A method for screening and identifying metabolites of bisphenols

Abstract

This invention relates to the field of environmental chemistry and toxicology analysis technology, and particularly to a method for identifying bisphenol metabolites, comprising the following steps: S1: establishing a list of potential metabolites; S2: acquiring high-resolution mass spectrometry data of biological samples; S3: automated screening and identification: automatically processing the mass spectrometry data obtained in step S2 using mass spectrometry analysis software, including precursor ion identification and fragment ion matching analysis, comparing the analysis results with the list of potential metabolites generated in step S1, and screening out metabolites that match the mass spectrometry data as candidate identification results; this invention achieves rapid and accurate identification of target metabolites from massive amounts of mass spectrometry information through the steps of computational simulation to predict potential metabolites, obtaining real mass spectrometry data through animal exposure experiments, and automated software comparison and screening.

Description

Technical Field

[0001] This invention relates to the field of environmental chemistry and toxicology analysis technology, and in particular to an identification method for screening metabolites of bisphenolic substances. Background Technology

[0002] Bisphenols are a class of endocrine disruptors widely used in the production of plastic products such as polycarbonate and epoxy resin. Among them, the use of bisphenol A (BPA) is restricted due to its potential health risks, which has led to the widespread use of various alternatives such as bisphenol S (BPS), bisphenol F (BPF), and bisphenol AF (BPAF). However, an increasing number of toxicological studies have shown that these alternatives may have biotoxicity comparable to or even higher than that of BPA.

[0003] Accurately assessing human exposure to blood lipids (BPs) is a prerequisite for health risk assessment. Currently, population-based exposure monitoring mainly relies on measuring the concentration of BP parent compounds in biological samples (such as serum and urine). However, this assessment strategy has significant drawbacks: after entering the body, BPs undergo phase I metabolism (such as hydroxylation and dealkylation) under the action of liver cytochrome P450 enzymes, and the resulting active intermediates can be further excreted through phase II metabolism (such as glucuronidation and sulfation) to form water-soluble conjugates. If BPs are rapidly metabolized in vivo, the concentration of their metabolites may be much higher than that of the parent compounds. Measuring only the parent compounds will severely underestimate the actual exposure level, which is likely a major reason for the contradictory phenomenon of high BP concentrations in the environment but low concentrations in population biomonitoring. Therefore, systematically identifying the metabolic transformation products of BPs in vivo and screening reliable exposure biomarkers are crucial for achieving accurate exposure assessment.

[0004] Currently, the identification of metabolites mainly relies on purchasing or synthesizing standards for comparison and confirmation. However, BPs have a wide variety of possible metabolites, and obtaining standards for all potential metabolites is costly and impractical. In addition, traditional non-targeted screening relies on manual analysis of massive amounts of high-resolution mass spectrometry data, which is labor-intensive, inefficient, and easily influenced by subjective experience, making it difficult to systematically and unbiasedly discover all relevant metabolites. Although there are some metabolism prediction software (such as BioTransformer) and mass spectrometry analysis tools (such as CFM-ID), existing technologies have not yet provided a mature solution for systematically integrating these tools into an efficient and reliable workflow specifically for solving the identification challenges of metabolites of specific compounds like BPs.

[0005] Therefore, to address the above problems, a method for screening bisphenol metabolites is proposed. This method involves a process of predicting potential metabolites through computational simulation, obtaining real mass spectrometry data through animal exposure experiments, and comparing and screening with automated software. This process enables the rapid and accurate identification of target metabolites from massive amounts of mass spectrometry information. Summary of the Invention

[0006] To overcome the problems of narrow coverage, strong dependence on standards, and low efficiency of manual analysis in existing methods for identifying bisphenol metabolites.

[0007] The technical solution of this invention is: a method for identifying bisphenol metabolites, comprising the following steps: S1: Establish a potential metabolite list: Based on the molecular structure of the bisphenol to be tested, predict its possible phase I and phase II metabolic reactions using metabolic simulation software, and generate a list containing molecular information of the potential metabolites of the bisphenol. S2: Obtain high-resolution mass spectrometry data of biological samples: pre-process serum, urine and / or fecal samples collected from experimental animals exposed to the bisphenols, and analyze them using liquid chromatography-high-resolution mass spectrometry to obtain mass spectrometry data containing information on the parent ion and fragment ions. S3: Automated screening and identification: The mass spectrometry data obtained in step S2 is automatically processed using mass spectrometry analysis software, including precursor ion identification and fragment ion matching analysis. The analysis results are compared with the potential metabolite list generated in step S1, and metabolites that match the mass spectrometry data are selected as candidate identification results.

