Mechanistic Modeling Of Chromatography: Opportunities And Challenges

Process development of biologics is becoming more and more data and simulation driven. An example of this trend is the increased interest in mechanistic modeling. This approach allows you to simulate and predict chromatographic behavior and experiments in silico.

Mechanistic modeling is considered a part of smart process development, which is a collection of approaches to get better 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.

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.