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The Tox21 robotic platform for the assessment of environmental chemicals – from vision to reality
Open accessAbstract
Since its establishment in 2008, the US Tox21 inter-agency collaboration has made great progress in developing and evaluating cellular models for the evaluation of environmental chemicals as a proof of principle. Currently, the program has entered its production phase (Tox21 Phase II) focusing initially on the areas of modulation of nuclear receptors and stress response pathways. During Tox21 Phase II, the set of chemicals to be tested has been expanded to nearly 10,000 (10K) compounds and a fully automated screening platform has been implemented. The Tox21 robotic system combined with informatics efforts is capable of screening and profiling the collection of 10K environmental chemicals in triplicate in a week. In this article, we describe the Tox21 screening process, compound library preparation, data processing, and robotic system validation.
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“While OGB-MolPCBA primarily focuses on predicting biophysical activity (e.g., protein binding), other datasets in MoleculeNet (Wu et al., 2018) include prediction of quantum mechanical properties (e.g., HOMO/LUMO), physical chemistry properties (e.g., water solubility), and physiological properties (e.g., toxicity prediction (Attene-Ramos et al., 2013)).”
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Zhang, J.-H., Chung, T. D. Y., & Oldenburg, K. R. (1999) · SLAS Discovery · doi:10.1177/108705719900400206 · Cited by 6,847
The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves
Hill, A. V., Hill, A. L., & Hill, A. V. (1910) · The Journal of Physiology · Cited by 2,181
Toxicity Testing in the 21st Century: A Vision and a Strategy
Krewski, D. R., Acosta, D., Andersen, M. E., Anderson, H. A., Bailar, J. C., Boekelheide, K., Brent, R., Charnley, G., Cheung, V. G., Green, S., et al. (2010) · Journal of Toxicology and Environmental Health Part B · doi:10.1080/10937404.2010.483176 · Cited by 1,696
PubChem: Integrated Platform of Small Molecules and Biological Activities
Bolton, E., Wang, Y., Thiessen, P. A., & Bryant, S. H. (2008) · Annual reports in computational chemistry · doi:10.1016/s1574-1400(08)00012-1 · Cited by 1,267
Toxicity testing in the 21st century: A vision and a strategy
Gibb, S. (2007) · Reproductive Toxicology · doi:10.1016/j.reprotox.2007.10.013 · Cited by 1,256
(2014) · Choice Reviews Online · doi:10.5860/choice.52-0597 · Cited by 889
Inglese, J., Auld, D. S., Jadhav, A., Johnson, R. L., Simeonov, A. M., Yasgar, A., Zheng, W., & Austin, C. P. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0604348103 · Cited by 794
Self-citationTransforming Environmental Health Protection
Collins, F. S., Gray, G. M., & Bucher, J. R. (2008) · Science · doi:10.1126/science.1154619 · Cited by 630
Improving the Human Hazard Characterization of Chemicals: A Tox21 Update
Tice, R. R., Austin, C. P., Kavlock, R. J., & Bucher, J. R. (2013) · Environmental Health Perspectives · doi:10.1289/ehp.1205784 · Cited by 619
Self-citationJudson, R., Houck, K. A., Kavlock, R. J., Knudsen, T. B., Martin, M. T., Mortensen, H. M., Reif, D. M., Rotroff, D. M., Shah, I., Richard, A. M., et al. (2009) · Environmental Health Perspectives · doi:10.1289/ehp.0901392 · Cited by 610
Self-citationHuang, R., Southall, N. T., Wang, Y., Yasgar, A., Shinn, P., Jadhav, A., Nguyễn, Ð.-T., & Austin, C. P. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3001862 · Cited by 400
Self-citationShukla, S. J., Huang, R., Austin, C. P., & Xia, M. (2010) · Drug Discovery Today · doi:10.1016/j.drudis.2010.07.007 · Cited by 291
Self-citationCompound Cytotoxicity Profiling Using Quantitative High-Throughput Screening
Xia, M., Huang, R., Witt, K. L., Southall, N. T., Fostel, J. M., Cho, M.-H., Jadhav, A., Smith, C. S., Inglese, J., Portier, C. J., et al. (2007) · Environmental Health Perspectives · doi:10.1289/ehp.10727 · Cited by 256
Self-citationToxicity Testing in the 21st Century: Implications for Human Health Risk Assessment
Kavlock, R. J., Austin, C. P., & Tice, R. R. (2008) · Risk Analysis · doi:10.1111/j.1539-6924.2008.01168.x · Cited by 239
Self-citationPredictive Models of Prenatal Developmental Toxicity from ToxCast High-Throughput Screening Data
Sipes, N. S., Martin, M. T., Reif, D. M., Kleinstreuer, N., Judson, R., Singh, A. V., Chandler, K. J., Dix, D. J., Kavlock, R. J., & Knudsen, T. B. (2011) · Toxicological Sciences · doi:10.1093/toxsci/kfr220 · Cited by 213
Self-citationChemical Genomics Profiling of Environmental Chemical Modulation of Human Nuclear Receptors