[0008] Preferably, in step S1, the metabolic simulation software includes a first metabolic simulation software and a second metabolic simulation software. The first metabolic simulation software is used to predict the phase I metabolic reaction of the bisphenol, and the second metabolic simulation software is used to predict the phase II metabolic reaction.

[0009] Preferably, the first metabolic simulation software is EAWAG-BBD, and the second metabolic simulation software is BioTransformer 3.0.

[0010] Preferably, the bisphenol substance to be tested is selected from at least one of bisphenol A, bisphenol AF, bisphenol S and bisphenol F.

[0011] Preferably, in step S2, the experimental animal is a rodent; the exposure method is oral gavage, the exposure concentration is 21.6 mg / kg / day to 307 mg / kg / day, and the exposure period is 28 days.

[0012] Preferably, in step S2, the pretreatment includes: for serum samples, precipitating proteins with an organic solvent; for urine samples, diluting with an organic solvent and then filtering; and for fecal samples, extracting with an organic solvent and then filtering.

[0013] Preferably, in step S2, the liquid chromatography-high resolution mass spectrometry instrument is an ultra-high performance liquid chromatography-tandem quadrupole / orbit trap high resolution mass spectrometer, and the mass spectrometry scan adopts a full scan-data dependent two-stage scan mode in positive ion mode and / or negative ion mode.

[0014] Preferably, in step S3, the mass spectrometry analysis software includes Mass Frontier software and CompetitiveFragmentation Modeling-ID software. The automated processing includes simulating and scoring the fragment ion spectrum using the CompetitiveFragmentation Modeling-ID software, and matching the high-scoring precursor ion information with the potential metabolite list using the Mass Frontier software.

[0015] Preferably, the potential metabolite list includes at least one set of the following metabolite information: 4 metabolites of bisphenol S, 9 metabolites of bisphenol AF, 7 metabolites of bisphenol A, and 6 metabolites of bisphenol F.

[0016] Preferably, serum, urine, and fecal samples of the experimental animals are collected on days 7, 14, 21, and 28 during the exposure period.

[0017] The beneficial effects of this invention are: This invention utilizes metabolic simulation software for theoretical prediction, constructing a comprehensive database of potential metabolites at low cost and overcoming the bottleneck of reliance on standards. It obtains real data through rigorously designed animal experiments, providing a high-quality mass spectrometry foundation for identification. Intelligent comparison is achieved through mass spectrometry analysis software, automatically linking the predicted list with experimental data to quickly identify candidate metabolites, improving identification efficiency and coverage. This method not only significantly reduces the cost and time required for metabolite identification but also, through systematic screening, can discover metabolites that have not been reported or are difficult to detect using traditional methods. Therefore, it provides strong technical support and a more comprehensive compound list for the study of the in vivo metabolic mechanisms of bisphenol A, the screening of exposure biomarkers, and subsequent precise population exposure assessment and health risk evaluation. Attached Figure Description

[0018] Figure 1 The diagram shown is a schematic flowchart of the identification method for screening bisphenol metabolites according to the present invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] Example 1 Please see Figure 1 This invention provides an embodiment: a method for identifying bisphenol metabolites, comprising the following steps: S1: Establish a potential metabolite list: Based on the molecular structure of the bisphenol to be tested, predict its possible phase I and phase II metabolic reactions using metabolic simulation software, and generate a list containing molecular information of potential metabolites of the bisphenol. S2: Obtaining high-resolution mass spectrometry data of biological samples: Pre-processing serum, urine and / or fecal samples collected from experimental animals exposed to bisphenols, and analyzing them using liquid chromatography-high-resolution mass spectrometry to obtain mass spectrometry data containing information on the parent ion and fragment ions. S3: Automated screening and identification: The mass spectrometry data obtained in step S2 is automatically processed using mass spectrometry analysis software, including precursor ion identification and fragment ion matching analysis. The analysis results are compared with the potential metabolite list generated in step S1, and metabolites that match the mass spectrometry data are selected as candidate identification results.

