Mechanistic Modeling Of Chromatography: Opportunities And Challenges

As process development of biologics becomes more and more data and simulation driven (Fig 1), interest in mechanistic modeling is also increasing. This approach allows you to simulate and predict chromatographic behavior and experiments in silico.

Mechanistic modeling is part of smart process development, which is a collection of approaches to improve process outcomes and speed up process development. Together with statistical models based on multivariate data analysis (MVDA) such as design of experiments (DoE), it can be a powerful tool to save time and create more robust processes, which is the goal of the overarching QbD framework.

The approach can be a shortcut to more robust process outcomes, but it is in no way a straight path. This article outlines both the current opportunities and challenges for using mechanistic modeling for process development of chromatography steps.