Apr 9, 2019 – MSDG Meeting

Liquid Chromatography High Resolution Mass Spectrometry (LC-HRMS) has been transforming the pace of biomedical research and discovery at an ever increasing pace over the past two decades. However, we are still challenged by the vastness of the data being generated and are unable to maximize all possible knowledge that can be gained from these experiments. Recent advances in computational methods have provided several tools that if used properly, can greatly increase the amount of clinically useful information that can be extracted from a single mass spectrometry assay. Employing such approaches will lead to a drastic reduction in the gaps in time between the acquisition of data, interpretation of results, formulation of new hypotheses and testing those new hypotheses. We hope to introduce the audience to some of those computational techniques available for use in LC-HRMS based biomedical research and to provide evidence of how these techniques are being used to gain new insights into previously unexplained disease pathologies.

In order to increase the safety and efficacy of drug products, impurity profiling is routinely performed to identify and quantify any residual impurities. These impurities may result from the instability of drug substances or reaction with added excipients or packaging materials or they may arise from starting and intermediate materials used in the manufacturing of active pharmaceutical ingredients (API). Since the amount of impurities will determine the safety and potency of the final product, a key challenge for drug development researchers is to identify, quantify, and control any impurities as early and efficiently as possible.

Because there can potentially be many impurities, some of which can be toxic and of low abundance, data processing can be onerous and time consuming if the software or the quality and type of data itself is not up to the task. Thus, an automated, high performance solution for detecting and identifying impurities is essential for relieving the burden and efficiently finding any stability or degradation issues.

The SCIEX X500R QTOF system and SCIEX OS Analytics were used to measure stability and detect impurities in amiodarone samples. Amiodarone is an anti-arrhythmic medication used to treat and prevent several types of irregular heartbeats. This integrated hardware and software solution compounds from MS and MS/MS data and reduces the need for manual review.