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Improving Protein Purification through Data Science: A Case Study on Alexion Pharmaceuticals
Technology Category
- Analytics & Modeling - Big Data Analytics
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Equipment & Machinery
- Pharmaceuticals
Applicable Functions
- Product Research & Development
Use Cases
- Time Sensitive Networking
Services
- Data Science Services
- System Integration
The Challenge
Alexion Pharmaceuticals, a biopharmaceutical company, was facing several challenges in its downstream process development operations. The company was struggling with data silos as instrument vendors did not integrate with one another, leading to manual data movement. The proprietary data formats of vendors required their specific tools, which hampered third-party data standardization. Additionally, informatics leads had to manually move, update, and curate files from multiple locations. Scientists were also required to enter peak information into analytical software themselves. Furthermore, existing dashboarding tools had steep learning curves and could not manage metadata. Scientists were constantly needing to test their hypotheses and derive useful insights from heterogeneous data sources.
About The Customer
Alexion Pharmaceuticals, Inc. is a biopharmaceutical company that focuses on serving patients with devastating and rare disorders through the development and commercialization of life-transforming therapeutics. The company's product focus includes research novel molecules and targets in the complement cascade and the development efforts in the core therapeutic areas of hematology, nephrology, neurology, metabolic and cardiology. Alexion tackles rare diseases using biologic therapeutics, transforming the lives of many patients affected by devastating conditions such as generalized Myasthenia Gravis (gMG) and Hypophosphatasia (HPP).
The Solution
Alexion Pharmaceuticals adopted the Tetra Data Platform (TDP) to overcome these challenges. The TDP transfers, standardizes, and harmonizes siloed data in the cloud. New data is indexed via ElasticSearch and structured into SQL tables to enable searches and queries. TetraScience’s pipelines standardize and structure data for downstream visualization, storage, or AI/ML insights. Data flows from source to the cloud to ELN and data science targets without any manual intervention. The TetraScience API can be used to funnel harmonized data into data science applications. Consuming this data in customizable data science applications like Streamlit provides scientists full control over chromatogram overlays and integration.
Operational Impact
Quantitative Benefit
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