Case Studies Automating LC-UV-MS-Based Analytics in Therapeutic Oligonucleotide Process Development

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	Automating LC-UV-MS-Based Analytics in Therapeutic Oligonucleotide Process Development

Automating LC-UV-MS-Based Analytics in Therapeutic Oligonucleotide Process Development

Technology Category	Analytics & Modeling - Data-as-a-Service 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	Roche, a leading biopharma company, was facing challenges in the analytical process of developing oligonucleotide-based drugs. The process involved the use of high-performance liquid chromatography (HPLC) and mass spectrometry (MS) to determine the number, amount, and identity of impurities in crude oligonucleotide samples. However, the number and complexity of samples combined with the laborious nature of the data analysis generated a significant bottleneck in the analytical process. The company was struggling to meet high analytical throughput demands due to the laborious manual operations for impurity quantification and any requested deeper analysis. The samples often contained many impurities, translating into the analytical challenge of assessing 20–30 peaks arising from 50–100 closely coeluting impurities. The large number of manual operations in the previous quantification process often required 5–6 hours of an analyst’s time, limiting the ability of MS experts to perform required deeper characterization in a timely manner. Read More
About Customer	Roche is a leading provider of in-vitro diagnostics and a global supplier of transformative innovative solutions across major disease areas. The company is currently developing several oligonucleotide-based drugs to address a range of diseases. Their lab provides analytical support to the process chemistry department, which is responsible for optimizing oligonucleotide synthesis processes. They combine the information-rich data obtained from high-performance liquid chromatography (HPLC) and mass spectrometry (MS) to determine the number, amount, and identity of impurities in crude oligonucleotide samples. These insights are key to optimizing the oligonucleotide solid-phase synthesis process. Read More
Solution	Roche collaborated with Genedata to develop and implement a tailored MS data processing workflow that increased their throughput and the level of characterization while reducing the burden on their MS experts. Genedata Expressionist® integrated seamlessly into Roche's analytical process and enabled them to directly import and process raw UV and MS data and combine these data streams for each analysis. The workflow-based approach enabled them to automate routine data processing, analysis, and reporting while still allowing their expert users to bring their expertise to bear on the critical stages of the analysis, such as identifying unexpected impurities. Automation of all routine data processing steps—such as blank subtraction, spectrum averaging, and MS quantification— enabled them to streamline and standardize their MS analyses, increasing both throughput and result quality. The data workflow presented here was created and optimized for a specific product synthesis process. However, the inherent flexibility of Genedata Expressionist enables them to use this workflow as a template that can easily be adapted for processing other molecules. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Data-as-a-Service

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

Roche, a leading biopharma company, was facing challenges in the analytical process of developing oligonucleotide-based drugs. The process involved the use of high-performance liquid chromatography (HPLC) and mass spectrometry (MS) to determine the number, amount, and identity of impurities in crude oligonucleotide samples. However, the number and complexity of samples combined with the laborious nature of the data analysis generated a significant bottleneck in the analytical process. The company was struggling to meet high analytical throughput demands due to the laborious manual operations for impurity quantification and any requested deeper analysis. The samples often contained many impurities, translating into the analytical challenge of assessing 20–30 peaks arising from 50–100 closely coeluting impurities. The large number of manual operations in the previous quantification process often required 5–6 hours of an analyst’s time, limiting the ability of MS experts to perform required deeper characterization in a timely manner.

About Customer

Roche is a leading provider of in-vitro diagnostics and a global supplier of transformative innovative solutions across major disease areas. The company is currently developing several oligonucleotide-based drugs to address a range of diseases. Their lab provides analytical support to the process chemistry department, which is responsible for optimizing oligonucleotide synthesis processes. They combine the information-rich data obtained from high-performance liquid chromatography (HPLC) and mass spectrometry (MS) to determine the number, amount, and identity of impurities in crude oligonucleotide samples. These insights are key to optimizing the oligonucleotide solid-phase synthesis process.

Solution

Roche collaborated with Genedata to develop and implement a tailored MS data processing workflow that increased their throughput and the level of characterization while reducing the burden on their MS experts. Genedata Expressionist® integrated seamlessly into Roche's analytical process and enabled them to directly import and process raw UV and MS data and combine these data streams for each analysis. The workflow-based approach enabled them to automate routine data processing, analysis, and reporting while still allowing their expert users to bring their expertise to bear on the critical stages of the analysis, such as identifying unexpected impurities. Automation of all routine data processing steps—such as blank subtraction, spectrum averaging, and MS quantification— enabled them to streamline and standardize their MS analyses, increasing both throughput and result quality. The data workflow presented here was created and optimized for a specific product synthesis process. However, the inherent flexibility of Genedata Expressionist enables them to use this workflow as a template that can easily be adapted for processing other molecules.

Impact #1	Genedata Expressionist automated data processing increased analytical throughput by shortening the time required for analysis from 5–6 hours to just 30 minutes in the case of very complex samples.
Impact #2	By eliminating analytical bottlenecks, Roche was able to keep pace with the large number of samples generated by their process chemists systematically optimizing each parameter and step.
Impact #3	The additional expert analytical capacity provided by automation of routine data processing tasks enabled Roche to better characterize unexpected impurities and provide more detailed reports to stakeholders without impacting the overall laboratory throughput.

Impact #1

Genedata Expressionist automated data processing increased analytical throughput by shortening the time required for analysis from 5–6 hours to just 30 minutes in the case of very complex samples.

Impact #2

By eliminating analytical bottlenecks, Roche was able to keep pace with the large number of samples generated by their process chemists systematically optimizing each parameter and step.

Impact #3

The additional expert analytical capacity provided by automation of routine data processing tasks enabled Roche to better characterize unexpected impurities and provide more detailed reports to stakeholders without impacting the overall laboratory throughput.

Benefit #1	Increased analytical throughput by shortening the time required for analysis from 5–6 hours to just 30 minutes in the case of very complex samples.
Benefit #2	Automated data processing increased the number of process-related impurities identified in each analysis.

Benefit #1

Increased analytical throughput by shortening the time required for analysis from 5–6 hours to just 30 minutes in the case of very complex samples.

Benefit #2

Automated data processing increased the number of process-related impurities identified in each analysis.

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Overview

Automating LC-UV-MS-Based Analytics in Therapeutic Oligonucleotide Process Development

Operational Impact

Quantitative Benefit