MSACL 2024 Abstract

Simultaneous Quantitation and Discovery (SQUAD) Metabolomics: A Combination of Targeted and Untargeted MS Workflows for Drug Toxicology Bashar Amer (1), Courtney Patterson (1), Jillian R Neifeld (2), Sarah H Bartock (2), Wen X Dui (2), Kerry Hassell (1), and Susan S. Bird (1) (1) Thermo Fisher Scientific, San Jose, California, United States (2) Quest Diagnostics, Chantilly, Virginia, United States Bashar Amer, PhD (Presenter) Thermo Fisher Scientific

Presenter Bio: Bashar is an LC-MS and GC-MS metabolomics scientist in the vertical marketing team at Thermo Fisher Scientific. He is developing metabolomics end-to-end workflows utilizing state-of-the-art orbitrap-based mass spectrometers and sophisticated software solutions such as compound discoverer and trace finder as part of his role. Bashar has a background in microbial, and nutritional metabolomics, but he also developed analytical assays for food studies.
Lately, Bashar and the team introduced a new metabolomics workflow called SQUAD for simultaneous quantitation and discovery analysis, which is offering researchers a way to strike the balance between untargeted and targeted approaches in one single experiment.

Introduction:

Without internal standards and libraries, untargeted metabolomics lacks accurate quantitation and identification of metabolites needed to study biological systems. These steps can complicate the study design and data processing; thus, many researchers prefer to target a few analytes and risk missing significant compounds.

Therefore, a single injection simultaneous quantitation and discovery (SQUAD) metabolomics workflow that provides confident identification and/or accurate quantitation of analytes such as drugs, by analyzing their authentic standards, without compromising the untargeted analysis is preferred. The workflow also enables the discovery of analytes with potential biological significance like drugs' metabolism products.

The goal of this study was to use SQUAD analysis to uncover the presence and relative quantitation of drug metabolites present in unknown urine samples. As drug abuse continues to be on the rise, it is important to be able to identify drugs and their metabolites in toxicological samples.

Methods:

14 unknown urine patient samples, obtained from Quest Diagnostics, were extracted with a hydrolysis enzyme and pooled into three mixes. Drug metabolites were separated with on a Thermo Scientific™ Accucore™ Phenyl-Hexyl column connected to a Thermo Scientific™ Vanquish™ Horizon system. Data was acquired on the Thermo Scientific™ Orbitrap Exploris™ 120 high-resolution mass analyzer using data dependent MS2 mode in both polarities and AcquireX™ software was used to generate automatic and iterative exclusion/inclusion lists for each of the 4 identification scans. Thermo Scientific™ Compound Discoverer™ 3.3 software was used for data processing, analytes relative quantitation, and unknown annotation.

Preliminary Data:

In this study, the pooled unknown patient samples showed the ability of the Orbitrap Exploris ™ 120 paired with AcquireX™ and Compound Discoverer™ software to detect over 150 metabolites in positive mode and over 40 metabolites in negative mode per pooled mix. This workflow facilitates a reliable quantitation over 4 orders of magnitude for various metabolites. In one of the mixes, norfentanyl was identified and relatively quantified to about 700 ng/mL.

In addition, using the compound class scoring node for fentanyl's, common fentanyl metabolites were detected including norfentanyl, β-Hydroxyfentanyl, 4-flurofentanyl, and despropionyl p-fluorofentanyl. Using diagnostic fentanyl fragments and retention times, a potential unknown fentanyl metabolite was also detected.

High data quality, reliability, and robustness of measurement were observed by evaluating selected metabolites to assess instrument performance using metrics including retention time, mass accuracy, and signal response. Minimal chromatographic shift and consistent signal responses were observed as evidenced by a low % CV for sample replicates. Sub-ppm mass accuracy was detected for all targets over the entire acquisition period.