
==== Front
Anal Chem
Anal Chem
ac
ancham
Analytical Chemistry
0003-2700
1520-6882
American Chemical Society

37713273
10.1021/acs.analchem.3c02419
Article
Identification of Xenobiotic Biotransformation Products Using Mass Spectrometry-Based Metabolomics Integrated with a Structural Elucidation Strategy by Assembling Fragment Signatures
Chen Yuan-Chih †
https://orcid.org/0000-0003-0722-0201
Wu Hsin-Yi ‡
Wu Wei-Sheng §
Hsu Jen-Yi †
Chang Chih-Wei †
Lee Yuan-Han §
https://orcid.org/0000-0001-8510-9870
Liao Pao-Chi *†
† Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
‡ Instrumentation Center, National Taiwan University, Taipei 106, Taiwan
§ Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
* Email: liaopc@mail.ncku.edu.tw. Tel.: 886-6-2353535 #5566. Fax: 886-6-2743748.
15 09 2023
26 09 2023
15 09 2024
95 38 1427914287
03 06 2023
01 09 2023
© 2023 The Authors. Published by American Chemical Society
2023
The Authors
https://creativecommons.org/licenses/by-nc-nd/4.0/ Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).

The identification of xenobiotic biotransformation products is crucial for delineating toxicity and carcinogenicity that might be caused by xenobiotic exposures and for establishing monitoring systems for public health. However, the lack of available reference standards and spectral data leads to the generation of multiple candidate structures during identification and reduces the confidence in identification. Here, a UHPLC-HRMS-based metabolomics strategy integrated with a metabolite structure elucidation approach, namely, FragAssembler, was proposed to reduce the number of false-positive structure candidates. biotransformation product candidates were filtered by mass defect filtering (MDF) and multiple-group comparison. FragAssembler assembled fragment signatures from the MS/MS spectra and generated the modified moieties corresponding to the identified biotransformation products. The feasibility of this approach was demonstrated by the three biotransformation products of di(2-ethylhexyl)phthalate (DEHP). Comprehensive identification was carried out, and 24 and 13 biotransformation products of two xenobiotics, DEHP and 4′-Methoxy-α-pyrrolidinopentiophenone (4-MeO-α-PVP), were annotated, respectively. The number of 4-MeO-α-PVP biotransformation product candidates in the FragAssembler calculation results was approximately 2.1 times lower than that generated by BioTransformer 3.0. Our study indicates that the proposed approach has great potential for efficiently and reliably identifying xenobiotic biotransformation products, which is attributed to the fact that FragAssembler eliminates false-positive reactions and chemical structures and distinguishes modified moieties on isomeric biotransformation products. The FragAssembler software and associated tutorial are freely available at https://cosbi.ee.ncku.edu.tw/FragAssembler/ and the source code can be found at https://github.com/YuanChihChen/FragAssembler.

Ministry of Science and Technology, Taiwan 10.13039/501100004663 MOST110-2113-M-006-014 Ministry of Science and Technology, Taiwan 10.13039/501100004663 MOST111-2113-M-006-011 document-id-old-9ac3c02419
document-id-new-14ac3c02419
ccc-price
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pmcHHumans are exposed to and absorb countless exogenous small-molecule compounds, namely, xenobiotics, in our daily lives. Most of these xenobiotics undergo reactions in the body to become biotransformation products.1 These products usually exhibit reduced pharmacological activity or enhanced toxicity and excretability.2 Some of them may cause accidental effects, such as toxicity or carcinogenicity. For example, 5-hydroxydiclofenac, a metabolite of diclofenac, is associated with hepatocyte cytotoxicity,3 and the reactive metabolites of polycyclic aromatic hydrocarbons (PAHs) have been confirmed to have carcinogenic properties.4 Since xenobiotic biotransformation products have crucial health impacts, the identification of these compounds is necessary and has attracted research attention in the context of drug development,5−7 the discovery of biomarkers for biomonitoring,8−10 and forensic toxicology.11,12

Multiple enzymes, such as cytochrome P450 (CYP) enzymes, UDP-glucuronosyltransferases (UGTs), sulfotransferases, and glutathione S-transferases,13 participate in biotransformation and convert xenobiotics into phase I and II biotransformation products. The highly complicated and dynamic biotransformation process generates distinct chemical entities during metabolic processes. To identify these unknown biotransformation products, high-resolution mass spectrometry (HRMS) coupled with ultrahigh-performance liquid chromatography (UHPLC) is the preferred analytical instrument. UHPLC-HRMS provides an omics-scale tool to simultaneously measure a vast number of chemical features in samples of interest. Using UHPLC-HRMS-based untargeted metabolomics, the features of biotransformation products can be comprehensively filtered from complicated backgrounds with diverse approaches, such as stable isotope tracing, mass defect filtering (MDF), time-dependent methods, concentration–response design, and similarities between the MS/MS spectra of the parent compound and its biotransformation products.9,10,14−18 However, the enormous number of unknown features also enhances the requirement for reliable structural identification approaches. The lack of availability of standards and reference MS/MS data for most xenobiotic biotransformation products limits the application of high-confidence structural identification methods, such as reference standard-based confirmation and library spectrum matching.19

Computational approaches provide another feasible strategy for identifying xenobiotic biotransformation products without standards or reference MS/MS data. The chemical structures of biotransformation product candidates can be predicted by in silico tools based on experimental data, expert knowledge, physicochemical properties, or information about metabolic enzymes.20In silico fragmentation software21−24 could further simulate the MS/MS spectra of these predicted chemical structures. If the simulated spectra could interpret most of the fragments presented in the experimental MS/MS spectra of unknown biotransformation products, then the corresponding predicted chemical structure was considered the most likely structure of the xenobiotic biotransformation product. Computational approaches exist to identify such unknown chemical structures of biotransformation products but often produce many false-positive candidates.25 The generation of false-positive candidates could be due to the existence of numerous undetermined biotransformation reactions and chemical structures.26,27 To improve confidence in biotransformation product identification, a strategy to eliminate false-positive candidates during computational identification is desperately needed.

