= Discovery stage.
= Translation stage.
= Clinically available.
MSACL 2019 EU : Luan

MSACL 2019 EU Abstract

Self-Classified Topic Area(s): Data Science

Comparison of Three Automated Data Processing and Reporting Approaches for Inborn Errors of Metabolism by LC-MS Flow Injection Analysis for Clinical Research

Bradley Hart, Yu Zhou, Xiaolei Xie, Kristine Van Natta, Shen Luan, Debadeep Bhattacharyya, Shawnna Donop
Thermo Fisher Scintific, San Jose, CA


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 Shen Luan (Presenter)
Thermo Fisher Scentific

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Relevant Financial Disclosures (within past 24 months)
Salary Thermo Fisher Scientific

Abstract

INTRODUCTION: With the advances of tandem mass spectrometry (MS), it is now easier to identify inborn errors of metabolism (IEM) for clinical research. However, there are some practical challenges in processing and interpreting MS data, for example, 1) an increasing number of targets for research, 2) the complexity, time, and effort of data interpretation, and 3) the lack of hands-on experience due to the occurrence of rare diseases. The success of an IEM workflow depends largely on how it processes, interprets, and integrates data and information from multiple sources. Automated reporting and interpretation tools are solutions which could streamline or expand the capability of data processing software for efficient data review, and rapid report delivery.

OBJECTIVES: This study was to evaluate and compare three automated data processing and reporting approaches, including evaluations of data acquisition, peak integration, concentration calculation, data reporting and interpretation, and ease of use.

METHODS: Dried blood spot samples were extracted and injected onto a Thermo Scientific TSQ Fortis MS by flow injection analysis (FIA) using a Vanquish Flex HPLC. The generated data files were processed using 3 different software packages: 1) Thermo Scientific™ TraceFinder™ software Version 4.1, 2) Thermo Scientific™ Chromeleon™ 7.2, and 3) iRC PRO software, (2Next srl and Fantoni, Prato, Italy). Custom report plug-in templates were created for TraceFinder(TF) and Chromeleon(CM) to perform data reporting and interpretation.

RESULTS: All three approaches could generate the desired results and perform user-defined meta calculations. From the view of the completeness of a start-to-finish strategy, both TF and CM could control instrument, acquire, process, and report data. While IRC PRO could only process and report data. For peak integration, all could do time-ranged integration, which is preferred by data from FIA analysis. However, IRC PRO does not allow user review of peak integration. TF and CM allow full user review of integrated peaks as well as adjusting compound specific integration parameters. IRC PRO and TF have built-in calibration mode for concentration calculation. CM needs to take advantage of a plug-in template to perform the calculation. IRC PRO has built-in data report and interpretation, while both TF and CM need a plug-in template to include those functions and bridge the gaps. However, the plug-in templates could expand the functions according to user needs and have more reporting flexibility. For easy of use, IRC PRO is very simple and straight-forward, but needs to import data from data acquisition software. TF and CM could integrate plug-in template into data acquisition step, with even less manual intervention.

CONCLUSION: All three approaches could significantly improve productivity, timeliness, quality management, and communication.