实例探究 > Validation of LC–MS Multi-Attribute Method Supporting Biopharma Process Characterization

Validation of LC–MS Multi-Attribute Method Supporting Biopharma Process Characterization

公司规模
1,000+
地区
  • Europe
国家
  • France
产品
  • Genedata Expressionist
技术栈
  • Mass Spectrometry
  • Automated Workflows
  • Data Processing
实施规模
  • Enterprise-wide Deployment
影响指标
  • Innovation Output
  • Productivity Improvements
技术
  • 分析与建模 - 数据即服务
  • 分析与建模 - 实时分析
适用行业
  • 医疗保健和医院
  • 药品
适用功能
  • 产品研发
  • 质量保证
用例
  • 质量预测分析
  • 过程控制与优化
服务
  • 数据科学服务
  • 系统集成
关于客户
Merck KGaA is a major biopharmaceutical manufacturer based in Martillac, France. The company's Microbial Process Development Group is responsible for developing and producing recombinant proteins expressed in Escherichia coli and Pichia pastoris. The group includes scientists and experts in microbial processing analytics. They use mass spectrometry as a routine tool for supporting process development in recombinant protein production and are involved in developing original mass spectrometry-based approaches that enable better understanding of protein expression in microorganisms for biopharmaceutical development. Once a process is developed, it can be transferred to a manufacturing unit, where GMP specialists manage production, from pre-clinical stages through to commercial batch manufacturing of a drug substance.
挑战
The Microbial Process Development Group at Merck KGaA was tasked with developing and producing recombinant proteins expressed in Escherichia coli and Pichia pastoris. They used mass spectrometry (MS) as a routine tool for supporting process development in recombinant protein production. However, routine use of Multi-attribute method (MAM) in this environment meant overcoming various scientific, technological, and methodological challenges. These challenges included managing large amounts of data, producing unbiased audited results, and meeting process validation requirements. The overall production process for recombinant proteins involves multiple processing steps that are driven by defined process parameters. The characteristics of the resulting protein product must be such that the final drug substance is both safe and efficient. Studying the desired protein characteristics, then defining, monitoring, and managing critical quality attributes (CQAs) is key to success.
解决方案
The team at Merck KGaA implemented a flexible software solution with automated workflows that enabled them to address the challenges and reap the benefits of using MAM analyses routinely. They developed a peptide matching LC–MS method capable of monitoring four CQAs—oxidation, deamidation, gluconoylation, and truncation. The method was optimized to monitor the four identified CQAs, which reduced the run time from 120 minutes to just 30 minutes, quadrupling sample throughput. They also implemented an enterprise software solution (Genedata Expressionist, Genedata AG) to handle large and complex experimental MS datasets. The software enabled them to develop automated data workflows and provided full transparency throughout, with the ability to review results or export to final reports.
运营影响
  • The MAM approach developed was targeted to be high throughput. Simply being able to monitor four CQAs in one analytical run proved to be time-saving.
  • By using appropriate sample preparation and optimizing LC–MS gradients and analyses the team achieved a throughput of almost 300 samples per week.
  • Using Genedata Expressionist to completely automate the data processing allowed for a reduction in total data analysis time to less than 1 hour.
  • Both the method and the data processing are user friendly, so the MAM approach can be run by operators with no prior MS experience.
  • This MAM approach is validated and compliant within the 21 Code of Federal Regulations Part 11 elements of the software platform and as such, it is ready for QC testing to support GMP batch intermediate monitoring.
数量效益
  • Reduced the run time from 120 minutes to just 30 minutes, quadrupling sample throughput.
  • Achieved a throughput of almost 300 samples per week.
  • Reduced total data analysis time to less than 1 hour.

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