Here, we proposed a UHPLC-HRMS-based metabolomics strategy integrated with a fragment signature-based metabolite structure elucidation approach, namely, FragAssembler, to reduce the number of false-positive xenobiotic biotransformation product candidates. The features in the LC–MS data were first filtered with an integrated metabolomics strategy and selected as biotransformation product candidates. By assembling the fragment signatures, the biotransformation-related fragments in the MS/MS data, the most likely biotransformation reaction, and the modified moiety of the parent compound for each candidate can be elucidated by FragAssembler. As a proof-of-concept, the identification of the biotransformation products of two xenobiotics, di(2-ethylhexyl) phthalate (DEHP) and 4′-Methoxy-α-pyrrolidinopentiophenone (4-MeO-α-PVP), in an S9 incubation experiment was performed with the proposed approach. Our results indicated that the proposed strategy could reduce the number of possibilities during xenobiotic biotransformation product identification.

Experimental Section

Chemicals and Reagents

DEHP (purity >98%) was purchased from Toronto Research Chemicals (TRC Inc.). Mono-2-ethylhexyl phthalate (MEHP, purity >98%), mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP, purity >98%), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP, purity >98%), and mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP, purity >98%) were purchased from Cambridge Isotope Laboratories, Inc. (Tewksbury, MA, USA). 4-MeO-α-PVP (purity ≥98%) and 4-Cl-α-PHP (purity ≥98%) were purchased from Cayman Chemicals (Ann Arbor, MI, USA). Pooled human liver S9 fraction (20 mg protein/mL, from 150 individual donors) and NADPH Regenerating System (Solution A and Solution B) were purchased from Corning GentestTM (Woburn, MA, USA). Acetonitrile (ACN, purity ≥99.9%) was purchased from J.T. Baker (Philipsburg, New Jersey, USA). The internal standards, N-(2-hydroxybenzoyl)pyrrolidine (N2P, purity 97%), 2-methyl-4′-(methylthio)-2-morpholino-propiophenone (2M4M, purity 98%), 2-benzyl-2-(dimethylamino)-4-morpholinobutyrophenone (2B2D, purity 97%), reserpine (purity ≥99%), ethyl 4-(dimethylamino)benzoate (E4D, purity ≥99%), ketoprofen (purity ≥99%), 4-methylumbelliferone (purity ≥99.9%), phenolphthalein (purity ≥98%), and diclofenac sodium salt (purity ≥99.9%), were obtained as powders from Sigma–Aldrich (St. Louis, MO, USA).

Human Liver S9 Incubation

The standard solution in MeOH (1 mg/mL) was diluted with sodium phosphate buffer (50 mM, pH 7.4) to generate 100, 50, 25, 10, 5, and 0 μg/mL solutions. The buffer-diluted standard solution was stored for no longer than 1 h before enzyme incubation. Pooled human liver S9 fractions (25 μL) were aliquoted into 1.6 mL tubes with 12.5 μL of NADPH Regenerating System A, 2.5 μL of NADPH Regenerating System B, standard stock solution, and sodium phosphate buffer. The final concentration of standard was 20, 10, 5, 2, 1, or 0 μM in the 250 μL incubation mixtures. Five replicates were tested for each concentration. Incubation was performed in a 37 °C water bath for 16 h. The final proportion of MeOH in the incubation mixture was less than 0.6%. Positive controls (40 μL of DEHP) were also prepared. Incubation was terminated by adding 250 μL of ACN in −20 °C and stored for 30 min. After centrifugation at 20,000g for 20 min at 4 °C, 150 μL of the supernatants were spiked with internal standards at 50 μg/mL in the final solution and subsequently stored at −20 °C for subsequent analysis.

UHPLC-HRMS Analysis

UHPLC-HRMS analyses were performed using a Dionex UltiMate 3000 UHPLC system connected to an Orbitrap Q Exactive Plus hybrid mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). Analyte separation for UHPLC was accomplished using a Phenomenex Luna Omega polar C18 column (100 × 2.1 mm, 1.6 μm, Phenomenex, Torrance, CA, USA) and eluent gradient (5% B for 0–1 min; 5–95% B in 1–21 min; 95% B in 21–22 min; 95–5% B in 22–24 min, and then returned to 2% B for 1 min for pressure equilibration). A 5 μL sample was loaded into the column, and HRMS was used in each analysis. The separation was operated at a flow rate of 250 μL/min and maintained at 45 °C. For the HRMS analysis, full scans mode with 100 to 1000 m/z scanning range was performed at 70,000 fwhm with automatic gain control (AGC) = 3 × 106 and the max injection time was 200 ms. Tandem mass spectrometry analysis was performed in parallel reaction monitoring (PRM) mode with resolution = 70,000; normalized collision energy = 20, 35, 60; AGC = 1 × 105 and max injection time = 200 ms. The mass-to-charge ratio values of each biotransformation product candidate are listed in the inclusion list of PRM analysis.

Filtering the Features of Biotransformation Product Candidates from S9 Incubation Data

The UHPLC-HRMS-based metabolomics strategy and FragAssembler for biotransformation product identification can be illustrated in 6 steps (Figure 1). After S9 incubation with multiple concentrations (Figure 1A) and UHPLC-HRMS analysis (Figure 1B), the analysis results were first processed with MS-DIAL software (version 5.06) to generate the alignment table of all S9 incubation samples. The alignment table was input into an R language-based program for m/z, abundance normalization, and MDF.18,28 The raw abundance of each feature in the alignment table was normalized by multiple internal standards.29 For the MDF (Figure 1C), the mass defect windows were set as ±50 mDa around the mass defect of the parent xenobiotic.30 A multiple-group comparison tool based on Spearman’s rank correlation coefficient and the Kruskal–Wallis test (K–W test) was utilized to filter the biotransformation product candidates.31 After concentration–response relationships between peak abundances and concentrations of the parent xenobiotics spiked in mixtures were assessed (Figure 1D), features with K–W test p values smaller than 0.001 and correlation coefficients higher than 0.85 were filtered as biotransformation product candidates. UHPLC-HRMS acquired the MS/MS data of the filtered biotransformation product candidates for the following FragAssembler calculation (Figure 1E).

