanistic models derived from high-throughput batch isotherm data. Journal of Chromatography A, 1693. https://doi.org/10.1016/j.chroma.2023.463878