The DOI matches its Crossref record; 161 of the 161 references it cites were found in a registry or an index.
Zebrafish disease models in drug discovery: from preclinical modelling to clinical trials
Open accessAbstract
Numerous drug treatments that have recently entered the clinic or clinical trials have their genesis in zebrafish. Zebrafish are well established for their contribution to developmental biology and have now emerged as a powerful preclinical model for human disease, as their disease characteristics, aetiology and progression, and molecular mechanisms are clinically relevant and highly conserved. Zebrafish respond to small molecules and drug treatments at physiologically relevant dose ranges and, when combined with cell-specific or tissue-specific reporters and gene editing technologies, drug activity can be studied at single-cell resolution within the complexity of a whole animal, across tissues and over an extended timescale. These features enable high-throughput and high-content phenotypic drug screening, repurposing of available drugs for personalized and compassionate use, and even the development of new drug classes. Often, drugs and drug leads explored in zebrafish have an inter-organ mechanism of action and would otherwise not be identified through targeted screening approaches. Here, we discuss how zebrafish is an important model for drug discovery, the process of how these discoveries emerge and future opportunities for maximizing zebrafish potential in medical discoveries. Zebrafish are useful model organisms for drug discovery, particularly in screening, disease modelling and toxicity assays. Here, Patton, Zon and Langenau discuss key recent successes in which zebrafish models have had key roles. Advances in gene modification technologies have enabled individual mutations to be recapitulated in zebrafish, leading to individualized treatments. Zebrafish xenografts as cancer models could also lead to rapid, personalized platforms for drug discovery.
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
1 retracted
Morita, K., He, S., Nowak, R. P., Wang, J., Zimmerman, M. W., Fu, C., Durbin, A. D., Martel, M. W., Prutsch, N., Gray, N. S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.03.051 · Cited by 78
RetractedNatural products as sources of new drugs from 1981 to 2014
Newman, D. J., & Cragg, G. M. (2016) · Journal of Natural Products · doi:10.1021/acs.jnatprod.5b01055 · Cited by 5,858
The zebrafish reference genome sequence and its relationship to the human genome.
Howe, K., Clark, M. D., Torroja, C. F., Torrance, J., Berthelot, C., Muffato, M., Collins, J. E., Humphray, S., McLaren, K., Matthews, L., et al. (2013) · Nature · doi:10.1038/nature12111 · Cited by 5,100
Self-citationThe re-emergence of natural products for drug discovery in the genomics era
Harvey, A. L., Edrada-Ebel, R., & Quinn, R. J. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4510 · Cited by 2,572
Identification of a Primary Target of Thalidomide Teratogenicity
Ito, T., Ando, H., Suzuki, T., Ogura, T., Hotta, K., Imamura, Y., Yamaguchi, Y., & Handa, H. (2010) · Science · doi:10.1126/science.1177319 · Cited by 2,136
Lenalidomide Causes Selective Degradation of IKZF1 and IKZF3 in Multiple Myeloma Cells
Krönke, J., Udeshi, N. D., Narla, A., Grauman, P., Hurst, S. N., McConkey, M., Svinkina, T., Heckl, D., Comer, E., Li, X., et al. (2014) · Science · doi:10.1126/science.1244851 · Cited by 1,879
How were new medicines discovered?
Swinney, D. C., & Anthony, J. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3480 · Cited by 1,812
The Myeloma Drug Lenalidomide Promotes the Cereblon-Dependent Destruction of Ikaros Proteins
Lu, G., Middleton, R. E., Sun, H., Naniong, M., Ott, C. J., Mitsiades, C. S., Wong, K.-K., Bradner, J. E., & Kaelin, W. G. (2014) · Science · doi:10.1126/science.1244917 · Cited by 1,583
Zebrafish as tools for drug discovery
MacRae, C. A., & Peterson, R. T. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4627 · Cited by 1,204
Towards a Comprehensive Catalog of Zebrafish Behavior 1.0 and Beyond
Kalueff, A. V., Gebhardt, M., Stewart, A. M., Cachat, J. M., Brimmer, M., Chawla, J. S., Craddock, C., Kyzar, E. J., Roth, A., Landsman, S., et al. (2013) · Zebrafish · doi:10.1089/zeb.2012.0861 · Cited by 1,061
Cited by
Phenotypic drug discovery: recent successes, lessons learned and new directions
Vincent, F., Nueda, A., Lee, J., Schenone, M., Prunotto, M., & Mercola, M. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00472-w · Cited by 348
Emerging insights in senescence: pathways from preclinical models to therapeutic innovations
Mansfield, L., Ramponi, V., Gupta, K., Stevenson, T., Mathew, A. B., Barinda, A. J., Herbstein, F., & Morsli, S. (2024) · npj Aging · doi:10.1038/s41514-024-00181-1 · Cited by 88
Stein, M., Elefteriou, F., Busse, B., Fiedler, I. A., Kwon, R. Y., Farrell, E., Ahmad, M., Ignatius, A., Grover, L., Geris, L., et al. (2023) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.4868 · Cited by 42
Karthika, C., Sureshkumar, R., Zehravi, M., Akter, R., Ali, F., Ramproshad, S., Mondal, B., Kundu, M. K., Dey, A., Rahman, M. H., et al. (2022) · Life · doi:10.3390/life12060811 · Cited by 30
A p21-GFP zebrafish model of senescence for rapid testing of senolytics in vivo
Morsli, S., Henriques, C. M., Ellis, P. S., Mortiboys, H., Baxendale, S., Loynes, C. A., Renshaw, S. A., & Bellantuono, I. (2023) · Aging Cell · doi:10.1111/acel.13835 · Cited by 21
Metadata
- Authors
- Patton, E. E., Zon, L. I., & Langenau, D. M.
- Venue
- Nature Reviews Drug Discovery
- Published
- 2021-06-11
- Volume
- 20
- Issue
- 8
- Pages
- 611-628
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W3171325432
- Cited by
- 567
- References
- 161
- FWCI
- 9.57
- Type
- journal article
- Language
- en
- Open access
- green
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: Zebrafish disease models in drug discovery: from preclinical modelling to clinical trials. https://app.cochat.ai/papers/10.1038/s41573-021-00210-8
- The paper
- Patton, E. E., Zon, L. I., & Langenau, D. M. (2021). Zebrafish disease models in drug discovery: from preclinical modelling to clinical trials. Nature Reviews Drug Discovery. https://doi.org/10.1038/s41573-021-00210-8
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.