Figure 1 Schematic illustration of the UHPLC-HRMS-based metabolomics strategy and FragAssembler for biotransformation product identification. (A) Multiple-group in vitro S9 incubation. (B) UHPLC-HRMS analysis and peak alignment. (C) MDF to remove features with mass defects over ±50 Da around the mass defect of the parent xenobiotic. (D) A multiple-group comparison tool was used to filter the biotransformation product candidates with concentration–response relationships in the S9 incubation data. (E) Collection of the MS/MS data of each biotransformation product candidate. (F) Assignment of the possible metabolic reactions of each biotransformation product candidate. (G) Assembly of the fragment signatures and annotation of the moiety with biotransformation. The FragAssembler software was used for the annotation of the moiety with biotransformation in steps F and G.

Use of FragAssembler To Reduce the Number of Biotransformation Product Candidates

The FragAssembler software was written in the R programming language; the code and tutorial are available at https://cosbi.ee.ncku.edu.tw/FragAssembler/. The source code can be found at https://github.com/YuanChihChen/FragAssembler. The flowchart of the FragAssembler workflow is illustrated in Figure S1, and the graphical user interface of FragAssembler is depicted in Figure S2. FragAssembler includes two steps, metabolic reaction assignment and annotation of the moiety with biotransformation on the structure of the parent xenobiotic (Figure 1F,G). For the first step, the known metabolic reactions list from a previous study14 (Table S1) was used to generate all possible metabolic reactions of the targeted parent xenobiotics. The number of successive reactions was set from 2 to 5. After generating the list of all possible biotransformation, the calculated m/z values corresponding to these reactions, Mp, were matched to the m/z values of biotransformation product candidates selected from the S9 incubation data set, Mc (Figure 2A). Once eq 11

was observed, the metabolic reaction of Mp was assigned to the matched biotransformation product candidate. The value z was set to allow measurement uncertainty, which was determined to be 5 ppm. If a biotransformation product candidate was assigned to more than one metabolic reaction, then the reaction with fewer reactions would be ranked first.

Figure 2 Workflow of FragAssembler for the structural identification of the xenobiotic biotransformation products. (A) biotransformation reaction assignment of all biotransformation product candidates. (B) Structural elucidation of fragments of the parent xenobiotic. (C) Characterization of fragment signatures in the MS/MS data of the biotransformation product. The lower spectra all belong to the parent xenobiotic, and the upper spectra are derived from the biotransformation products. (D) Assembly of the fragment structures of each signature and the proposed modified moiety on the structure of the parent xenobiotic (in color).

The concept of fragment signature identification in FragAssembler originated from previous studies.32,33 The details of the identification process are as follows: the structure of the parent xenobiotic fragments was first annotated by MS-FINDER software22 (version 3.52) with a fragment mass error lower than 5 ppm (Figure 2B). The elucidated fragments of parent xenobiotics and the corresponding structures were imported into the FragAssembler program automatically based on the fmcsR package.34 Then, a comparison between MS/MS spectra of the parent xenobiotic and each biotransformation product candidate was performed for the characterization of fragment signatures (Figure 2C). The possible subreactions were first listed based on the assigned metabolic reactions of each biotransformation product candidate. For example, the metabolic reaction “hydroxylation + ketone” has three possible subreactions for each fragment: “hydroxylation” (m/z + 15.9949), “ketone” (m/z + 13.9793), and “hydroxylation + ketone” (m/z + 29.9742). Next, FragAssembler screened the mass difference between each fragment in the MS/MS data of the biotransformation product candidate and parent compound. When the mass difference matched the mass of the subreaction calculated based on eq 2, the matched fragment pairs would be considered fragment signatures of the subreaction.2

where mcf and mpf in eq 2 represent the m/z value of fragments of biotransformation product candidate and parent xenobiotics, respectively. ΔM represents the mass change in the subreaction. The value u was set to allow for measurement uncertainty, which was determined to be 5 ppm.

As an output, FragAssembler generated modified moieties on the targeted xenobiotic structure by assembling the identified fragment signatures (Figure 2D). A modified moiety was defined as the moiety of the parent xenobiotic structure that could be modified during biotransformation. For each biotransformation product candidate, the structure of the parent xenobiotic fragments corresponding to the identified fragment signatures were intersected and assembled to yield the modified moieties and illustrated on the target xenobiotic structure. The modified moieties are labeled with colors. For example, the red moiety in Figure 2D was considered to have hydroxylation. The confidence of transformation product identification based on FragAssembler was level 3.19 Additional information, such as reference standard or reference spectrum data, can improve the identification confidence level to level 2 or 1. The structure-related calculation involved in FragAssembler was based on the ChemmineR package.35

Ranking possible biotransformation reactions is based on the results of FragAssembler. To reduce the number of possible biotransformation products, each assigned reaction was scored by FragAssembler to determine the most likely candidates. The confidence scores for each assigned metabolic reaction are calculated based on the following equation (eq 3):

where N is the number of all possible subreactions; factor T = [T1,T2......TN] represents all possible subreactions, each Tk = 1, and factor R = [R1,R2......RN] represents the number of explained subreactions. When subreaction k could be proposed as a modified moiety, Rk = 1; otherwise, Rk = 0. For example, modified moieties for both fragment modifications in Figure 2C (“+hydroxylation”) were found.

Therefore, the score of this candidate is the cosine of vectors (1) and (1) = 100%. The biotransformation reaction with a higher score and fewer successive reaction steps was considered to be the most likely reaction of the biotransformation product candidate.

