Mass Spectrometry & Spectroscopy


Detecting pharmaceuticals and their transformation products Martin Hoffmann and Franziska Hufsky, Bright Giant GmbH, Jena, Germany


The presence of pharmaceuticals and their transformation products in water sources is an emerging environmental concern, posing potential risks to ecosystems and human health. Traditional targeted analysis focuses on known compounds but overlooks transformation products that may exhibit higher persistence or bioactivity than the original drug.


Non-targeted screening using high-resolution mass spectrometry has emerged as a powerful tool for detecting both known and unknown contaminants, unlike targeted approaches that only screen for a predefi ned list of suspects. Further expanding analytical strategies to include transformation products enables a deeper understanding of their environmental fate and behaviour, which is essential for developing effective mitigation strategies to safeguard public health. This approach is relevant not only for pharmaceuticals but also for pesticides and industrial chemicals, whose degradation products may have signifi cant environmental impacts.


Here, we demonstrate how SIRIUS annotates pharmaceuticals in Luxembourgish rivers, from precursor drug screening in structure databases and spectral libraries to transformation product screening using a custom-generated database.


Experimental Setup


The analysed dataset was obtained from Singh et al. (2021). Surface water samples were collected from Luxembourgish rivers at 13 different locations. A total of 92 samples were gathered during routine monitoring events between 2019 and 2020.


A non-targeted analysis approach was employed. The water samples were concentrated using solid-phase extraction and subsequently analysed using liquid chromatography coupled to a high-resolution mass spectrometer (LC-HRMS). The LC-HRMS analysis was conducted on a Thermo QExactive HF mass spectrometer equipped with a Waters Acquity UPLC BEH C18 column (1.7 μm, 2.1 × 150 mm) using positive electrospray ionisation and data-dependent acquisition. Note that while only the positive ion mode was used in this analysis, SIRIUS can also process negative ion mode spectra. MS and MS/MS spectra were imported to SIRIUS.


For further details on data acquisition, please refer to Singh et al. (2021).


Data Resources For our analysis, we utilised the following data resources. Drug list: A compilation of 8161


Methods


All 92 samples were imported to SIRIUS (Dührkop et al. 2019) for analysis. Preprocessing yielded a total of 29 646 features, of which 15 819 were valid for computation, i.e. meeting the criteria of having MS/MS data, not being multiply charged, and not being multimeric. Results were considered for features of all quality levels, ensuring inclusion of all computable data within SIRIUS. Be aware that all reported identifi cations are putative identifi cations, as annotation results were not experimentally verifi ed.


Molecular formula annotation


Molecular formulas were assigned using database search. In that case, SIRIUS exclusively considers molecular formulas included in the chosen database(s). Here, we chose SIRIUS’s biomolecule structure database5 (MassBank and MoNA, drug_suspects, drug_TP).


, along with the imported databases Structure annotation


The structure annotation workfl ow in SIRIUS is a non-targeted workfl ow. SIRIUS identifi es the structure of a molecule by predicting its molecular fi ngerprint and searching for matches in a molecular structure database (Dührkop et al. 2015). Here, we demonstrate how SIRIUS detects precursor drugs as well as transformation products using custom databases (see Figure 1). We searched within the biomolecule structure database5


(part of SIRIUS) as well as the imported drug_suspects and drug_


TP databases. PubChem was used as fallback database: If the top hit in PubChem had a confi dence score (measure of a hit being correct (Hoffmann et al. 2022)) at least twice as high as the top hit from the selected databases, the search was expanded to include PubChem results.


pharmaceutical compounds that have marketing


authorisation in Luxembourg from the Ministry of Health and are therefore potentially in use domestically.


Spectral libraries: As spectral libraries we used MassBank2 and MoNA3 . Out of the drugs listed, 577 have reference spectra available in these spectral libraries.


Drug database (drug_suspects): A custom structure database for the pharmaceuticals from the drug list. This database contains 772 molecular structures. For stereoisomers, one representative is used; compounds with invalid SMILES notations were discarded.


Transformation product database (drug_TP): We generated transformation products using BioTransformer4


(Djoumbou-Feunang et al. 2019; Wishart et al. 2022) resulting


in over 1.06 million transformation products in total for 713 of the drugs from the drug list. These structures are not unique, as BioTransformer can produce the same transformation product for multiple drugs. The fi nal drug_TP contains 483 203 unique structures, corresponding to 22 804 different molecular formulas.


Custom structure databases and spectral libraries can be imported to SIRIUS via the Databases dialog. All imported spectra will automatically be used for spectral library matching during molecular formula annotation. A spectral library is also a molecular structure database and thus can also be selected for structure database search in SIRIUS.


In addition, SIRIUS automatically performs spectral library searches against all available spectral libraries whenever the molecular formula annotation workfl ow is applied. We searched in MassBank and MoNA. Spectral matching is based on the cosine score, calculated with squared peak intensities while ignoring the precursor peak. A drug was considered putatively identifi ed if the cosine similarity score of the best spectral match was at least 0.8 and a minimum of three peaks were matched.


Results Precursor drug screening


(see Figure 2). Notably, 17 of these otherwise unidentified compounds were confidently annotated by SIRIUS (confidence score >0.64)7


A total of 80 precursor drugs from the target drug list were reported as top hits using SIRIUS. Among these, 30 could not be matched through spectral library search6


with SIRIUS. This


highlights SIRIUS’s ability to detect compounds that would have been missed by spectral library searches while still integrating the power of spectral library search for suspect identification.


One drug (Aspirin, acetylsalicylic acid) was putatively identified through spectral library search but not detected as top hit in SIRIUS. The best SIRIUS match for this feature was monomethyl phthalate, which had an similarly high spectral library search score (both cosine similarities ~0.97) but is not included in the drug list. Monomethyl phthalate is a breakdown product of dimethyl phthalate, used in


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