[0021] Furthermore, in step S1, the metabolic simulation software includes a first metabolic simulation software and a second metabolic simulation software. The first metabolic simulation software is used to predict the phase I metabolic reaction of bisphenol substances, and the second metabolic simulation software is used to predict the phase II metabolic reaction. Since the enzymatic reaction mechanisms of phase I and phase II metabolism are different, using software specifically designed for different metabolic stages for prediction can more comprehensively and accurately cover all possible metabolic pathways, avoid the prediction blind spots of a single software, and thus establish a more complete database of potential biomarkers.

[0022] Furthermore, the first metabolic simulation software is EAWAG-BBD, and the second metabolic simulation software is BioTransformer 3.0. EAWAG-BBD is based on a microbial degradation reaction rule library and has unique advantages in predicting phase I reactions such as oxidation and reduction. BioTransformer 3.0 integrates human metabolomics databases and machine learning models and is particularly good at predicting phase II binding reactions such as glucuronidation and sulfation. Using the two together can leverage their respective strengths to achieve simulation of the entire metabolic pathway of bisphenols from primary oxidation to final binding and excretion.

[0023] Furthermore, the bisphenols to be tested are selected from at least one of bisphenol A, bisphenol AF, bisphenol S, and bisphenol F. The method of the present invention is universal and can be applied not only to BPA, which has been extensively studied, but also to mainstream alternatives (BPS, BPF, BPAF) for which current population exposure monitoring data is lacking but their environmental detection rate is increasing. It provides a unified technical tool for comprehensively assessing the health risks of the bisphenol family.

[0024] Furthermore, in step S2, the experimental animals were rodents; the exposure method was oral gavage, with exposure concentrations ranging from 21.6 mg / kg / day to 307 mg / kg / day, and an exposure period of 28 days; oral gavage was used to simulate the most common dietary exposure route in humans; setting a subchronic exposure period (28 days) allows the metabolic process to reach a stable state, which is conducive to the full generation and accumulation of metabolites; the concentration range was set based on previous toxicological experiments, which can ensure the generation of detectable metabolic signals while avoiding acute toxicity interference, and obtain a representative in vivo metabolic profile.

[0025] Furthermore, in step S2, the pretreatment includes: for serum samples, protein precipitation is performed using organic solvents; for urine samples, dilution is performed using organic solvents followed by filtration; for fecal samples, extraction is performed using organic solvents followed by filtration. Different pretreatment methods are used for the three different matrices: serum, urine, and feces. Serum is treated with protein precipitation to rapidly remove large molecular interferences; urine, due to its relatively simple matrix, is treated with dilution filtration to maximize the retention of target substances; feces are treated with solid-liquid extraction to release metabolites from the matrix. This differentiated treatment strategy can specifically improve the extraction efficiency of target metabolites in different samples and reduce matrix inhibition effects.

[0026] Furthermore, in step S2, the liquid chromatography-high resolution mass spectrometry (LC-HDMS) instrument is an ultra-high performance liquid chromatography-tandem quadrupole / orbittrap HDMS instrument. The mass spectrometry scan adopts a full scan-data-dependent two-stage scan mode in positive ion mode and / or negative ion mode. Among them, the high resolution (e.g., 70,000 FWHM) and high quality precision (<5ppm) provided by the orbital trap mass spectrometry can accurately determine the elemental composition of the parent ion and fragment ions for the identification of unknown metabolites. The full scan-data-dependent two-stage scan (Full MS-ddMS2) mode can simultaneously acquire the precise mass numbers of all ions and their corresponding fragment spectra in a single injection, without the need for a preset target list, which is suitable for non-targeted screening and ensures the integrity of the data and the possibility of retrospective analysis.

[0027] Furthermore, in step S3, the mass spectrometry analysis software includes Mass Frontier software and Competitive Fragmentation Modeling-ID software. The automated processing includes simulating and scoring fragment ion spectra using Competitive Fragmentation Modeling-ID software, and matching high-scoring precursor ion information with a potential metabolite list using Mass Frontier software. Specifically, CFM-ID software employs a competitive fragmentation model, which can simulate the mass spectrometric fragmentation behavior of compounds based on chemical rule theory and match and score experimental spectra. This scoring can serve as a quantitative indicator of identification reliability. Mass Frontier software can efficiently manage the large potential metabolite list and experimental mass spectrometry data, and achieve automated batch comparison. The synergy of these two methods transforms tedious manual spectrum analysis into an efficient computer-automated process, improving identification throughput and objectivity.