Results and Discussion

Structural Identification of DEHP Biotransformation Products

The feasibility of the UHPLC-HRMS-based metabolomics strategy and FragAssembler was first demonstrated by the identification of 3 biotransformation products of DEHP, MEHHP, MEOHP, and MECPP. The biotransformation products of DEHP have been comprehensively studied in previously publications,36,37 and reference standards for these products were available. In this study, the identification of MEHHP, MEOHP, and MECPP was demonstrated as an example to verify the proposed approach. The primary biotransformation product of DEHP, MEHP, was substituted for DEHP as the parent xenobiotic during the following calculation because the parent xenobiotic DEHP and its biotransformation products are usually analyzed in different charge states.38 The standards of MEHP, MEHHP, MEOHP, and MECPP were first used to confirm that these 4 biotransformation products could be found in the S9 incubation data (Figure S3). Then, three biotransformation products in the S9 incubation data were identified by FragAssembler. The identification results are shown in Figure 3. The mass defects of the 3 biotransformation products were first calculated ([M – H]−). All 3 biotransformation products had mass defect values lower than 50 mDa (MEHHP: 6.1 mDa; MEOHP: 21.9 mDa; MECPP: 27.2 mDa), which indicated that the determined cutoff value was appropriate. For the following multiple-group comparison, the Spearman’s rank correlation coefficients and K–W test p values of these 3 biotransformation products were calculated. The correlation coefficients MEHHP, MEOHP, and MECPP were 0.99, 0.98, and 0.97, respectively, and the K–W test p values were all 3.28 × 10–5, which indicated that the determined criteria, a p value lower than 0.001 and a correlation coefficient higher than 0.85, successfully filtered these 3 biotransformation products (Figure 3A).

Figure 3 Demonstration of the UHPLC-HRMS-based metabolomics strategy and FragAssembler identification with 3 biotransformation products of DEHP. (A) Scatter plots of the normalized abundance of three biotransformation products and corresponding Spearman’s rank correlation coefficients and K–W test p values. The correlation coefficients of the three biotransformation products were 0.99, 0.98, and 0.97, and the p values were all 3.28 × 10–5. (B) Annotation of fragment signatures in MS/MS data of 3 biotransformation products using FragAssembler. The lower spectra all belong to the parent xenobiotic, and the upper spectra are from each biotransformation product. (C) Proposed biotransformation reaction moieties of 3 biotransformation products on the structure of MEHP. The colored moieties were considered to have modifications. The scores of the actual biotransformation reactions of the 3 metabolites were all 100%.

The identification process of FragAssembler in 3 biotransformation products is illustrated in Figure 3B,C. Each secondary metabolite generated one subreaction based on known metabolic reactions (Figure 3B). By comparing the MS/MS spectra of each of the 3 biotransformation products and MEHP, each fragment modification corresponded to 1 to 2 fragment signatures. For example, MEHHP has one subreaction, “hydroxylation”. The “hydroxylation” matched one fragment signature located at the fatty acid chain (Figure 3B). After assembling the fragment signatures, FragAssembler computed the proposed modified moieties of 3 biotransformation products on the targeted xenobiotic structure (Figure 3C). These labeled moieties can reasonably correspond to the real structures of 3 biotransformation products, which indicated that FragAssembler could generate accurate identification results for the 3 biotransformation products of DEHP.

A reduction in the number of false-positive results was also demonstrated in the identification of the 3 DEHP biotransformation products. For example, two possible biotransformation reactions of MEHHP were “hydroxylation” and “ketone + reduction” (Figure 4). The calculation result of the true reaction, “hydroxylation”, generates one subreaction, which can be assigned a modified moiety with the score = 100% (Figure 4A). On the other hand, the false-positive reaction candidate, “ketone + reduction”, yields three fragment modifications. However, only one of three modifications had modified moieties with a score of 58% (Figure 4B). This result implies that FragAssembler could diminish the score of false-positive reactions and eliminate the number of possible biotransformation products during structural identification.

Figure 4 false-positive biotransformation reaction was eliminated based on the scoring system. (A) Calculation result of the true reaction, “hydroxylation”. Two fragment modifications can be assigned proposed biotransformation moieties, and the score = 100%. (B) Calculation result of the false-positive reaction, “ketone + reduction”; only one of three fragment modifications can be assigned proposed biotransformation moieties, and the score = 58%. n.d., not determined.

Comprehensive Identification of DEHP Biotransformation Products

After confirming that the UHPLC-HRMS-based metabolomics strategy and FragAssembler could identify the 3 biotransformation products of DEHP, the proposed approach was applied to comprehensively identify DEHP biotransformation products in the S9 incubation result. A total of 12,064 features were detected in 30 S9 incubation samples in ESI negative mode. Of these, 2564 features passed MDF, with mass defects smaller than 50 mDa. The filtered features were then subjected to multiple-group comparisons, performed using the software tool provided in a previous study.31 A visualization plot of the multiple-group comparisons is shown in Figure S5. The cutoff values of the correlation coefficient (≥0.85) and K–W test p value (≤0.001) retained 81 biotransformation product candidates. Among these 81 candidates, 27 could be assigned more than one possible reaction in FragAssembler. Unreasonable reactions were determined based on the chemical characteristics of the parent xenobiotic. For example, the reaction assigned to one candidate result (m/z = 351.0968, RT = 8.1 min) was “carboxylic acid and 2 oxidative deamination and acetylation”. However, no amine is present in the structure of MEHP, which indicated that the proposed reaction assignment was unreasonable. After tandem mass spectrometry analysis, the MS/MS data of 24 reaction-assigned candidates (without MEHP) were obtained and input into FragAssembler for subsequent structural identification.

