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.

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.

Approach

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

Impacts

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.

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

Value Statement/Outcomes

By implementing high-throughput robotics combined with mechanistic isotherm modeling for antibody purification, an organization will reduce process development costs, minimizing experimental burden and resin consumption, this approach will accelerate timelines for chromatography optimization through predictive simulations and reverse curve fitting and enable robust, scalable purification strategies for complex biologics ensuring consistent product quality and regulatory compliance.

Outputs/Deliverables

Isotherm fitting to a broad high throughput screening of antibodies on ion exchange and mixed mode resins

Side-by-side comparison of isotherms, both empirical and mechanistic, for a variety of antibody-resin systems

Modeling of industrial case studies for column chromatography that presents specific challenges

Optimization of reverse curve fitting through the selection of the most informative data and a reduction of parameter space through observed correlations

Direct quantitative bridging between high throughput batch purification and column purification using mechanistic isotherms

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

Welsh, J., Altern, S., Lyall, J., Burgess, S., Rauscher, M., Lenhogg, A., Cramer, S., Williams. C. (2024) Coupling High-throughput and modeling approaches to streamline early-stage Process for biologics.https://doi.org/10.1002/btpr.3523

Posters

Williams, C., Cramer, S., Welsh, J., Lenhoff, A., Lyall, J., Kumar, V., Altern, S. H., & Peyser, J., Mechanistic Modeling for Enhanced Chromatographic Productivity, NIIMBL National Meeting, Virtual, July 15, 2021.

Presentations

Altern, S. H. &. C., S., Bridging high-throughput screening and mechanistic modeling for the development of multimodal chromatographic processes, ACS National Meeting, Troy, NY, August 17, 2020.

Williams, C., Cramer, S., Lenhoff, A., Welsh, J., Gillespie, C., Lyall, J., & Kuriyel, R., Mechanistic Modeling for Enhanced Chromatographic Productivity, NIIMBL National Meeting, Washington, DC, June 28, 2019.

Williams, C., Lenhoff, A., Cramer, S., Welsh, J., Lyall, J., Kumar, V., & Altern, S. H., Mechanistic Modeling for Enhanced Chromatographic Productivity, NIIMBL Members Forum, Virtual, April 23, 2020.