[0028] Furthermore, the potential metabolite list includes at least one set of metabolite information: four metabolites of bisphenol S, nine metabolites of bisphenol AF, seven metabolites of bisphenol A, and six metabolites of bisphenol F. This preferred embodiment clarifies the specific number of metabolite types that can be systematically identified using the method of this invention for several typical BPs. This demonstrates the productivity of this method in practical applications; for example, it successfully predicted and screened nine metabolites of BPAF, far exceeding the information obtained by traditional methods that only focus on the parent compound, providing a rich candidate pool for subsequent selection of the best exposure biomarkers.

[0029] Furthermore, serum, urine, and fecal samples were collected from experimental animals on days 7, 14, 21, and 28 during the exposure period. Dynamic collection of various biological matrices at different time points (serum reflects internal exposure load, while urine and feces reflect clearance and excretion) allows for the study of metabolite generation kinetics, accumulation trends, and main excretion pathways. This helps to distinguish between transient and stable metabolites, thereby screening out metabolites that remain stable during exposure and are more suitable as biomarkers for long-term exposure.

[0030] Through the above steps, the method of this invention utilizes metabolic simulation software for theoretical prediction, constructing a comprehensive database of potential metabolites at low cost and overcoming the bottleneck of reliance on standards. It obtains real data through rigorously designed animal experiments, providing a high-quality mass spectrometry foundation for identification. Intelligent comparison is achieved through mass spectrometry analysis software, automatically linking the predicted list with experimental data to quickly identify candidate metabolites, improving identification efficiency and coverage. This method not only significantly reduces the cost and time required for metabolite identification but also, through systematic screening, can discover metabolites that have not been reported or are difficult to detect using traditional methods. Therefore, it provides strong technical support and a more comprehensive compound list for the study of the in vivo metabolic mechanisms of bisphenol A substances, the screening of exposure biomarkers, and subsequent precise population exposure assessment and health risk evaluation.

[0031] Example 2 Optionally, this embodiment demonstrates a complete process for systematically identifying BPAF and BPS metabolites in rats using the method of the present invention.

[0032] S1: Establish a potential metabolite inventory First, the target analytes were identified as bisphenol AF (BPAF) and bisphenol S (BPS); then, two metabolic simulation software programs were used for prediction. Phase I metabolic prediction: The SMILES molecular formulas of BPAF and BPS are input into the EAWAG-BBD prediction software; the software simulates reactions such as hydroxylation and defluorination based on a known rule library of phase I metabolic reactions in microorganisms and mammals; for example, for BPAF, it is predicted that monohydroxylation and dihydroxylation reactions may occur on its benzene ring.

[0033] Phase II metabolism prediction: The parent molecular structures of BPAF and BPS, as well as the SMILES formulas of the Phase I metabolites (such as hydroxylated BPAF) predicted in the previous step, were input into the BioTransformer 3.0 software. This software mainly predicts Phase II binding reactions such as glucuronidation and sulfation based on the enzyme reaction rules in the human metabolomics database.

[0034] By integrating the prediction results of the two software programs, two independent "potential metabolite lists" are generated. Each list contains the molecular formula, precise molecular weight, possible chemical structure (denoted as SMILES or InChIKey), and the predicted metabolic reaction type for each predicted metabolite. After integration, a potential metabolite list for BPAF containing products from nine different metabolic pathways (named BPAF-T1 to BPAF-T9) and a potential metabolite list for BPS containing products from four different metabolic pathways (named BPS-T1 to BPS-T4) are obtained.

