Biologics Characterization | TetraScience
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Tetra workflow\ for biologics characterization Traditional workflow\ for biologics characterization
Automated processes
Connect all data sources and targets seamlessly, boosting speed and data quality
Centralized, enriched data
Find previous results fast with searchable data in the cloud and avoid repeating experiments
Ready for AI
Model the CQA-CPP relationship to accurately assess risk and identify deviations earlier
Inaccessible data
Lack of access to historical data hinders the design of characterization studies and selection of CQAs
Manual contextualization
Manually linking metadata to raw and processed data and to ELN entries is inefficient
Wasted effort
Late identification of deviations leads to characterizing samples that already failed earlier testing
Harness your data for CQA identification and monitoring
Replatform
Collect and centralize data from all instruments and software
Engineer
Contextualize and harmonize the data for search and analytics/AI
Analytics
Monitor and trend CQAs with visualization and analytics tools
AI
Use AI/ML to understand how CPPs impact CQAs, leading to better QC
How to free your data from isolation
Explore how dispersed scientific data can easily be accessed, enriched, and harmonized for analytics and AI/ML with the Tetra Scientific Data and AI Cloud. This on-demand webinar features multiple case studies.