This document describes how an analytical laboratory automated their urine drug screening workflow using ASCENT rules-based software. The new workflow applied data quality rules to 90% of samples, flagging only those that needed human review. This reduced the manual review time per batch by 83%. The laboratory tested the new system on 1400 samples across 35 batches, finding 97.9% correlation between automated and manual results. The automated workflow provided consistent application of rules, remote data access, and reduced the workload for certifiers and analysts.

1. Analyst Reviews Data On-Screen at LC-MS/MS Reviews Data, Compares against Rules and Flags Appropriate Samples Post to LIMS Performed by Analyst Post to LIMS Print to Print-Capture Database Review Flagged Results in ASCENT Performed by Analyst Review Reports in Database Review Posted Results in LIMS Review Posted Results in LIMS Performed by Certifier Review Results in LIMS, ASCENT and batch sheet Review Results in LIMS, Database and batch sheet Performed by Certifier Certifier Releases Results Certifier Releases Results Increased Process Efficiency in UPLC-MS/MS Data Review Through Use of Rules-Based Software Automation Joseph F. Ervin1, Ben Abel1, Andrea Terrell1, Amy M. Gilchriest2, Justin M. Grimes2, Randall K. Julian, Jr.2 1AIT Laboratories, 2265 Executive Dr., Indianapolis, IN 46241, 2Indigo BioSystems, 20 East 91st St. Suite 200, Indianapolis, IN 46240 Example Correlation Plot and Outlier Result Solution: ASCENT Abstract A previously manual data review process for opiates in urine using UPLC-MS/MS was automated via Indigo BioSystems' ASCENT software system. Ascent applied data quality rules and determined that approximately 90% of results adhered to quality standards and were releasable without further human review. Additionally all problematic results needing human intervention were flagged by one of the rule scripts due to data unacceptability. This process has further implications as result quality can be increased through consistent application of rules based on quality standards. ASCENT by Indigo BioSystems is a configurable data analysis program for chromatographic mass spectrometry data. Ascent retrieves raw data from the mass spectrometer and converts it to a standard XML format. It then applies proprietary peak-processing/integration algorithms to extract chromatograms for each acquisition function. Rules-scripts compare the data to customer-specified criteria and flags data that do not conform to acceptability parameters. The data is accessible for review via a configurable web-based interface, and results can also be autoposted to LIMS. ASCENT integration model correctly integrates to exclude interferent Manually Processed Peak incorrectly integrated coeluting interferent Bland-Altman Plot for Batch #358xxx Background ASCENT Review Interface AIT Laboratories is a premier clinical/forensic toxicology lab in Indianapolis , Indiana. One of our highest volume assays is for opiates in urine. Urine samples undergo enzyme hydrolysis and are analyzed via UPLC-APCI-MS/MS. The analytes included in the assay (in elution order) are morphine, oxymorphone, hydromorphone, codeine, oxycodone, hydrocodone, and 6-monoacetylmorphine (6-MAM), plus their respective deuterated analogs. Linear range of the assay is 10 ng/mL  10,000 ng/mL (5000 ng/mL for oxycodone) using a single-point calibrator. Features: - Simple navigation using mouse-clicks or keys - Data can be sorted by sample name, analyte, flagged/unflagged or column values - Terminal interface at top allows analysts to add or remove chromatogram sets, i.e. for keeping the calibrator visible as comparison or for checking metabolites - “Find” function allows analyst to go directly to a specific record - Flags are displayed at the bottom in red Current Data Analysis Workflow New Workflow with Initial Install of ASCENT Performed by ASCENT Key Issues to be Addressed by Automation Workflow Impacts Provide a data review interface that allows remote access via network and analyst interaction with chromatograms. - Effect: Reduce redundancy Provide an automated ability for under- or over-linearity results to be posted according to in-house rules. - Effect: Reduce the need for analysts to correct autoposted results Provide an automated system that compares chromatographic data to the correct quality rules and flags results that do not meet criteria. - Effects: Ensure consistent application of rules Reduce the need for human review to only those samples that require human judgment, reducing the workload on the certifiers/analysts. ASCENT has not only allowed us to reduce the number of “touch-time” steps in the process, it has also reduced the amount of time required for the remaining steps by simplifying them and providing a useful, flexible interface for data review. - One secure, web-based interface that allows data interaction, enhances accessibility, and simplifies workflow - Data rules are centralized, allowing updates and changes to be quickly and consistently applied - Reduction of human review to only flagged samples reduces the workload on human analysts and ensures they are reviewing the peaks that need a human decision - Flexible configuration allows the system to be adapted to changing needs, enhancing our ability to use new ideas in the future Initial Testing and Rule Setup 35 batches (approximately 1400 samples) were analyzed by Ascent and the results were compared to the manual process for correlation. Correlation was calculated by comparing the Ascent result to the mean of the Ascent and original results. A total of 3298 non-zero results were used, with 97.9% of these correlating within 10%. All of the non-correlating samples were addressed by data rule flags. Concurrently, 100 batches were processed and flagged samples were reviewed by certifiers to determine workflow efficiency gains. 90% of the results were acceptable by our data quality criteria and releasable without further review. Review and address of flagged samples required an average review time of approximately 15 minutes per batch, an 83% reduction over the manual process. Acknowledgements •Special thanks must go to Dr. Michael Evans for his ongoing support and assistance throughout this project. Also, special thanks to Lisa Deakins, Julie Lowe, Paul Ridenour, and Jared van der Linden for their contributions to this project.