[0035] S2: Acquiring high-resolution mass spectrometry data from biological samples (1) Animal exposure and sample collection: Healthy adult male SD rats (weighing 250-300g) were selected as experimental animals and randomly divided into three groups: control group (corn oil), BPAF exposure group, and BPS exposure group, with 6 rats in each group. The exposure groups were administered BPAF at 234 mg / kg / day and BPS at 216 mg / kg / day by gavage, respectively, while the control group was given an equal volume of corn oil. The exposure was carried out for 28 consecutive days. Before exposure on days 7, 14, 21, and 28, the rats were placed in metabolic cages and urine and feces were collected for 12 hours. After exposure ended on day 28, the rats were fasted for 12 hours and blood was collected via tail vein after anesthesia. The blood samples were anticoagulated with lithium heparin and centrifuged at 4°C and 4000 rpm for 10 minutes to separate serum. All samples were immediately flash-frozen in liquid nitrogen after collection and then transferred to an ultra-low temperature freezer at -80°C for storage.

[0036] (2) Sample pretreatment: Serum pretreatment: Take 50 μL of the mixed serum sample from each time point, dilute with 150 μL of pure water, and then add 600 μL of acetonitrile (containing 0.1% formic acid) for protein precipitation; vortex for 3 minutes, sonicate for 10 minutes, and then centrifuge at 4°C and 12000 rpm for 15 minutes; take the supernatant and filter it through a 0.22 μm microporous membrane to obtain the test solution.

[0037] Urine pretreatment: Take 50 μL of the mixed urine sample from each time point and dilute it with 800 μL of methanol / water (70 / 30, v / v) solution; vortex mix for 3 minutes and centrifuge at 4°C and 12000 rpm for 15 minutes; take the supernatant and filter it through a 0.22 μm microporous membrane to obtain the test solution.

[0038] Fecal pretreatment: Take 50 mg of equal amounts of mixed and homogenized fecal samples from each time point and add 2 mL of methanol / water (70 / 30, v / v) solution. Vortex for 2 minutes, sonicate for 10 minutes, and then centrifuge at 4°C and 12000 rpm for 15 minutes. Filter the supernatant through a 0.22 μm microporous membrane to obtain the test solution.

[0039] (3) Instrumental analysis: The analysis was performed using an ultra-high performance liquid chromatography-quadrupole-electrostatic field track trap high-resolution mass spectrometer. Chromatographic conditions: Hypersil GOLD C18 column (100 × 2.1 mm, 1.9 μm); column temperature 40 °C; flow rate 0.3 mL / min; injection volume 5 μL; mobile phase A was an aqueous solution containing 0.1% formic acid, and mobile phase B was methanol; gradient elution program was as follows: 0–5 min, 2% B to 20% B; 5–12 min, 20% B to 95% B; 12–16 min, maintain 95% B; 16.1–20 min, return to 2% B and equilibrate.

[0040] Mass spectrometry conditions: Electrospray ionization (ESI) source was used, with positive and negative ion modes introduced separately; spray voltage: +4.0 kV (positive mode), -3.0 kV (negative mode); sheath gas, auxiliary gas, and purge gas flow rates were 35, 10, and 0 arb, respectively; ion transfer tube temperature was 320 °C, and auxiliary heater temperature was 350 °C; Full MS-ddMS2 scanning mode was used: full scan (Full MS) range m / z 50-750, resolution 70,000 FWHM; data-dependent secondary scan (ddMS2) trigger intensity threshold was set to 1e5, fragmentation mode was high-energy collision-induced dissociation, isolation window was 0.4 m / z, and fragmentation energy was 50% of the stepped normalized collision energy (Stepped NCE).

[0041] S3: Automated Screening and Identification (1) Data preprocessing: The raw mass spectrometry data collected is imported into Compound Discoverer 3.3 software for preliminary processing, including peak extraction, alignment, background subtraction, etc., to generate a list of compounds containing the retention time of each characteristic peak, the exact mass number of the parent ion, isotope distribution and multi-level fragment ion information.

[0042] (2) Theoretical spectral simulation and matching: Import the BPAF and BPS "potential metabolite list" generated in step S1 into the Mass Frontier 8.0 software library.

[0043] The Competitive Fragmentation Modeling for Metabolite Identification (CFM-ID) tool was used. For each predicted metabolite in the list, CFM-ID simulated its theoretical fragment ion spectrum under HCD conditions based on its chemical structure using a competitive fragmentation model, and scored each theoretical fragment ion.

