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Highlights • Mass spectrometry produces data which can be used to monitor instrument performance. • We describe a tool that parses and visualizes mass spectrometry data. • This toolkit can be applied to increase quality control for a complex LCMS assay., Introduction We have developed a set of tools built with open-source software that includes both a database and a visualization component to collect LC-MS/MS data and monitor quality control parameters. Description of tool To display LC-MS/MS data we built a parsing tool using Python and standard libraries to parse the XML files after each clinical run. The tool parses the necessary information to store a database comprised of three distinct tables. Another component to this toolkit is an interactive data visualization tool that uses the data from the database. There are 5 different visualizations that present the data based on interchangeable parameters. Evaluation of tool Using histogram visualization, we assessed how quality control parameters that feed our quality control algorithm, SMACK, which assists to improve the efficiency of data review and results, performed against the collective data. Using the newly identified QC parameter values from the toolkit, we compared the output of the SMACK algorithm; the number of QC flags changed in that there was a 1.7% (31/1944 observations) increase in flags and a 7.1% (138/1944 observations) decrease in presumed false positive flags, increasing the overall performance of SMACK which helped staff focus their time on reviewing more concerning QC failures. Discussion We have developed a customizable web-based dashboard for instrument performance monitoring for our opiate confirmation LC-MS/MS assay using data collected with each batch. The web-based platform allows users to monitor instrument performance and can encompass other instruments throughout the laboratory. This information can help the laboratory take proactive measures to maintain instruments, ultimately reducing the amount down time needed for maintenance.

Background Dried specimens have been proposed in multiple environments to minimize costs associated with specimen storage and shipping in clinical studies. This report describes the development and validation of an automated method for qualitative toxicology screening of dried urine samples using LC–MS/MS. Methods Urine standards containing 41 compounds were prepared and applied to filter paper cards. Dried urine was eluted from the cards using a Dried Blood Spot (DBS) autosampler from Spark Holland, which was plumbed inline with a Thermo Scientific Turboflow chromatography system for subsequent MS/MS detection with selected reaction monitoring . Limits of detection, precision of peak areas, repeatability, and carryover studies were conducted. Concordance with a reference LC–MS/MS method using liquid samples was evaluated using remnant discarded specimens. Results The limit of detection ranged from 5 to 75 ng/mL for most compounds. At the LOD for each analyte, the peak area precision ranged from 8 to 29%. For 20 repeat injections of samples spiked at ±25% of the LOD, there was a 4% false positive rate for the 75% × LOD samples, and a 0.4% false negative rate for the +125% × LOD samples. In comparing 40 known positive specimens analyzed with the DUS method and a liquid urine reference method, there was 88% agreement. Analysis of 10 known negative specimens yielded negative results. There was no significant carryover detected up to 2000 ng/mL for any of the analytes in the assay. Conclusion Using a robotic DUS sampling an inline HTLC–MS/MS system, we have developed and validated a fully-automated and robust method for multi-analyte detection of drugs of abuse in dried urine specimens.