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Activity of GPCR-targeted drugs influenced by human gut microbiota metabolism

Nature Chemistry volume 17, pages 808–821 (2025)Cite this article

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Abstract

Microbiota-mediated drug metabolism can affect pharmacological efficacy. Here we conducted a systematic comparative metabolomics investigation of drug metabolism modes by evaluating the impacts of human gut commensal bacteria on 127 G-protein-coupled receptor (GPCR)-targeted drugs. For the most extensively metabolized drugs in our screen, we elucidated both conventional and unconventional drug transformations and the corresponding activities of generated metabolites. Comparisons of drug metabolism by a gut microbial community versus individual species revealed both taxon intrinsic and collaborative processes that influenced the activity of the metabolized drugs against target GPCRs. We also observed iloperidone inactivation by generating unconventional metabolites. The human gut commensal bacteria mixture incorporated sulfur in the form of a thiophene motif, whereas Morganella morganii used a cascade reaction to incorporate amino-acid-derived tricyclic systems into the drug metabolites. Our results reveal a broad impact of human gut commensal bacteria on GPCR-targeted drug structures and activities through diverse microbiota-mediated biotransformations.

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Fig. 1: Workflow to understand chemical interactions between human gut commensal microbiota and GPCR drugs.
Fig. 2: Screening for bacterial drug metabolism reveals conserved changes in receptor affinity associated with specific structural motifs.
Fig. 3: Structural determination of metabolites reveals alteration in drug activity for six drugs through unconventional pathways.
Fig. 4: Comparative metabolomics analysis uncovered an unconventional drug metabolism pathway of iloperidone.
Fig. 5: Mapping drug metabolism by individual bacterial species.
Fig. 6: M. morganii D82 inactivates iloperidone through amino-acid utilization.
Fig. 7: Bacterial metabolism of iloperidone observed in vivo.

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Data availability

All data generated during this study are included in the Article and its Supplementary Tables 1–25 and Supplementary Figs. 1–309. The information on all GPCR drugs discussed in this Article was sourced from DrugBank Online at https://go.drugbank.com/. Source data are provided with this paper.

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Acknowledgements

We thank members of the Crawford lab for helpful discussions. We also thank T. Wu and J. Kim at the Yale West Campus Analytical Core for their assistance with LC–MS. This work was primarily supported by the National Institute of General Medical Sciences (1RM1GM141649 to J.M.C. and N.W.P.). The mouse model experiment (DP2DK125119 to N.W.P.) and preparation of the microbial community (R01AT010014 to A.L.G.) were supported by the NIH. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. T.T. was in part supported by the NIH Chemistry-Biology Interface Training Program (T32GM067543) and the NSF Predoctoral Fellowship Program (2020293597). A.A.V. was in part supported by the NIH Individual Predoctoral Fellowship program (F31DK132941). B.D.-L. is a Cancer Research Institute Irvington Fellow supported by the Cancer Research Institute (CRI4515). Y.Z. was in part supported by a Peter Moore fellowship in the Department of Chemistry.

Author information

Author notes

  1. Qihao Wu

    Present address: Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA, USA

  2. These authors contributed equally: Qihao Wu, Deguang Song.

Authors and Affiliations

  1. Department of Chemistry, Yale University, New Haven, CT, USA

    Qihao Wu, Yanyu Zhao, Tayah Turocy & Jason M. Crawford

  2. Institute of Biomolecular Design and Discovery, Yale University, West Haven, CT, USA

    Qihao Wu, Yanyu Zhao, Tayah Turocy & Jason M. Crawford

  3. Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA

    Deguang Song, Brianna Duncan-Lowey & Noah W. Palm

  4. Department of Microbial Pathogenesis, Yale University School of Medicine, New Haven, CT, USA

    Andrew A. Verdegaal, Andrew L. Goodman & Jason M. Crawford

  5. Microbial Sciences Institute, Yale University, West Haven, CT, USA

    Andrew A. Verdegaal & Andrew L. Goodman

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Contributions

Q.W., D.S., A.L.G., N.W.P. and J.M.C. conceived the project. Q.W., D.S., Y.Z., A.A.V. and T.T. conducted the experiments. B.D.-L. performed molecular docking. Q.W. and D.S. carried out data analyses. Q.W. prepared graphical illustrations and wrote the first draft of the manuscript. All authors reviewed, edited and contributed to the final version of the manuscript.

Corresponding authors

Correspondence to Andrew L. Goodman, Noah W. Palm or Jason M. Crawford.

