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Enhanced stability testing with LIMS

12th July 2019


A new stability analytics capability has been introduced for the Matrix Gemini LIMS (Laboratory Information Management System) from Autoscribe Informatics.  A fully integrated charting module provides early indications of shelf-life performance for each product or batch of product under test.

Stability studies are used to assess the shelf life of a product by storing samples under differing conditions, typically as defined by regulatory authorities, and then testing samples taken from storage at pre-determined intervals. Running and managing multiple studies can be complex and time consuming; but Matrix Gemini LIMS helps to simplify the whole study management process. The in-built stability study management functionality is designed to automate and control the entire operation of the stability study including: protocol creation, study initiation and management, inventory management, sample pull points, future workload reporting and stability study reporting. 

Matrix Gemini helps to optimise the number of samples stored for a study, therefore avoiding shortages and eliminating waste. Samples to be pulled are automatically registered with relevant tests and test limits assigned, and users are automatically alerted that the samples need to be removed from the storage environments. This increases laboratory efficiency, as does the ability to define and manage the stability chambers and track their contents. However, understanding that no two organisations are likely to work in the same way, the unique configuration tools provided with Matrix Gemini allow specific workflows to be modified while maintaining the underlying stability functionality.
 
The new stability analytics module provides an integrated system for analysing stability studies that often involve multiple batches, users, and data transfers. The statistics and charting capabilities include ANCOVA statistics, tests for poolability, out of trend detection and residuals analysis. Predictions can be made using pooled data, pooled slope and worst case.





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