Cost of Goods Reduction Through Machine Learning-Assisted Protein A Resin Management in Chromatography
Resin aging is a common occurrence in chromatographic processes and generally influenced by factors such as cleaning procedure and composition of the feed stream. Two major events occur along with protein fouling, one is the loss of protein A ligand and the other is non-specific, irreversible interactions of foulants with resin particles. Both these are responsible for resin aging. As a result, the performance of the resin suffers a fall, and this can be quantified through indicators like reduction in dynamic binding capacity, increased column pressure, or peak broadening. The number of reuse cycles of a resin has a major influence on the cost per batch.
Machine learning platforms, like as PCA and PLS for Multivariate Data Analysis, are valuable proactive approaches that are simple to deploy in chromatographic process monitoring to assure process performance and robustness. Since the impact of feed variability, varied material attributes, resin age etc. on the process can only be understood by monitoring and analyzing process data it makes sense to leverage such platforms for continuous process verification.
Similarly, maximizing resin life usage, particularly for expensive resins such as Protein A, is crucial for optimizing manufacturing cost. Protein A resin degradation can occur for a variety of reasons, including pore fouling, ligand leaching, loss of resin integrity, irreversible ligand fouling, and so on. Effective process monitoring, along with efficient resin regeneration and novel technologies such as Simulated Moving Bed (SMB) and Periodic Countercurrent Chromatography (PCC), has the potential to optimize resin life use.
This webinar discusses how to assimilate structured process data, contextualize, and pretreat the data, analyze it with the correct tools, and finally apply the insights to develop the right strategies to monitor the health of the Protein A chromatography column.
What You Will Learn:
- Understand how Machine Learning techniques can help detect resin aging at an early stage
- Learn about how data analytics assist in maximizing resin life usage
- Get insights into how to monitor chromatography column health