Development and evaluation of a comprehensive workflow for suspect screening of exposome-related xenobiotics and phase II metabolites in diverse human biofluids
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Date
2024-01-13Author
Musatadi Larrucea, Mikel
Baciero Hernández, Inés
Prieto Sobrino, Ailette
Olivares Zabalandikoetxea, Maitane
Etxebarria Loizate, Nestor
Zuloaga Zubieta, Olatz
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Chemosphere 351 : (2024) // Article ID 141221
Abstract
Suspect and non-target screening (SNTS) methods are being promoted in order to decode the human exposome since a wide chemical space can be analysed in a diversity of human biofluids. However, SNTS approaches in the exposomics field are infra-studied in comparison to environmental or food monitoring studies. In this work, a comprehensive suspect screening workflow was developed to annotate exposome-related xenobiotics and phase II metabolites in diverse human biofluids. Precisely, human urine, breast milk, saliva and ovarian follicular fluid were employed as samples and analysed by means of ultra-high performance liquid chromatography coupled with high resolution tandem mass spectrometry (UHPLC-HRMS/MS). To automate the workflow, the “peak rating” parameter implemented in Compound Discoverer 3.3.2 was optimized to avoid time-consuming manual revision of chromatographic peaks. In addition, the presence of endogenous molecules that might interfere with the annotation of xenobiotics was carefully studied as the employment of inclusion and exclusion suspect lists. To evaluate the workflow, limits of identification (LOIs) and type I and II errors (i.e., false positives and negatives, respectively) were calculated in both standard solutions and spiked biofluids using 161 xenobiotics and 22 metabolites. For 80.3 % of the suspects, LOIs below 15 ng/mL were achieved. In terms of type I errors, only two cases were identified in standards and spiked samples. Regarding type II errors, the 7.7 % errors accounted in standards increased to 17.4 % in real samples. Lastly, the use of an inclusion list for endogens was favoured since
it avoided 18.7 % of potential type I errors, while the exclusion list caused 7.2 % of type II errors despite making the annotation workflow less time-consuming.