High-risk contaminants detected in wastewater effluent samples can be prioritized prior to structural assignment...

Discovering hazardous pollutants currently relies on the tedious and often inaccurate structural identification step, required for further toxicity and exposure studies. Here, we propose and validate a workflow for prioritizing the detected features prior to structural elucidation. The proposed approach relies on the priority score (environmental concentration divided by lethal concentration) serving as a proxy of the risk and is deduced solely from mass spectrometric data. The workflow integrates two machine learning approaches, MS2Tox and MS2Quant, that predict toxicity and ionizability of unidentified molecular features, respectively, and the relative risk-based ranking of detected features does not require any additional standards to be measured in the same run, allowing the application to digitally frozen data. Validation by using priority score for classifying 23 chemicals with available risk quotient values into high and low risk categories yielded recall value of 0.33 to 0.81, precision of 0.08 to 0.50, and accuracy of 0.52 to 0.81, depending on the acquisition mode and fish species. Applying the developed workflow to wastewater effluent prioritized 13-19% of features with predicted fingerprints as "precautionary risk features" with a risk quotient ≥1 based on the lower limit of the 95% prediction interval. All prioritized features were subject to spectral library matching, with 12% of the features yielding level 2 identification. Features categorized as high-risk were further subject to structural annotation using in silico identification tools SIRIUS+CSI:FingerID and MetFrag. While plausible candidates were suggested, the in silico tools disagreed in the top 10 suggested structures, highlighting structural assignment as a bottleneck in risk estimation. This work investigates the usefulness of applying data-driven feature prioritization prior to identification.