The use of predictive models to develop chromatography-based purification processes.

Area of Science:

• Biopharmaceutical Downstream Processing
• Chemical Engineering
• Computational Modeling

Background:

• Chromatography is essential for biopharmaceutical purification, separating target products from impurities based on molecular properties.
• Process development involves extensive parameter testing (resin, ligand, pH, conductivity, gradients), leading to high time and cost demands.
• Current high-throughput methods still face limitations in managing the vast experimental space.

Purpose of the Study:

• To review the benefits and challenges of chromatography modeling in biopharmaceutical downstream processing.
• To guide researchers in establishing effective chromatography modeling workflows.
• To highlight how in silico approaches can streamline process development and enhance understanding.

Main Methods:

• Discussing the experimental characterization of chromatography systems (e.g., column porosity).
• Detailing the setup and calibration of chromatography models, including data-driven and hybrid approaches.
• Emphasizing the importance of model cross-validation and verification.

Main Results:

• Chromatography modeling predicts optimal conditions for high purity and recovery, significantly reducing empirical testing.
• In silico prediction provides in-depth process understanding, aligning with regulatory recommendations.
• Modeling overcomes the unmanageable number of experimental conditions encountered in traditional process development.

Conclusions:

• Chromatography modeling is a cost-effective and efficient strategy for biopharmaceutical downstream process development.
• Successful implementation requires careful experimental characterization and robust model validation.
• This approach supports researchers in optimizing purification processes and gaining deeper insights.