Automating the processing of analytical data for System Suitability Testing (Case study)

This case study examines how AstraZeneca integrated Chrom SST automation into their purification workflow, enabling daily checks, long-term trend analysis, and preventive maintenance. These advancements minimized downtime and ensured consistent system performance.

Introduction

In early drug discovery, Design-Make-Test-Analyze (DMTA) cycles generate thousands of molecules annually that require thorough analysis. For successful purification, these results must be high-quality, reproducible, and scalable. However, system downtime and the need for re-analysis can significantly slow down the process, making such delays undesirable.

Problem Statement

Manual processing of LCMS data is not only time-consuming but also prone to subjective interpretation. Small fluctuations in peak retention times (RT) are difficult to detect visually, often impacting reproducibility. Additionally, the complexity of ionization processes demands significant operator expertise, presenting further challenges in achieving consistent and reliable results.

Methodology

To address these challenges, AstraZeneca’s analytical and purification lab implemented automated SST analysis using Mnova Gears and Chrom SST. Mnova Gears, deployed on a server, collects and aggregates raw data from multiple instruments several times a day. Chrom SST then automatically analyzes this data, comparing it against user-defined specification files to ensure objective and reliable results. Processed data is stored in a PostgreSQL database and visualized through Spotfire dashboards, providing a streamlined interface for trend analysis and system monitoring.

Results

The integration of automation improved result accuracy and reproducibility while enabling daily performance checks. The addition of advanced analytics and visualization tools supported long-term trend analysis and preventive maintenance, reducing downtime, and ensuring consistent purification performance.

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