BI publishes on Good Modeling Practice for industrial chromatography
August 26, 2019, Biberach/Karlsruhe, Germany. When bringing a new biopharmaceutical drug to the market, biopharma companies must ensure efficacy and safety of the drug, but also a stable and robust production process. The regulatory bodies thereby require the identification of critical process parameters and acceptable operating ranges of the process. But how can critical process parameters and acceptable operating ranges be accurately defined before running the final production process?
The problem tackled by the team around Federico Rischawy and Simon Kluters from Boehringer Ingelheim (BI) is well known amongst biopharma companies that are about to bring new products to the market. Traditionally, most companies have to establish and qualify experimental scale-down models, so that the small-scale process reacts the same way to variations as the commercial process. Already at BioProcess International Europe 2019, BI showed a promising alternative: Using mechanistic models, they could predict the behavior of commercial processes more accurately than with the experimental scale-down models. Applying GoSilico’s simulation software ChromX, BI investigated the process in silico. For a bispecific antibody purification process, BI created a predictive mechanistic process model from very few lab-scale experiments while following Good Modeling Practice (GMoP) methods. In total, seven validation runs were performed to prove the predictive power of the model and the representativeness for large scale. With the established model, BI was then able to determine the potentially critical process parameters and asses acceptable operating ranges for the at-scale process, solely by simulation with ChromX.
Co-author Tobias Hahn from GoSilico says: “Everyday work in the industry rarely allows to publish a detailed scientific paper on such an innovative approach in process development and process characterization. We are more than pleased that Boehringer Ingelheim has published such a very detailed study on how they use mechanistic modeling and simulation to increase the speed to market while reducing the experimental effort.”
The full paper is available in the journal Computers & Chemical Engineering. (https://doi.org/10.1016/j.compchemeng.2019.106532)