[0044] The MS2 experimental spectra of the list of experimental compounds obtained in step (1), especially those ion peaks that appear in the exposure group but are missing in the control group, are automatically compared with the theoretical spectrum library generated by CFM-ID. The comparison process will calculate a fragment ion matching score, which takes into account the mass deviation between experimental and theoretical fragment ions (usually required to be <5 ppm), the relative abundance matching degree of fragment ions, and the presence or absence of key diagnostic fragments.

[0045] (3) Results screening: Set the matching score threshold in the Mass Frontier software (e.g., >80%); the software will automatically screen out those compounds whose experimental spectra match the theoretical spectra highly and associate them with the entries in the "Potential Metabolites List" to output the "Candidate Metabolites" list.

[0046] Using the method described in this embodiment, nine metabolites (BPAF-T1 to T9) were successfully screened from biological samples of rats exposed to BPAF, and four metabolites (BPS-T1 to T4) were screened from rats exposed to BPS, confirming the effectiveness of this method in systematically identifying bisphenol metabolites.

[0047] Example 3 Optionally, this embodiment illustrates how the method of the present invention can be applied to the identification of metabolites of the classic bisphenol A (BPA), focusing on the application of different exposure concentrations and software combinations.

[0048] The steps in this embodiment are basically the same as those in embodiment 2, except that: S1. Establish the list: The target substance is bisphenol A (BPA); use BioTransformer 3.0 software to perform phase I and phase II metabolism prediction simultaneously (this can be used alone in this scenario); predict and generate a list of potential BPA metabolites (BPA-T1 to BPA-T7) containing products of 7 different metabolic pathways.

[0049] S2 Animal Experiment: The gavage dose for the BPA-exposed group was 307 mg / kg / day; the sample collection and pretreatment steps were the same as in Example 2.

[0050] S3 Screening and Identification: Using the same CFM-ID and Mass Frontier software workflow as in Example 2, the experimental mass spectrometry data were automatically compared with the list of potential metabolites of BPA.

[0051] Using this method, seven metabolites (BPA-T1 to T7) were successfully identified from BPA-exposed rat samples, including various types of products such as hydroxylation, glucuronidation, and sulfation, verifying the applicability of this method to traditional bisphenol substances.

[0052] Example 4 Optionally, this embodiment focuses on how to use the method of the present invention to process complex matrix (feces) samples and perform more refined data screening and analysis.

[0053] This embodiment uses bisphenol F (BPF) as the target compound. Steps S1 (using EAWAG-BBD and BioTransformer 3.0 to predict and obtain a list of BPF products containing six compounds) and S2 are largely the same as in Example 2. The main differences and refinements are as follows: Optimization of fecal pretreatment in S2: To address the complex matrix and numerous interfering substances in feces, a solid-phase extraction purification step is added after the extraction step. Specifically, the supernatant after centrifugation is loaded onto a pre-activated Oasis HLB solid-phase extraction column, and impurities are removed by sequential rinsing with water and 5% methanol aqueous solution. Finally, the target analyte is eluted with methanol. The eluent is concentrated to near dryness by nitrogen blowing, then reconstituted with 100 μL of the initial mobile phase, filtered through a 0.22 μm filter membrane, and injected. This step can more effectively remove interferences such as pigments and lipids, improving the signal-to-noise ratio of mass spectrometry detection.

[0054] Advanced strategies for automated screening in S3: Hierarchical screening in Mass Frontier software: Primary screening (mass number filtering): First, based on the precise molecular weight of each product in the potential metabolite list, calculate its possible ionic forms (e.g., [MH]). - , [M+FA-H] - , [M+Na] + The precise m / z values ​​of (etc.) were obtained; in the experimental data, all precursor ions with a mass deviation within the range of 5 ppm were first screened out.

[0055] Secondary screening (isotope pattern matching): For ions selected in the primary screening, check whether their isotope distribution (e.g., for chlorine / fluorine-containing BPF metabolites) matches theoretical predictions.

[0056] Level 3 screening (deep matching of fragment ions): Using CFM-ID, not only is overall spectral matching and scoring performed, but special attention is paid to diagnostic fragment ions; for example, for glucuronic acid conjugates, fragments characterizing the loss of glucuronic acid groups must be present (e.g., m / z 175.0248 [C6H7O6)). - (for negative mode); for sulfate conjugates, the presence of HSO4 is required. - (m / z 96.9601) Characteristic fragments. Set these key fragment ions as "must match".

