Case Studies Automated Software-Driven MAM Implementation for Biotherapeutic Characterization

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	Automated Software-Driven MAM Implementation for Biotherapeutic Characterization

Automated Software-Driven MAM Implementation for Biotherapeutic Characterization

Technology Category	Analytics & Modeling - Big Data Analytics Analytics & Modeling - Real Time Analytics
Applicable Industries	Healthcare & Hospitals Pharmaceuticals
Applicable Functions	Product Research & Development Quality Assurance
Use Cases	Predictive Quality Analytics
Services	Data Science Services
Challenge	Just Biotherapeutics faced several challenges in analyzing biotherapeutics using mass spectrometry-based methods. The large amount of data generated by comprehensive in-depth characterization was a major challenge, as it required frequent analysis of various samples and large-scale studies. Current solutions often relied on multiple software packages and manual transfer, making data processing and analysis a major bottleneck in the biopharmaceutical development process. Additionally, the richness of MS data often led to the generation of large numbers of false-positive identifications, which had to be manually investigated, a process that was typically laborious and time-consuming. MAM monitoring systems also needed to deliver efficient, reproducible, and reliable detection of new or unexpected peaks. Lastly, the development and manufacturing of biopharmaceutical molecules generated data from a multitude of sources, requiring a data system that could manage and collate large amounts of disparate data. Read More
About Customer	Just Biotherapeutics is an integrated design company that focuses on technologies that will accelerate the development of biotherapeutics and substantially reduce their manufacturing cost. The company is based in Seattle, Washington, USA and has been a customer since 2017. Just Biotherapeutics is part of the MAM consortium, which includes biotherapeutic manufacturers, instrument and software vendors, and government agencies in the USA (NIST and the FDA) and Japan. The goal of the consortium is to enable the BioPharma community to implement a robust mass-spectrometry-based method for biotherapeutic characterization and release of biotherapeutics from QC. Read More
Solution	Just Biotherapeutics implemented Genedata Expressionist, a software that processes and analyzes data from any MS instrument using fully automatable and configurable workflows. The software uses metadata to directly apply specific parameters to individual data sets at any step, offering complete control over each experiment while boosting productivity by increasing throughput and eliminating laborious manual intervention. Genedata Expressionist workflows are highly flexible and are designed to automate processes specific to each user. Each step of the workflow can be controlled by user-definable settings, offering an unparalleled level of control during MS data processing. For example, signals can be filtered according to their intensity, charge, or presence in a given proportion of samples. This provides an efficient method for reducing the number of false-positive identifications, while ensuring that genuine contaminants are not overlooked. The software also takes all available MS data into account during MAM new peak detection, greatly increasing the likelihood that unexpected contaminants and product variants are detected. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Analytics & Modeling - Real Time Analytics

Applicable Industries

Healthcare & Hospitals

Pharmaceuticals

Applicable Functions

Product Research & Development

Quality Assurance

Use Cases

Predictive Quality Analytics

Services

Data Science Services

Challenge

Just Biotherapeutics faced several challenges in analyzing biotherapeutics using mass spectrometry-based methods. The large amount of data generated by comprehensive in-depth characterization was a major challenge, as it required frequent analysis of various samples and large-scale studies. Current solutions often relied on multiple software packages and manual transfer, making data processing and analysis a major bottleneck in the biopharmaceutical development process. Additionally, the richness of MS data often led to the generation of large numbers of false-positive identifications, which had to be manually investigated, a process that was typically laborious and time-consuming. MAM monitoring systems also needed to deliver efficient, reproducible, and reliable detection of new or unexpected peaks. Lastly, the development and manufacturing of biopharmaceutical molecules generated data from a multitude of sources, requiring a data system that could manage and collate large amounts of disparate data.

About Customer

Just Biotherapeutics is an integrated design company that focuses on technologies that will accelerate the development of biotherapeutics and substantially reduce their manufacturing cost. The company is based in Seattle, Washington, USA and has been a customer since 2017. Just Biotherapeutics is part of the MAM consortium, which includes biotherapeutic manufacturers, instrument and software vendors, and government agencies in the USA (NIST and the FDA) and Japan. The goal of the consortium is to enable the BioPharma community to implement a robust mass-spectrometry-based method for biotherapeutic characterization and release of biotherapeutics from QC.

Solution

Just Biotherapeutics implemented Genedata Expressionist, a software that processes and analyzes data from any MS instrument using fully automatable and configurable workflows. The software uses metadata to directly apply specific parameters to individual data sets at any step, offering complete control over each experiment while boosting productivity by increasing throughput and eliminating laborious manual intervention. Genedata Expressionist workflows are highly flexible and are designed to automate processes specific to each user. Each step of the workflow can be controlled by user-definable settings, offering an unparalleled level of control during MS data processing. For example, signals can be filtered according to their intensity, charge, or presence in a given proportion of samples. This provides an efficient method for reducing the number of false-positive identifications, while ensuring that genuine contaminants are not overlooked. The software also takes all available MS data into account during MAM new peak detection, greatly increasing the likelihood that unexpected contaminants and product variants are detected.

Impact #1	Increased productivity: Using optimized Genedata Expressionist workflows, data processing for MAM monitoring can be fully automated, enabling processing of hundreds of samples per week with very little manual interaction.
Impact #2	Easier collaboration and better decision-making: The enterprise nature of Genedata Expressionist facilitates knowledge transfer and harmonizes processes within and between labs and provides every user within an organization with fast and easy access to all relevant knowledge on a given biopharmaceutical.
Impact #3	Less time required for data review and triage: Knowledge-based grouping and filtering of spurious signals greatly reduces the number of false-positive signals and consequently the time required for data review and curation.

Impact #1

Increased productivity: Using optimized Genedata Expressionist workflows, data processing for MAM monitoring can be fully automated, enabling processing of hundreds of samples per week with very little manual interaction.

Impact #2

Easier collaboration and better decision-making: The enterprise nature of Genedata Expressionist facilitates knowledge transfer and harmonizes processes within and between labs and provides every user within an organization with fast and easy access to all relevant knowledge on a given biopharmaceutical.

Impact #3

Less time required for data review and triage: Knowledge-based grouping and filtering of spurious signals greatly reduces the number of false-positive signals and consequently the time required for data review and curation.

Benefit #1	Increased throughput of data processing for MAM monitoring, enabling processing of hundreds of samples per week with very little manual interaction.
Benefit #2	Significant reduction in the number of false-positive signals and consequently the time required for data review and curation.

Benefit #1

Increased throughput of data processing for MAM monitoring, enabling processing of hundreds of samples per week with very little manual interaction.

Benefit #2

Significant reduction in the number of false-positive signals and consequently the time required for data review and curation.

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Overview

Automated Software-Driven MAM Implementation for Biotherapeutic Characterization

Operational Impact

Quantitative Benefit