9 water sample untargeted LC-MS/MS
How to screen for pharmaceuticals in an environmental sample
Precursor drug screening structure database search
predicted molecular fingerprint structure database confidence score
Transformation product screening
structure database search
in a custom generated database of transformation products
predicted molecular fingerprint
transformation product database
Figure 1: How to screen for pharmaceuticals in an environmental sample: We screen for pharmaceuticals in environmental samples on two levels: precursor drug screening and transformation product screening. For precursor drug screening, we search biomolecule structure databases including a custom structure database of pharmaceuticals. For transformation product screening, we generate transformation products with BioTransformer (Djoumbou-Feunang et al. 2019; Wishart et al. 2022). Results are validated using spectral library search, confi dence scores for structure database search hits, and substructure annotation. All methods are part of SIRIUS.
precursor drug screening
probably incorrect
probably correct
substructure annotation spectral library search
spectral library search
01 5
30
structure database search
Figure 3: SIRIUS results for monomethyl phthalate and Aspirin which have similarly highspectral library search scores for the same feature. The colour highlighting of the candidate structures reflects substructures that are supported by fingerprint evidence (green), substructures that contradict fingerprint evidence (pink), and substructures with mixed support (yellow).
Figure 2: Precursor drug screening: Number of pharmaceutical compounds from the drug list putatively identifi ed by spectral library search (left, blue) and by structure database search using SIRIUS (right, pink). 50 compounds have been identifi ed by both methods.
insect repellent and plastic, justifying the possibility of this alternative putative identification8
. Further investigation into both results could provide additional
clarification (see Figure 3). As molecular structure annotation in SIRIUS is based on molecular fingerprinting, structural explanations can be explored within SIRIUS (see Figure 3). Moreover, substructure annotations visualise the direct connection to the input MS/MS spectrum (see Figure 5). The unambiguous identity of this feature can only be resolved by additional experimental data. The advantage of using SIRIUS lies in its ability to present both possibilities - Aspirin as the best spectral match and monomethyl phthalate as the best structure database match - preventing ‘suspect-blindness’ and showing that the annotation of Aspirin is not unambiguous.
Transformation product screening
For transformation product screening, we only considered transformation products that were not themselves on the drug list. A total of 292 unique transformation products were detected as top-ranked hits. Of these, only 33 transformation products were also detected through spectral library searches (see Figure 4).
In total 85 transformation products were classifi ed as high-confi dence identifi cations7
Notably, 56 of these high-confi dence hits were not found through spectral library search, highlighting the complementary nature of SIRIUS in detecting transformation products that might otherwise be overlooked.
. structure database search
85 29
207 4
spectral library search
transformation product
high confidence
screening
Figure 4: Transformation product screening: Number of putatively identifi ed transformation products using spectral library search (blue, 33 in total) and using SIRIUS structure database search (pink, 292 in total) in a custom database of transformation products generated with BioTransformer. The share of compounds displayed in dark pink represents those detected with high confi dence.
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Biotransformer
validation
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