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An Automated MS Data Workflow Enabling Targeted, Site-Specific N-Glycosylation Monitoring of Biopharmaceuticals
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Pharmaceuticals
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Predictive Quality Analytics
- Root Cause Analysis & Diagnosis
Services
- Data Science Services
The Challenge
The biopharmaceutical industry is constantly seeking strategies to improve the quality and effectiveness of their products. One such strategy is the characterization and control of glycosylation patterns. However, the current gold standard for N-glycan mapping, ultrahigh pressure liquid chromatography (UHPLC) with a fluorescence-based detection method, presents several limitations. It loses site-specific information for molecules with multiple glycosylation sites, has issues with co-elution and new peak appearance, and requires laborious data processing, analysis, and reporting. The need for a more robust and high-throughput glycan analysis methodology led to the exploration of LC-MS glycopeptide mapping.
About The Customer
The customer in this case study is a biopharmaceutical developer, likely a large-scale company given the mention of sales of antibody-based therapeutics totaling around $100 billion in the U.S. alone. The company is involved in the development of monoclonal antibodies (mAbs), which are currently the largest and fastest-growing class of biopharmaceutical. These mAbs undergo post-translational modifications (PTMs), including N-linked glycosylation, which modulates antibody activity and can affect a therapeutic antibody’s immunogenicity and pharmacokinetics. The company is seeking to understand how cellular conditions and biotechnical process parameters modulate N-glycosylation to establish robust manufacturing processes and safer therapeutics.
The Solution
The solution involved streamlining the data processing workflow of LC-MS glycopeptide mapping by automating the processing steps using Genedata Expressionist. This software allows for the detection of each targeted glycopeptide, automatically reports its mass-to-charge ratio and retention time, directly groups isomers (including newly discovered ones), and can visualize data at any point within the workflow. Users can also create “approved” workflows by blocking edits to selected parameters or the entire workflow, simplifying usage in routine monitoring and allowing the workflows to be used by non-experts. This method provides site-specific monitoring with no co-elution issues, identifies new peaks directly, and is suitable for complex molecules.
Operational Impact
Quantitative Benefit
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