Advances in genomics have revealed many of the genetic underpinnings of human disease, but exposomics methods are currently inadequate to obtain a similar level of understanding of environmental contributions to human disease. Exposomics methods are limited by low abundance of xenobiotic metabolites and lack of authentic standards, which precludes identification using solely mass spectrometry-based criteria. Here, we develop and validate a method for enzymatic generation of xenobiotic metabolites for use with high-resolution mass spectrometry (HRMS) for chemical identification. Generated xenobiotic metabolites were used to confirm identities of respective metabolites in mice and human samples based upon accurate mass, retention time and co-occurrence with related xenobiotic metabolites. The results establish a generally applicable enzyme-based identification (EBI) for mass spectrometry identification of xenobiotic metabolites and could complement existing criteria for chemical identification.

Advances in genomics have revealed many of the genetic underpinnings of human disease, but exposomics methods are currently inadequate to obtain a similar level of understanding of environmental contributions to human disease. Exposomics methods are limited by low abundance of xenobiotic metabolites and lack of authentic standards, which precludes identification using solely mass spectrometry-based criteria. Here, we develop and validate a method for enzymatic generation of xenobiotic metabolites for use with high-resolution mass spectrometry (HRMS) for chemical identification. Generated xenobiotic metabolites were used to confirm identities of respective metabolites in mice and human samples based upon accurate mass, retention time and co-occurrence with related xenobiotic metabolites. The results establish a generally applicable enzyme-based identification (EBI) for mass spectrometry identification of xenobiotic metabolites.

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Background The diversity of chemicals detectable in human samples by high‐resolution mass spectrometry (HRMS) exceeds the number of readily available chemical standards, limiting our ability to identify minor metabolites of drugs and other xenobiotics in vivo. Standard workflows for identification of unknown metabolites typically begin by interpretation of ion dissociation spectra (MS/MS) and comparison with library spectra and/or authentic standards – many of which are not available and do not incorporate enzymatic precursor‐product relationships as identification criteria. ObjectiveTo develop a high‐throughput strategy to generate human biotransformation products from diverse xenobiotic and drugs for analysis with LC‐HRMS. HypothesisWe hypothesize that unidentified metabolites are generated from enzymatic reactions of known metabolites. Therefore, strategies to generate and characterize metabolic products for diverse arrays of xenobiotics will facilitate identification of metabolites in vivo. MethodsHere, we adopt a strategy to generate human biotransformation products of diverse xenobiotics in a high‐throughput 96‐well plate format using incubations with human liver S9 fractions. Extracts from these reactions were collected at 0 and 24 hour time points and analyzed using LC‐HRMS (Thermo Scientific Fusion/High‐Field Q‐Exactive) to characterize metabolic products generated in a time‐dependent manner. Data‐dependent MS/MS was performed to collect MS/MS spectra. Expected biotransformation products were characterized by retention time, accurate mass m/z (MS1), MS/MS, and an increase in signal intensity for predicted or previously unreported biotransformation products. Incubations with stable‐isotope precursors aided the identification of previously unreported biotransformation products. ResultsOur data show that known and previously unidentified Phase 1 and Phase 2 metabolites were produced from a range of xenobiotics in a 96‐well format using human liver S9 fractions. S9 reaction extracts of 138 drug or xenobiotic precursors were analyzed generating a total of 502 validated metabolites. Selected metabolites were used to support their identification in mouse and human samples based on matched accurate mass MS1, retention time, and co‐occurrence with the parent xenobiotic and/or related metabolites in the samples. ConclusionWe have developed a scalable tool to generate metabolites from thousands of xenobiotics to facilitate identification of drug and other xenobiotic metabolites in human samples.

Precision medicine requires methods to assess drug metabolism and distribution, including the identification of known and undocumented drug and chemical exposures as well as their metabolites. Recent work demonstrated high-throughput generation of xenobiotic metabolites with human liver S-9 fractions and detection in human plasma and urine. Here, we developed a panel of lentivirally transduced human hepatoma cell lines (Huh7) that stably express individual cytochrome P450 (P450) enzymes and generate P450-specific xenobiotic metabolites. We verified protein expression by immunoblotting and demonstrated that the cell lines generate P450-specific metabolites from probe substrates. To increase analytical throughput, we used a pooling strategy where 36 chemicals were grouped into 12 unique mixtures, each mixture containing 6 randomly selected compounds, and each compound being present in two separate mixtures. Each mixture of compounds was incubated with 8 different P450 cell lines with cell extracts analyzed at 0 and 2 h. Extracts were analyzed using liquid chromatography-high resolution mass spectrometry. Cell lines selectively metabolized test substrates, with pazopanib metabolized by CYP3A4 and CYP2C8 cells, bupropion by CYP2B6, and β-naphthoflavone by CYP1A2 for example, showing substrate-enzyme specificity. Predicted metabolites from the remaining 33 compounds as well as many unidentified m/z features were detected. We also show that a specific metabolite generated by CYP2B6 cells, but not detected in the S9 system, was identified in human samples. Our data show that incubating these cell lines with chemical mixtures accelerated characterization of xenobiotic chemical space, while simultaneously allowing for the contributions of specific P450 enzymes to be identified.