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Competing interests

N.W.P. is a co-founder of Artizan Biosciences and has received research funding for unrelated studies from Artizan Biosciences and F. Hoffmann-La Roche. A.L.G. serves on the scientific advisory boards for Seres Therapeutics, Piton Therapeutics, Nuanced Health and Taconic Biosciences. The others authors declare no competing interests.

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Nature Chemistry thanks Aaron Wright and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Mapping of GPCR drug activities.

(a) Number of GPCRs and their class that interacted with the 127 selected drugs. The drug target information was obtained from the DrugBank database. (b) Mapping specific interactions between drugs and GPCRs. Agonist interactions were labeled in red and antagonist interactions were labeled in grey. Aligned with the prevalence of antagonistic GPCR drugs in the DrugBank database, our drug panel was composed primarily of antagonists (74.8 %, 95/127), but they were complemented with some agonists (20.5 %, 26/127) and dual-function drugs (4.7 %, 6/127).

Extended Data Fig. 2 High-throughput identification of 12 GPCR drugs heavily consumed through drug metabolism screening by the human gut bacterial collection.

(a) The bar graph illustrates that out of the screened 127 drugs, 12 were heavily consumed (drug consumed ≥ 80%). n = 3 biological replicates. Data represent mean ± s.d. (b) The EICs of parent drug ions for both Bac+ and Bac- (after 24 h of drug incubation) offer a relative quantification of their concentration in the supernatants. The significant differences in drug concentration observed between the presence and absence of bacteria strongly suggest that drug consumption is dependent on the presence of bacteria. The EICs (with a ± 20 ppm window) of drug ions were determined using the following ions: [M + H]+, m/z 559.2300 for olmesartan medoxomil; [M + H]+, m/z 569.1667 for azilsartan medoxomil; [M + H]+, m/z 389.2071 for trimethobenzamide; [M + H]+, m/z 412.2846 for fesoterodine; [M + H]+, m/z 497.2217 for selexipag; [M + H]+, m/z 427.2028 for iloperidone; [M + H]+, m/z 405.1921 for ozaminod; [M + H]+, m/z 461.1296 for ponesimod; [M + H]+, m/z 430.2224 for sarpogrelate; [M + H]+, m/z 501.1360 for SB756050; [M + H]+, m/z 388.2118 for Trimebutine; [M + H]+, m/z 457.1904 for GSK1292263. The experiment was conducted in triplicates for both Bac+ and Bac- conditions. Data are means ± SEM. n.d., not detected. Statistical significance was determined using two-tailed t-test.

Source data

Extended Data Fig. 3 Impact of gut bacterial drug metabolism on GPCR activity.

(a) Sample preparation procedure for GPCR activity tests. (b) GPCR activity of 12 drugs were tested and compared under two conditions: drugs were administered without undergoing metabolism (at 0 h, blue), and drugs undergoing complete metabolization (at 24 h, red). Drug activities were quantified by EA50 values, Log10[Conc, M] refers to the starter drug concentration of 10 μM in the complex medium extracts. n = 3 biological replicates. Data are means ± SEM.

Source data

Extended Data Fig. 4 Comparative metabolomics-based drug metabolite discovery and characterization.

Volcano plot illustrates the FC (molecular features with Bac+ versus molecular features with Bac-) and FDR values in LC-QTOF-MS positive ion mode. (a) The raw LC-MS files of Bac+ and Bac- were processed using XCMS modules to transform them into molecular feature information through feature detection, grouping, and filtering ( < 3,000 molecular features were prioritized based on a strict intensity cutoff to focus on robust drug transformations in this study). (b) Molecular feature selection involved volcano plot analysis with a p-value and fold change cutoff, followed by a manual inspection to create a conclusive validated hit list. (c) Structural elucidation of molecular features of interest was carried out through MS/MS and NMR techniques, as well as chemical standard comparisons.

Extended Data Fig. 5 Drug metabolite profiles for six drug metabolomes.

(a) Information on the metabolites of six drugs. The bar graphs on the left and the EIC traces on the right depict a comparison between Bac- and Bac+ for ponesimod (b), SB756050 (c), trimethobenzamide (d), GSK1292263 (e), ozanimod (f), and trimebutine (g). The metabolism of five other drugs, including sarpogrelate, olmesartan medoxomil, fesoterodine, and azilsartan medoxomil, was characterized through standard comparison (Supplementary Fig. S2). The metabolome of iloperidone is detailed in Fig. 4. n = 3 biological replicates. Data are means ± SEM. Statistical significance was determined using two-tailed t-test.

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Extended Data Fig. 6 Mapping drug metabolism by individual bacterial species.

