Liquid chromatography–high-resolution mass spectrometry (LC/HRMS) is a powerful tool for detecting chemicals that are present in low concentrations. While this technique has revealed thousands of ionizable pollutants in environmental samples [1,2], the expanding list of emerging contaminants highlights the urgency to speed up their risk assessment [3,4]. Generally, the risk assessment workflow starts with structural identification, followed by obtaining the analytical standard for confirmation, toxicity assessment, and quantification with a calibration curve. To speed up the process, machine learning has found use in predicting toxicity and ionization efficiency; however, most of the models in use still require a chemical structure as an input. Therefore, detected but unidentified chemicals are frequently discarded from further analysis and the bioactivity of samples often remains partially unexplained [5]. Still, the fragmentation spectrum provides information about the structure which can be related to the properties of the chemical. We developed a workflow for estimating the risk of chemicals detected in non-target screening based on their MS2 data. Two prediction models, MS2Quant [6] for ionization efficiency and MS2Tox [7] for acute fish toxicity, were trained based on structural fingerprints. While structural fingerprints can be calculated from a structure, the recently developed SIRIUS+CSI:FingerID software [8] offers the possibility to predict these fingerprints based on the MS2 spectrum, and therefore predict chemical properties without structural assignment. Based on the validation set, the root mean square errors of MS2Quant and MS2Tox were 5.9× (39 chemicals) and 7.8× (219 chemicals), respectively. These models were applied in a non-target screening workflow regarding wastewater analysis. The preliminary results show that MS2Quant and MS2Tox help to pinpoint chemicals that pose a higher risk compared to a top five approach. Therefore, this approach provides the possibility to evaluate the risk of unidentified LC/HRMS features and prioritize high-risk chemicals in identification.
Author Contributions
H.S., P.P. and A.K. designed the research study. H.S. and P.P. developed the models and wrote the code. L.J., H.S. and L.M. performed the measurements. A.K., M.P. and M.M. (Michael McLachlan) performed supervision. A.K., J.M., M.M. (Matthew MacLeod) and M.B. acquired funding for the project. All authors have read and agreed to the published version of the manuscript.
Funding
The funding was generously provided by the Swedish Research Council for Sustainable Development, grant 2020-01511.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data will be made available together with the full publication.
Acknowledgments
The authors would like to thank Claudia Möckel and Merle Plassmann for their technical support.
Conflicts of Interest
The authors declare no competing financial interests.
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