The identification results of 24 DEHP biotransformation products are listed in Table S2, and the corresponding scores are presented in Table S3 (showing scores of the top 1–10 reaction candidates only). All 24 biotransformation products were successfully assigned with modified moieties. Among them, 4 biotransformation products (M7 ∼ M10) were identified as involving hydroxylation of the ethylhexyl group (one of them, M7, was MEHHP). The multiple hydroxylation products of MEHP were reported in a published study of DEHP biotransformation.36 The other 5 biotransformation products (M17 ∼ M21) were identified as dihydroxylation products. The dihydroxylation of phthalate was also described in a reported publication investigating the biotransformation product identification for another phthalate, di(2-propylheptyl) phthalate (DPHP), and the dihydroxylation product was considered a sensitive exposure marker.9

Application of FragAssembler for the Identification of Biotransformation Products of Illicit Drugs

As a proof of concept, FragAssembler was applied to two additional examples: the 4-MeO-α-PVP S9 incubation data set, and the MS/MS spectra of cocaine and its biotransformation products in the MoNA online database. Information on the biotransformation products of illicit drugs is indispensable for forensic toxicologists, because such information can enhance drug toxicity39 research and expand the window to detect targeted drugs.40 For the 4-MeO-α-PVP data set, the data were from the reported study41 with the incubation conditions described in the experimental section. Among the 22,081 features in the alignment table, 5578 passed the MDF, and 52 of them were also filtered by multiple-group comparison (Figure S6). The following reaction assignments identified reasonable biotransformation reactions for 17 biotransformation product candidates. The FragAssembler identification results indicated that 13 candidates had the explainable modified moieties and were identified as biotransformation products of 4-MeO-α-PVP, which are listed in Table S4, and the corresponding scores are shown in Table S5 (only showing scores of the top 1–10 reaction candidates). Compared to the reported study using an in silico metabolite prediction tool,42 the UHPLC-HRMS-based metabolomics strategy and FragAssembler identified 3 additional biotransformation products of 4-MeO-α-PVP (not including phase II metabolites).

Among the 13 identified biotransformation products, M06 (m/z = 264.1601, RT = 6.38 min) and M07 (m/z = 264.1602, RT = 5.84 min) were assigned to the same biotransformation reaction, hydroxylation, and demethylation, with scores = 82% (rank 5) and 100% (rank 1), respectively. Fragment signature identification in both biotransformation products demonstrated the ability of FragAssembler to distinguish isomers. In the MS/MS data for M06, 3 identified fragment signatures were assembled moiety located on the pyrrole ring (Figure 5A). In contrast, the calculation result of M07 showed that the proposed modification site was on the benzene ring (Figure 5B). The identification results of M06 and M07 in the 4-MeO-α-PVP with the modified data set indicated that FragAssembler could distinguish isomeric biotransformation products based on the assembled fragment signature information.

Figure 5 FragAssembler distinguished the isomeric biotransformation products of 4-MeO-α-PVP. (A) FragAssembler identified 3 fragment signatures on MS/MS of M06. The assembly result of these fragment signatures revealed that hydroxylation occurred on the pyrrole ring. (B) The FragAssembler calculation result of M07 revealed the different location of hydroxylation compared to the result of M06.

For the MS/MS spectra from the online database, the MS/MS spectra of 10 parent compounds and their major biotransformation products were selected to demonstrate the capacities of FragAssembler. The calculation results are shown in Figure S6. The modified moiety labeled on each parent compound accurately responded to the truly biotransformed part of the structures. For example, the identification result of 6-monoacetylmorphine (Figure S6B), the FragAssembler assigned C3 as the deacetylated site instead of C6, identical to the structure of the 6-monoacetylmorphine standard recorded in the MoNA reference spectra database. The identification of 10 compounds in the MoNA database indicated that the developed FragAssembler could generally be applied to biotransformation study data sets from different sources.

FragAssembler Reduces the Number of Biotransformation Reaction and Chemical Structure Candidates

To demonstrate the feasibility of using FragAssembler, the number of structures assigned for 13 identified 4-MeO-α-PVP biotransformation products by the in silico prediction tool BioTransformer43,44 and the proposed FragAssembler (Figure 6) were compared. As illustrated in Figure 6A, MS1 signals of biotransformation product candidates in the S9 incubation sample were first filtered by both a mass defect filter and multiple-group comparison. The filtered candidates were structurally identified by two approaches, BioTransformer and FragAssembler. The combination of in vitro incubation and in silico prediction for the identification of biotransformation products is a classic approach and has been reported.42,45,46 The structure of the parent compound (4-MeO-α-PVP) was input into BioTransformer for in silico prediction with the following parameters: transformation, phase I (CYP450); CYP450 mode, combined; and number of reaction iterations to calculate, 3. The number of matched possible structures (mass error <5 ppm) of each filtered candidate is illustrated as a blue bar in Figure 6B, while the result from FragAssembler is shown as an orange bar. As shown in Figure 6, a reduction in the number of structure candidates in the FragAssembler results could be observed for 9 of 12 4-MeO-α-PVP biotransformation products (Figure 6, M01, M05, M06, M14, and M17 were not considered because one of the identification methods did not have results), representing a reduction of approximately 2.1 times in the number of candidates for the sum of the 12 biotransformation products (from 54 to 113).

Figure 6 FragAssembler reduced the number of potential chemical structures of biotransformation products in the BioTransformer in silico prediction result of 4-MeO-α-PVP. (A) Schematic illustration of the structure elucidation based on FragAssembler and BioTransformer. (B) The number of structure candidates of each biotransformation product was calculated by FragAssembler (orange bar) and BioTransformer (blue bar).

The decrease in the number of chemical structure candidates can be attributed to the elimination of false-positive biotransformation reactions and chemical structures. For instance, BioTransformer 3.0 predicted 14 chemical structure candidates for M12 (Figure 7). With FragAssembler, “hydroxylation” had the highest score (100%). The modified moiety information calculated by FragAssembler further indicated the reaction site of the hydroxylation of the benzene ring, which further removed 8 of the 9 remaining possible structures of M12. Overall, FragAssembler was found to reduce the number of candidate biotransformation reaction combinations and chemical structures, which facilitated the structural characterization of the biotransformation products. It is suggested that FragAssembler can be combined with in silico metabolism prediction tools, such as BioTransformer 3.0, in biotransformation product identification to identify the most likely chemical structures among the prediction results.

Figure 7 Reduction in the number of chemical structure candidates in the identification of M12 of 4-MeO-α-PVP by FragAssembler. The 9 candidate structures predicted by BioTransformer 3.0 can be reduced to 1 candidates by adding the information about the modified moieties provided by FragAssembler.