(a) Selected 12 isolates from bacterial collection represent their corresponding clades for the examination of drug metabolism. Proteobacteria, red; Actinobacteria, purple; Bacteroidetes, green; Firmicutes, blue. Mono bacterial drug metabolism for azilsartan medoxomil (b), olmesartan medoxomil (c), sarpogrelate (d), fesoterodine (e), selexipag (f), ozanimod (g), GSK1292263 (h), ponesimod (i), and iloperidone (j). No significant O-demethylation metabolism of the three drugs (trimebutine, trimethobenzamide, and SB756050) was detected in the 12 selected bacterial isolates. Further details are provided in Supplementary Fig. S81. n = 3 biological replicates. Data are means ± SEM.

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Extended Data Fig. 7 Proposed iloperidone drug metabolism pathways.

(a) Iloperidone metabolism pathways by human gut commensal bacteria described in this study. (b) Reported biosynthetic pathway for indolethiophene skeleton in Streptomyces sp. (c) Reported quinolizidine alkaloids in the biosynthesis of lupins.

Extended Data Fig. 8 Verification of the activity of metabolites for two drugs, GSK1292263 and iloperidone.

(a) Chemical transformation route for generating a GSK1292263 metabolite, GSK-459. (b) Structural elucidation of synthetic GSK-459 by NMR analyses. (c) LC-MS-based retention time comparison between synthetic standard (trace A in blue), drug metabolite from bacterial transformation (trace B in black) and co-injection (trace C in red). The EIC (with a ± 20 ppm window) for GSK-459 ion was determined using [M + H]+, m/z 459.2061. (d) MS2 similarity comparison with standard obtained from chemical reaction (top spectrum in blue) and drug metabolite from bacterial transformation (bottom spectrum in red). The predicted fragments from MS2 are indicated in panel b. (e) GPCR activity test (GPR119 agonist) on both GSK1292263 and its inactive metabolite GSK-459. n = 3 biological replicates. Data represents mean ± s.d. (f) Chemical structures of iloperidone and its metabolites. All metabolites were isolated from bacterial transformation culture broth, with their structures determined using NMR, excluding metabolite ilo-477, which was inferred by comparative tandem MS and isotope labelling studies. (g) Key 2D NMR (COSY and HMBC) correlations for the structural assignments of ilo-430. (h) GPCR activity test (DRD2 antagonist) on iloperidone and its metabolites. n = 3 biological replicates. Data represents mean ± s.d.

Extended Data Fig. 9 Summary of the metabolites of 17 drugs analyzed in our screen.

The metabolism of Iloperidone is summarized in Extended Data Fig. 8f. The chemical structures of azilsartan medoxomil (a), olmesartan medoxomil (b), ponesimod (c), clopidogrel (d), selexipag (e), risperidone (f), candesartan cilexetil (g), sarpogrelate (h), trimebutine (i), nicergoline (j), trimethobenzamide (k), MK-0557 (l), ozanimod (m), doxazosin (n), fesoterodine (o), SB756050 (p), GSK1292263 (q) and their metabolites are listed. The chemical structures of SB756050 metabolites, including SB-487, SB-473, and SB-459, were determined through 2D NMR analysis of materials isolated from the culture broth. A chemical synthesis approach and/or standard comparison was used to determine the structures of the metabolites of GSK1292263 (GSK-459), olmesartan medoxomil (olmesartan), azilsartan medoxomil (azilsartan), selexipag (ACT-333679), sarpogrelate (sarpogrelate-330), and fesoterodine (fesoterodine-342). MS/MS analysis was used to predict the chemical structures of ponesimod-463a/463b, trimebutine-374, trimethobenzamide-361, trimethobenzamide-347, ozanimod-407, ozanimod-408, doxazosin-438, doxazosin-424, SB-445, and GSK-460. The chemical structures of the remaining metabolites were proposed based on MS analysis. Ten microbial transformations, including those of azilsartan medoxomil, olmesartan medoxomil, clopidogrel, selexipag, candesartan cilexetil, sarpogrelate, nicergoline, ozanimod, doxazosin, and fesoterodine, were conserved with the transformations observed in human metabolism.

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Supplementary Figs. 1–309 and Tables 1–25.

Reporting Summary (download PDF )

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NMR data of nine identified compounds.

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The properties of all GPCR drugs studied in this Article.

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Wu, Q., Song, D., Zhao, Y. et al. Activity of GPCR-targeted drugs influenced by human gut microbiota metabolism. Nat. Chem. 17, 808–821 (2025). https://doi.org/10.1038/s41557-025-01789-w

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