LAUSR

The application of nontargeted strategies based on high-resolution mass spectrometry (HRMS) directed toward the discovery of metabolites of known contaminants in fish is an interesting alternative to true nontarget screening. To reduce prolonged and costly laboratory experiments, recent advances in computing power have permitted the development of comprehensive knowledge-based software to predict the metabolic fate of chemicals. In addition, machine-based learning tools allow the prediction of chromatographic retention times (RT) or collision cross section (CCS) values when using ion mobility spectrometry (IMS). These tools can ease data evaluation and strengthen the confidence in the identification of compounds. The current work explores the capabilities of in silico prediction tools, refined by the use of RT and CCS prediction, to prioritize and facilitate nontarget liquid chromatography (LC)-IMS-HRMS data processing. The fate of the insecticide pirimiphos-methyl (PM) in farmed Atlantic salmon exposed to contaminated feed was used as a case study. The theoretical prediction of 60 potentially relevant biological PM metabolites permitted the prioritization of screening in different salmon tissues (liver, kidney, bile, muscle, and fat) of known and unknown PM metabolites. An average of 43 potential positives was found in the sample matrixes based on the accurate mass of protonated molecules (mass error ≤5 ppm). The application of different tolerance filters for RT (Δ ≤ 2 min) and CCS (Δ ≤ 6%) based on predicted values permitted us to reduce this number up to 66% of the features. Finally, five PM metabolites could be identified; two known metabolites (2-DAMP and N-desethyl PM) were confirmed with a standard, whereas three previously unknown metabolites (2-DAMP glucuronide, didesethyl PM, and hydroxy-2-DAMP glucuronide) were tentatively identified in different matrixes, allowing the first proposition of a metabolic pathway in fish.