The necessity of high-quality MS/MS spectra for FragAssembler calculations is one noteworthy limitation. Comprehensive and high-quality MS/MS spectra of the parent compound and biotransformation products were recommended and used for FragAssembler calculation. Interference fragments derived from coeluted molecules in data-independent acquisition (DIA) MS/MS spectra may provide false-positive identification results (Figure S7).

Conclusions

In this study, a biotransformation product identification approach that integrated a UHPLC-HRMS-based metabolomics strategy and FragAssembler was proposed. FragAssembler located the truly modified moieties of 3 biotransformation products with reference standards, which indicated the feasibility of the proposed approach. The comprehensive identification results of DEHP, 4-MeO-α-PVP, and cocaine indicated that the proposed approach could be applied to discover biotransformation products of multiple xenobiotics. The combination of BioTransformer 3.0 and FragAssembler in the calculation of the 4-MeO-α-PVP data set successfully reduced the number of possible biotransformation products. In conclusion, we provided a strategy for the overall identification of xenobiotic biotransformation products and reduced false positives, which can expand our understanding of biotransformation.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.analchem.3c02419.Table S1: biotransformation reactions list and corresponding chemical composition and mass changes; Table S2: FragAssembler calculation result of 25 DEHP biotransformation product candidates; Table S3: Top 10 scores of 25 DEHP biotransformation product candidates; Table S4: FragAssembler calculation result of 17 4-MeO-α-PVP biotransformation product candidates; Table S5: Top 10 scores of 17 4-MeO-α-PVP biotransformation product candidates. Figure S1: Flowchart of the FragAssembler workflow; Figure S2: Graphical user interface of FragAssembler; Figure S3: Extract ion chromatograms of 4 DEHP metabolites and corresponded reference standards; Figure S4: Visualization plot of multiple-group comparison of DEHP S9 incubation experiment; Figure S5: Visualization plot of multiple-group comparison of 4-MeO-α-PVP S9 incubation experiment; Figure S6: FragAssembler calculation results of 10 parent drugs and corresponding biotransformation products from online database; Figure S7: The interference fragments in DIA MS/MS spectra provide false-positive result in the identification of MEOHP (PDF)

Supporting data 1: feature tables of a dilution series from DEHP data set; Supporting data 2: MS/MS data from DEHP data set; Supporting data 3:10 examples from public spectral libraries (ZIP)

Supplementary Material

ac3c02419_si_001.pdf

ac3c02419_si_002.zip

The authors declare no competing financial interest.

Acknowledgments

This work was supported by the National Science and Technology Council, Taiwan [MOST110-2113-M-006-014 and MOST111-2113-M-006-011]. The authors gratefully acknowledge the use of ICP00401 and MS004000 equipment belonging to the Core Facility Center of National Cheng Kung University, and the National Taiwan University Consortia of Key Technologies, National Taiwan University Instrumentation Center, and the Metabolomics Core Facility, Scientific Instrument Center at Academia Sinica.
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References

