We are leading the way in developing integrated online sampling and Process Analytical Technology (PAT) workflows for quick monitoring of critical quality attributes (CQA) to produce advanced glycosylated biologics (mAbs). This collaborative project involves Rutgers University, University of Delaware, Agilent Technologies, and Endress+Hauser. The creation of a PAT called the "N-GLYcanyzer" is the central component of our process. Using an integrated liquid chromatography (LC) system and online sequential flow injection analysis (FIA), the N-GLYcanyzer facilitates sophisticated CQA monitoring across a range of bioreactor operating modes. Robust workflow integration with cutting-edge bioanalytical technology from Agilent Infinity II Multisampler and InfinityLab Online LC Solutions makes this possible. The N-GLYcanyzer system has proven through our proof-of-concept studies to be able to adapt traditional (2-AB) and rapid (IPC) offline labeling methods for mAb N-glycosylation analysis into an online PAT platform. This allows for real-time monitoring from automated sample draw from bioreactor to glycoform LC chromatogram analysis in remarkably short times (3 hours and 30 minutes, respectively). We have also been working to improve the workflow's smooth integration by making updates to the python code that controls it. These updates include new parameters (such as dead volumes, tube/column sizing properties, and elution volumes) that guarantee the best possible reagent handling and sample handoff. To automate the complete workflow for simultaneous glycan and amino acid studies, we have also improved parameters for sample transport and sample preparation for online spent media amino acid analysis. Additionally, we have enabled proper electrical communication between the FIA and HPLC machines. Our goal is to provide strong integration between in-line Raman flow sensor-based PAT systems and multi-attribute liquid chromatography, enabling the simultaneous monitoring of several CQAs. This will be facilitated by robust process control and online model validation. In the end, our approach has the potential to improve the biologics manufacturing process by enabling the creation of high-quality biotherapeutics through automated PAT-based control measures.

Learning Objectives: 

1. Develop understanding of the importance of glycans. 

2. Summarize the importance of glycosylation and amino acid estimation in biopharma industries. 

3. Review the importance of PAT tools.