The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Interaction between drugs and the gut microbiome
Open accessAbstract
The human gut microbiome is a complex ecosystem that can mediate the interaction of the human host with their environment. The interaction between gut microbes and commonly used non-antibiotic drugs is complex and bidirectional: gut microbiome composition can be influenced by drugs, but, vice versa, the gut microbiome can also influence an individual’s response to a drug by enzymatically transforming the drug’s structure and altering its bioavailability, bioactivity or toxicity (pharmacomicrobiomics). The gut microbiome can also indirectly impact an individual’s response to immunotherapy in cancer treatment. In this review we discuss the bidirectional interactions between microbes and drugs, describe the changes in gut microbiota induced by commonly used non-antibiotic drugs, and their potential clinical consequences and summarise how the microbiome impacts drug effectiveness and its role in immunotherapy. Understanding how the microbiome metabolises drugs and reduces treatment efficacy will unlock the possibility of modulating the gut microbiome to improve treatment.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An obesity-associated gut microbiome with increased capacity for energy harvest
Turnbaugh, P. J., Ley, R. E., Mahowald, M. A., Magrini, V., Mardis, E. R., & Gordon, J. I. (2006) · Nature · doi:10.1038/nature05414 · Cited by 12,444
Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors
Routy, B., Le Chatelier, E., Derosa, L., Duong, C. P. M., Alou, M. T., Daillère, R., Fluckiger, A., Messaoudene, M., Rauber, C., Roberti, M. P., et al. (2018) · Science · doi:10.1126/science.aan3706 · Cited by 5,671
Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients
Gopalakrishnan, V., Spencer, C. N., Nezi, L., Reuben, A., Andrews, M. C., Karpinets, T. V., Prieto, P. A., Vicente, D., Hoffman, K., Wei, S. C., et al. (2018) · Science · doi:10.1126/science.aan4236 · Cited by 4,812
Commensal Bifidobacterium promotes antitumor immunity and facilitates anti–PD-L1 efficacy
Sivan, A., Corrales, L., Hubert, N., Williams, J. B., Aquino-Michaels, K., Earley, Z. M., Benyamin, F. W., Man Lei, Y., Jabri, B., Alegre, M.-L., et al. (2015) · Science · doi:10.1126/science.aac4255 · Cited by 3,888
Gut microbiota regulate motor deficits and neuroinflammation in a model of Parkinson's disease
Sampson, T. R., Debelius, J. W., Thron, T., Janssen, S., Shastri, G. G., Ilhan, Z. E., Challis, C., Schretter, C. E., Rocha, S., Gradinaru, V., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.018 · Cited by 3,622
Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota
Vétizou, M., Pitt, J. M., Daillère, R., Lepage, P., Waldschmitt, N., Flament, C., Rusakiewicz, S., Routy, B., Roberti, M. P., Duong, C. P. M., et al. (2015) · Science · doi:10.1126/science.aad1329 · Cited by 3,488
Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases
Lloyd-Price, J., Arze, C., Ananthakrishnan, A. N., Schirmer, M., Avila-Pacheco, J., Poon, T. W., Andrews, E., Ajami, N. J., Bonham, K. S., Brislawn, C. J., et al. (2019) · Nature · doi:10.1038/s41586-019-1237-9 · Cited by 3,143
The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients
Matson, V., Fessler, J., Bao, R., Chongsuwat, T., Zha, Y., Alegre, M.-L., Luke, J. J., & Gajewski, T. F. (2018) · Science · doi:10.1126/science.aao3290 · Cited by 3,107
Population-level analysis of gut microbiome variation
Falony, G., Joossens, M., Vieira-Silva, S., Wang, J., Darzi, Y., Faust, K., Kurilshikov, A., Bonder, M. J., Valles-Colomer, M., Vandeputte, D., et al. (2016) · Science · doi:10.1126/science.aad3503 · Cited by 2,400
Self-citationDisentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota
MetaHIT consortium, Forslund, K., Hildebrand, F., Nielsen, T., Falony, G., Le Chatelier, E., Sunagawa, S., Prifti, E., Vieira-Silva, S., Gudmundsdottir, V., et al. (2015) · Nature · doi:10.1038/nature15766 · Cited by 2,116
Cited by
Immune checkpoint therapy for solid tumours: clinical dilemmas and future trends
Sun, Q., Hong, Z., Zhang, C., Wang, L., Han, Z., & Ma, D. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01522-4 · Cited by 598
Mahmud, M. R., Akter, S., Tamanna, S. K., Mazumder, L., Esti, I. Z., Banerjee, S., Akter, S., Hasan, M. R., Acharjee, M., Hossain, M. S., et al. (2022) · Gut Microbes · doi:10.1080/19490976.2022.2096995 · Cited by 466
The Healthy Microbiome—What Is the Definition of a Healthy Gut Microbiome?
Shanahan, F., Ghosh, T. S., & O’Toole, P. W. (2021) · Gastroenterology · doi:10.1053/j.gastro.2020.09.057 · Cited by 383
Drug-microbiota interactions: an emerging priority for precision medicine
Zhao, Q., Chen, Y., Huang, W., Zhou, H., & Zhang, W. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01619-w · Cited by 268
Ma, Z., Zuo, T., Frey, N., & Rangrez, A. Y. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01946-6 · Cited by 259
Metadata
- Authors
- Weersma, R. K., Zhernakova, A., & Fu, J.
- Venue
- Gut
- Publisher
- BMJ
- Published
- 2020-05-14
- Volume
- 69
- Issue
- 8
- Pages
- 1510-1519
- ISSN
- 0017-5749 (ISSN-L)
- 1468-3288
- OpenAlex
- W3024321726
- Cited by
- 961
- References
- 59
- FWCI
- 31.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Interaction between drugs and the gut microbiome. https://app.cochat.ai/papers/10.1136/gutjnl-2019-320204
- The paper
- Weersma, R. K., Zhernakova, A., & Fu, J. (2020). Interaction between drugs and the gut microbiome. Gut. https://doi.org/10.1136/gutjnl-2019-320204
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