Di L. The role of drug metabolizing enzymes in clearance. Expert Opin. Drug Metab. Toxicol. 2014, 10 (3 ), 379–393. 10.1517/17425255.2014.876006.24392841
Correia M. A. Drug biotransformation. Basic & clinical pharmacology 2018, 53.
Bort R. ; Ponsoda X. ; Jover R. ; Gómez-Lechón M. J. ; Castell J. V. Diclofenac toxicity to hepatocytes: a role for drug metabolism in cell toxicity. J. Pharmacol. Exp. Ther. 1999, 288 (1 ), 65–72.9862754
Stading R. ; Gastelum G. ; Chu C. ; Jiang W. ; Moorthy B. Molecular mechanisms of pulmonary carcinogenesis by polycyclic aromatic hydrocarbons (PAHs): Implications for human lung cancer. Semin. Cancer Biol. 2021, 76 , 3–16. 10.1016/j.semcancer.2021.07.001.34242741
Schadt S. ; Bister B. ; Chowdhury S. K. ; Funk C. ; Hop C. E. ; Humphreys W. G. ; Igarashi F. ; James A. D. ; Kagan M. ; Khojasteh S. C. A decade in the MIST: learnings from investigations of drug metabolites in drug development under the “metabolites in safety testing” regulatory guidance. Drug Metab. Dispos. 2018, 46 (6 ), 865–878. 10.1124/dmd.117.079848.29487142
Kirchmair J. ; Göller A. H. ; Lang D. ; Kunze J. ; Testa B. ; Wilson I. D. ; Glen R. C. ; Schneider G. Predicting drug metabolism: experiment and/or computation?. Nat. Rev. Drug Discov. 2015, 14 (6 ), 387–404. 10.1038/nrd4581.25907346
Shanu-Wilson J. ; Evans L. ; Wrigley S. ; Steele J. ; Atherton J. ; Boer J. biotransformation: Impact and application of metabolism in drug discovery. ACS Med. Chem. Lett. 2020, 11 (11 ), 2087–2107. 10.1021/acsmedchemlett.0c00202.33214818
Chen Y. C. ; Hsu J. F. ; Chang C. W. ; Li S. W. ; Yang Y. C. ; Chao M. R. ; Chen H. J. C. ; Liao P. C. Connecting chemical exposome to human health using high-resolution mass spectrometry-based biomonitoring: Recent advances and future perspectives. Mass Spectrom. Rev. 2022, e21805 10.1002/mas.21805.36062854
Hsu J.-F. ; Tien C.-P. ; Shih C.-L. ; Liao P.-M. ; Wong H. I. ; Liao P.-C. Using a high-resolution mass spectrometry-based metabolomics strategy for comprehensively screening and identifying biomarkers of phthalate exposure: Method development and application. Environ. Int. 2019, 128 , 261–270. 10.1016/j.envint.2019.04.041.31063951
Liu K. H. ; Lee C. M. ; Singer G. ; Bais P. ; Castellanos F. ; Woodworth M. H. ; Ziegler T. R. ; Kraft C. S. ; Miller G. W. ; Li S. Large scale enzyme based xenobiotic identification for exposomics. Nat. Commun. 2021, 12 (1 ), 5418 10.1038/s41467-021-25698-x.34521839
Wagmann L. ; Frankenfeld F. ; Park Y. M. ; Herrmann J. ; Fischmann S. ; Westphal F. ; Müller R. ; Flockerzi V. ; Meyer M. R. How to study the metabolism of new psychoactive substances for the purpose of toxicological screenings—A follow-up study comparing pooled human liver S9, HepaRG cells, and zebrafish larvae. Front. Chem. 2020, 8 , 539 10.3389/fchem.2020.00539.32766204
Richter L. H. ; Herrmann J. ; Andreas A. ; Park Y. M. ; Wagmann L. ; Flockerzi V. ; Müller R. ; Meyer M. R. Tools for studying the metabolism of new psychoactive substances for toxicological screening purposes–a comparative study using pooled human liver S9, HepaRG cells, and zebrafish larvae. Toxicol. Lett. 2019, 305 , 73–80. 10.1016/j.toxlet.2019.01.010.30682400
Testa B. ; Pedretti A. ; Vistoli G. Reactions and enzymes in the metabolism of drugs and other xenobiotics. Drug Discov. Today 2012, 17 (11–12 ), 549–560. 10.1016/j.drudis.2012.01.017.22305937
Delcourt V. ; Barnabé A. ; Loup B. ; Garcia P. ; André F. ; Chabot B. ; Trévisiol S. ; Moulard Y. ; Popot M.-A. ; Bailly-Chouriberry L. MetIDfyR: An open-source r package to decipher small-molecule drug metabolism through high-resolution mass spectrometry. Anal. Chem. 2020, 92 (19 ), 13155–13162. 10.1021/acs.analchem.0c02281.32924440
Huber C. ; Müller E. ; Schulze T. ; Brack W. ; Krauss M. Improving the Screening Analysis of Pesticide Metabolites in Human Biomonitoring by Combining High-Throughput In Vitro Incubation and Automated LC–HRMS Data Processing. Anal. Chem. 2021, 93 , 9149–9157. 10.1021/acs.analchem.1c00972.34161736
Wang L. ; Ye H. ; Sun D. ; Meng T. ; Cao L. ; Wu M. ; Zhao M. ; Wang Y. ; Chen B. ; Xu X. Metabolic pathway extension approach for metabolomic biomarker identification. Anal. Chem. 2017, 89 (2 ), 1229–1237. 10.1021/acs.analchem.6b03757.27983783
Takahashi M. ; Izumi Y. ; Iwahashi F. ; Nakayama Y. ; Iwakoshi M. ; Nakao M. ; Yamato S. ; Fukusaki E. ; Bamba T. Highly accurate detection and identification methodology of xenobiotic metabolites using stable isotope labeling, data mining techniques, and time-dependent profiling based on LC/HRMS/MS. Anal. Chem. 2018, 90 (15 ), 9068–9076. 10.1021/acs.analchem.8b01388.30024726
Shih C.-L. ; Liao P.-M. ; Hsu J.-Y. ; Chung Y.-N. ; Zgoda V. G. ; Liao P.-C. Identification of urinary biomarkers of exposure to di-(2-propylheptyl) phthalate using high-resolution mass spectrometry and two data-screening approaches. Chemosphere 2018, 193 , 170–177. 10.1016/j.chemosphere.2017.10.162.29131975
Schymanski E. L. ; Jeon J. ; Gulde R. ; Fenner K. ; Ruff M. ; Singer H. P. ; Hollender J. Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environ. Sci. Technol. 2014, 48 (4 ), 2097–8. 10.1021/es5002105.24476540
Kazmi S. R. ; Jun R. ; Yu M.-S. ; Jung C. ; Na D. In silico approaches and tools for the prediction of drug metabolism and fate: A review. Comput. Biol. Med. 2019, 106 , 54–64. 10.1016/j.compbiomed.2019.01.008.30682640
Allen F. ; Pon A. ; Wilson M. ; Greiner R. ; Wishart D. CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra. Nucleic Acids Res. 2014, 42 (W1 ), W94–W99. 10.1093/nar/gku436.24895432
Tsugawa H. ; Kind T. ; Nakabayashi R. ; Yukihira D. ; Tanaka W. ; Cajka T. ; Saito K. ; Fiehn O. ; Arita M. Hydrogen rearrangement rules: computational MS/MS fragmentation and structure elucidation using MS-FINDER software. Anal. Chem. 2016, 88 (16 ), 7946–7958. 10.1021/acs.analchem.6b00770.27419259
