Newsletter | June 9, 2022

06.09.22 -- Mechanistic Chromatography Modeling For Smarter Process Development

What Is A Digital Twin?

Digital twins are commonly understood as virtual representations of real-world processes. They can be built using mechanistic or statistical models. Statistical approaches such as Big Data, machine learning, and artificial intelligence utilize statistics to predict trends and patterns. Mechanistic models are, instead, based on physical and biochemical principles.

Mechanistic Modeling: A Quick-Start Guide

Mechanistic chromatography models make it possible to create computer simulations of chromatograms. The simulations are based on physiochemical phenomena known to occur in chromatography. The benefit of a mechanistic model? You can test thousands of purification options in a few hours.

Opportunities And Challenges With Mechanistic Modeling

Mechanistic modeling complements other process development approaches. Compared with HTPD and DoE, it allows better understanding of both the entire process and the parameters that can influence your process. And it requires fewer experiments. As a result, you can get more from your process development — more speed and more reliable results. But there’s a learning curve.

Mechanistic Modeling For Downstream Processing: Digital Twins Are Here To Stay

Expensive and time-consuming laboratory experiments, iterative empirical optimization, and even statistical methods alone are not the answers to the challenges of the future. Many global biopharma companies are working on establishing digital twins of their upstream or downstream processes.

Understanding Fluid Dynamics Within A Chromatography Column

Mechanistic chromatography models are based on mathematical descriptions of the occurring physical and biochemical phenomena in a column. See how fluid flow effects within a chromatography column are used by mechanistic models.