Case Studies An Automated MS Data Workflow Enabling Targeted, Site-Specific N-Glycosylation Monitoring of Biopharmaceuticals

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	An Automated MS Data Workflow Enabling Targeted, Site-Specific N-Glycosylation Monitoring of Biopharmaceuticals

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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 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.

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.

Impact #1	The Genedata Expressionist automated workflow provides site-specific monitoring with no co-elution issues using LC-MS.
Impact #2	The method is suitable for complex molecules and identifies new peaks directly.
Impact #3	The method is also suitable for monitoring O-glycosylation.

Impact #1

The Genedata Expressionist automated workflow provides site-specific monitoring with no co-elution issues using LC-MS.

Impact #2

The method is suitable for complex molecules and identifies new peaks directly.

Impact #3

The method is also suitable for monitoring O-glycosylation.

Benefit #1	Increased throughput from 20 to 80 samples per week.
Benefit #2	Cut data processing time for a typical N-glycan experiment from weeks to minutes.

Benefit #1

Increased throughput from 20 to 80 samples per week.

Benefit #2

Cut data processing time for a typical N-glycan experiment from weeks to minutes.

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Overview

An Automated MS Data Workflow Enabling Targeted, Site-Specific N-Glycosylation Monitoring of Biopharmaceuticals

Operational Impact

Quantitative Benefit