Dührkop K. ; Fleischauer M. ; Ludwig M. ; Aksenov A. A. ; Melnik A. V. ; Meusel M. ; Dorrestein P. C. ; Rousu J. ; Böcker S. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat. Methods 2019, 16 (4 ), 299–302. 10.1038/s41592-019-0344-8.30886413
Blaženović I. ; Kind T. ; Ji J. ; Fiehn O. Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Metabolites 2018, 8 (2 ), 31 10.3390/metabo8020031.29748461
Vermeulen R. ; Schymanski E. L. ; Barabási A.-L. ; Miller G. W. The exposome and health: Where chemistry meets biology. Science 2020, 367 (6476 ), 392 10.1126/science.aay3164.31974245
Gulde R. ; Meier U. ; Schymanski E. L. ; Kohler H.-P. E. ; Helbling D. E. ; Derrer S. ; Rentsch D. ; Fenner K. Systematic exploration of biotransformation reactions of amine-containing micropollutants in activated sludge. Environ. Sci. Technol. 2016, 50 (6 ), 2908–2920. 10.1021/acs.est.5b05186.26864277
Fenner K. ; Gao J. ; Kramer S. ; Ellis L. ; Wackett L. Data-driven extraction of relative reasoning rules to limit combinatorial explosion in biodegradation pathway prediction. Bioinformatics. 2008, 24 (18 ), 2079–2085. 10.1093/bioinformatics/btn378.18641402
Zhang H. ; Zhang D. ; Ray K. A software filter to remove interference ions from drug metabolites in accurate mass liquid chromatography/mass spectrometric analyses. J. Mass Spectrom. 2003, 38 (10 ), 1110–1112. 10.1002/jms.521.14595861
Bijlsma S. ; Bobeldijk L. ; Verheij E. R. ; Ramaker R. ; Kochhar S. ; Macdonald I. A. ; van Ommen B. ; Smilde A. K. Large-scale human metabolomics studies: A strategy for data (pre-) processing and validation. Anal. Chem. 2006, 78 (2 ), 567–574. 10.1021/ac051495j.16408941
Zhang H. ; Zhang D. ; Ray K. ; Zhu M. Mass defect filter technique and its applications to drug metabolite identification by high-resolution mass spectrometry. J. Mass Spectrom. 2009, 44 (7 ), 999–1016. 10.1002/jms.1610.19598168
Pan Y.-Y. ; Chen Y.-C. ; Chang W. C.-W. ; Ma M.-C. ; Liao P.-C. Visualization of statistically processed LC-MS-based metabolomics data for identifying significant features in a multiple-group comparison. Chemom. Intell. Lab. Syst. 2021, 210 , 104271 10.1016/j.chemolab.2021.104271.
Gangl E. ; Utkin I. ; Gerber N. ; Vouros P. Structural elucidation of metabolites of ritonavir and indinavir by liquid chromatography–mass spectrometry. J. Chromatogr. A 2002, 974 (1–2 ), 91–101. 10.1016/S0021-9673(02)01243-8.12458929
Cooper B. T. ; Yan X. ; Simón-Manso Y. ; Tchekhovskoi D. V. ; Mirokhin Y. A. ; Stein S. E. Hybrid search: a method for identifying metabolites absent from tandem mass spectrometry libraries. Anal. Chem. 2019, 91 (21 ), 13924–13932. 10.1021/acs.analchem.9b03415.31600070
Wang Y. ; Backman T. ; Horan K. ; Girke T. fmcsR: mismatch tolerant maximum common substructure searching in R. Bioinformatics 2013, 29 (21 ), 2792–2794. 10.1093/bioinformatics/btt475.23962615
Cao Y. ; Charisi A. ; Cheng L.-C. ; Jiang T. ; Girke T. ChemmineR: a compound mining framework for R. Bioinformatics 2008, 24 (15 ), 1733–1734. 10.1093/bioinformatics/btn307.18596077
Koch H. M. ; Preuss R. ; Angerer J. D. Di (2-ethylhexyl) phthalate (DEHP): human metabolism and internal exposure–an update and latest results 1. Int. J. Androl. 2006, 29 (1 ), 155–165. 10.1111/j.1365-2605.2005.00607.x.16466535
Wang Y. ; Zhu H. ; Kannan K. A review of biomonitoring of phthalate exposures. Toxics 2019, 7 (2 ), 21 10.3390/toxics7020021.30959800
Takatori S. ; Kitagawa Y. ; Kitagawa M. ; Nakazawa H. ; Hori S. Determination of di (2-ethylhexyl) phthalate and mono (2-ethylhexyl) phthalate in human serum using liquid chromatography-tandem mass spectrometry. J. Chromatogr. B 2004, 804 (2 ), 397–401. 10.1016/j.jchromb.2004.01.056.
Maurer H. H. Chemistry, pharmacology, and metabolism of emerging drugs of abuse. Ther. Drug Monit. 2010, 32 (5 ), 544–549. 10.1097/FTD.0b013e3181eea318.20683389
Chen P. ; Braithwaite R. ; George C. ; Hylands P. ; Parkin M. ; Smith N. ; Kicman A. The poppy seed defense: a novel solution. Drug Test. Anal. 2014, 6 (3 ), 194–201. 10.1002/dta.1590.24339374
Wu H.-Y. ; Chen Y.-C. ; Hsu J.-F. ; Lu H.-T. ; Pan Y.-Y. ; Ma M.-C. ; Liao P.-C. Untargeted Metabolomics Analysis assisted by Signal Selection for Comprehensively Identifying Metabolites of New Psychoactive Substances: 4-MeO-α-PVP as an Example. J. Food Drug Anal. 2023, 31 (1 ), 137 10.38212/2224-6614.3447.37224557
Ellefsen K. N. ; Wohlfarth A. ; Swortwood M. J. ; Diao X. ; Concheiro M. ; Huestis M. A. 4-Methoxy-α-PVP: in silico prediction, metabolic stability, and metabolite identification by human hepatocyte incubation and high-resolution mass spectrometry. Forensic Toxicol. 2016, 34 (1 ), 61–75. 10.1007/s11419-015-0287-4.26793277
Djoumbou-Feunang Y. ; Fiamoncini J. ; Gil-de-la-Fuente A. ; Greiner R. ; Manach C. ; Wishart D. S. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J. Cheminform. 2019, 11 (1 ), 2 10.1186/s13321-018-0324-5.30612223
Wishart D. S. ; Tian S. ; Allen D. ; Oler E. ; Peters H. ; Lui V. W. ; Gautam V. ; Djoumbou-Feunang Y. ; Greiner R. ; Metz T. O. BioTransformer 3.0—a web server for accurately predicting metabolic transformation products. Nucleic Acids Res. 2022, 50 (W1 ), W115–W123. 10.1093/nar/gkac313.35536252
Carlier J. ; Berardinelli D. ; Montanari E. ; Sirignano A. ; Di Trana A. ; Busardò F. P. 3F-α-pyrrolydinovalerophenone (3F-α-PVP) in vitro human metabolism: Multiple in silico predictions to assist in LC-HRMS/MS analysis and targeted/untargeted data mining. J. Chromatogr. B 2022, 1193 , 123162 10.1016/j.jchromb.2022.123162.
Montesano C. ; Vannutelli G. ; Fanti F. ; Vincenti F. ; Gregori A. ; Rita Togna A. ; Canazza I. ; Marti M. ; Sergi M. Identification of MT-45 metabolites: in silico prediction, in vitro incubation with rat hepatocytes and in vivo confirmation. J. Anal. Toxicol. 2017, 41 (8 ), 688–697. 10.1093/jat/bkx058.28985323