[0057] Level 4 screening (judgment of the rationality of chromatographic behavior): auxiliary judgment is made by combining the retention time pattern of reversed-phase chromatography; generally, metabolites that are bound by phase II (such as glucuronide) have increased polarity, and their retention time should be earlier than their corresponding phase I metabolites or parent compounds.

[0058] Through the above-described refined four-step automated screening strategy, this embodiment successfully and reliably identified six metabolites (BPF-T1 to T6) from fecal samples and other samples of rats in the BPF-exposed group, demonstrating the advantages of this method in the analysis of complex biological matrices and in improving the reliability of identification results.

[0059] In the above embodiments 1-4 of the present invention, the selected bisphenol substances were BPS, BPAF, BPA, and BPF, and the structural formulas of their metabolites are shown in the table below:

[0060]

[0061]

[0062]

[0063] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for identifying bisphenol metabolites, characterized in that: Includes the following steps: S1: Establish a potential metabolite list: Based on the molecular structure of the bisphenol to be tested, predict its possible phase I and phase II metabolic reactions using metabolic simulation software, and generate a list containing molecular information of the potential metabolites of the bisphenol. S2: Obtain high-resolution mass spectrometry data of biological samples: pre-process serum, urine and / or fecal samples collected from experimental animals exposed to the bisphenols, and analyze them using liquid chromatography-high-resolution mass spectrometry to obtain mass spectrometry data containing information on the parent ion and fragment ions. S3: Automated screening and identification: The mass spectrometry data obtained in step S2 is automatically processed using mass spectrometry analysis software, including precursor ion identification and fragment ion matching analysis. The analysis results are compared with the potential metabolite list generated in step S1, and metabolites that match the mass spectrometry data are selected as candidate identification results.

2. The method of claim 1, wherein the method is used for screening metabolites of bisphenols. In step S1, the metabolic simulation software includes a first metabolic simulation software and a second metabolic simulation software. The first metabolic simulation software is used to predict the phase I metabolic reaction of the bisphenol substances, and the second metabolic simulation software is used to predict the phase II metabolic reaction.

3. The method for identifying bisphenol metabolites according to claim 2, characterized in that: The first metabolic simulation software is EAWAG-BBD, and the second metabolic simulation software is BioTransformer 3.

0.

4. The method for identifying bisphenol metabolites according to claim 1, characterized in that: The bisphenols to be tested are selected from at least one of bisphenol A, bisphenol AF, bisphenol S and bisphenol F.

5. The method for identifying bisphenol metabolites according to claim 1, characterized in that: In step S2, the experimental animal is a rodent; the exposure method is oral gavage, the exposure concentration is 21.6 mg / kg / day to 307 mg / kg / day, and the exposure period is 28 days.

6. The method for identifying bisphenol metabolites according to claim 1, characterized in that: In step S2, the pretreatment includes: for serum samples, precipitating proteins with organic solvents; for urine samples, diluting with organic solvents and then filtering; and for fecal samples, extracting with organic solvents and then filtering.

7. The method for identifying bisphenol metabolites according to claim 1, characterized in that: In step S2, the liquid chromatography-high resolution mass spectrometry instrument is an ultra-high performance liquid chromatography-tandem quadrupole / orbit trap high resolution mass spectrometer, and the mass spectrometry scan adopts a full scan-data dependent two-stage scan mode in positive ion mode and / or negative ion mode.

8. The method for identifying bisphenol metabolites according to claim 1, characterized in that: In step S3, the mass spectrometry analysis software includes Mass Frontier software and Competitive Fragmentation Modeling-ID software. The automated processing includes simulating and scoring the fragment ion spectrum using the Competitive Fragmentation Modeling-ID software, and matching the high-scoring precursor ion information with the potential metabolite list using the Mass Frontier software.

9. The method for identifying bisphenol metabolites according to claim 1, characterized in that: The potential metabolite list includes at least one set of the following metabolite information: 4 metabolites of bisphenol S, 9 metabolites of bisphenol AF, 7 metabolites of bisphenol A, and 6 metabolites of bisphenol F.

10. The method for identifying bisphenol metabolites according to claim 5, characterized in that: During the exposure period, serum, urine, and fecal samples were collected from the experimental animals on days 7, 14, 21, and 28, respectively.

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