Huang, R., Xia, M., Cho, M.-H., Sakamuru, S., Shinn, P., Houck, K. A., Dix, D. J., Judson, R., Witt, K. L., Kavlock, R. J., et al. (2011) · Environmental Health Perspectives · doi:10.1289/ehp.1002952 · Cited by 196
Self-citationPredictive Model of Rat Reproductive Toxicity from ToxCast High Throughput Screening1
Martin, M. T., Knudsen, T. B., Reif, D. M., Houck, K. A., Judson, R., Kavlock, R. J., & Dix, D. J. (2011) · Biology of Reproduction · doi:10.1095/biolreprod.111.090977 · Cited by 164
Self-citationA Robotic Platform for Quantitative High-Throughput Screening
Michael, S., Auld, D. S., Klumpp-Thomas, C. A., Jadhav, A., Zheng, W., Thorne, N., Austin, C. P., Inglese, J., & Simeonov, A. M. (2008) · Assay and Drug Development Technologies · doi:10.1089/adt.2008.150 · Cited by 155
Self-citationPerspectives on validation of high-throughput assays supporting 21st century toxicity testing
Judson, R. (2013) · ALTEX · doi:10.14573/altex.2013.1.051 · Cited by 151
(2004) · SLAS DISCOVERY · doi:10.1177/108705710400900511 · Cited by 125
A Grid Algorithm for High Throughput Fitting of Dose-Response Curve Data
Wang, Y., Jadhav, A., Southal, N., Huang, R., & Nguyễn, Ð.-T. (2010) · Current Chemical Genomics · doi:10.2174/1875397301004010057 · Cited by 123
Self-citationParadigm Shift in Toxicity Testing and Modeling
Sun, H., Xia, M., Austin, C. P., & Huang, R. (2012) · The AAPS Journal · doi:10.1208/s12248-012-9358-1 · Cited by 96
Self-citationCompound Management for Quantitative High-Throughput Screening
Yasgar, A., Shinn, P., Jadhav, A., Auld, D. S., Michael, S., Zheng, W., Austin, C. P., Inglese, J., & Simeonov, A. M. (2008) · JALA Journal of the Association for Laboratory Automation · doi:10.1016/j.jala.2007.12.004 · Cited by 89
Self-citationAttene‐Ramos, M. S., Austin, C. P., & Xia, M. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-386454-3.00209-8 · Cited by 58
Self-citationLafont, O. (2002) · European Journal of Medicinal Chemistry · doi:10.1016/s0223-5234(02)01367-3 · Cited by 38
Huang, R., Southall, N. T., Xia, M., Cho, M.-H., Jadhav, A., Nguyễn, Ð.-T., Inglese, J., Tice, R. R., & Austin, C. P. (2009) · Toxicological Sciences · doi:10.1093/toxsci/kfp231 · Cited by 35
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“…of different toxicity prediction software and web-based tools such as Tox21 (https:// ntp. niehs. nih. gov/ whatw estudy/ tox21/ index. html) [327], SEA (http:// sea. bkslab. org/) [328], eToxPred (https:// www. bryli nski. org/ etoxp red-0) [329], and TargeTox (https:// github. com/…”
admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties
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The Green ChemisTREE: 20 years after taking root with the 12 principles
Erythropel, H. C., Zimmerman, J. B., de Winter, T. M., Petitjean, L., Melnikov, F., Lam, J. C., Lounsbury, A. W., Mellor, K. E., Janković, N. Z., Tu, Q., et al. (2018) · Green Chemistry · doi:10.1039/c8gc00482j · Cited by 848
Tracking complex mixtures of chemicals in our changing environment
Escher, B. I., Stapleton, H. M., & Schymanski, E. (2020) · Science · doi:10.1126/science.aay6636 · Cited by 809
CERAPP: Collaborative Estrogen Receptor Activity Prediction Project
Mansouri, K., Abdelaziz, A., Rybacka, A., Roncaglioni, A., Tropsha, A., Varnek, A., Zakharov, A., Worth, A., Richard, A. M., Grulke, C. M., et al. (2016) · Environmental Health Perspectives · doi:10.1289/ehp.1510267 · Cited by 375
“EPA’s HTS assays, online databases, and other data sets used by participants to train models: • HTS data from Tox21 project consisting of ~ 8,000 chemicals evaluated in four assays (Attene-Ramos et al. 2013; Collins et al. 2008; Huang et al. 2014; Shukla et al. 2010; Tice et al. 2013), extending beyond the 1,677 used…”
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Metadata
- Authors
- Attene-Ramos, M. S., Miller, N., Huang, R., Michael, S., Itkin, M., Kavlock, R. J., Austin, C. P., Shinn, P., Simeonov, A., Tice, R. R., & Xia, M.
- Affiliations
- National Institutes of Health (US)
- Environmental Protection Agency (US)
- Corresponding author
- Xia, Menghang
- ORCID
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 18
- Issue
- 15-16
- Pages
- 716-723
- ISSN
- 1359-6446 (ISSN-L)
- PMC
- PMC3771082
- OpenAlex
- W1991146785
- Cited by
- 255
- References
- 27
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 19 · 2023: 26 · 2022: 25 · 2021: 11 · 2020: 18 · 2019: 23 · 2018: 33 · 2017: 24 · 2016: 23 · 2015: 20 · 2014: 10 · 2013: 2
- FWCI
- 13.48
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Drug Discovery Today (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (Z99 TR999999)
- National Human Genome Research Institute (ZIBHG200319)
- National Institute of Environmental Health Sciences (Y02 ES007020)
- Topics
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Effects and risks of endocrine disrupting chemicals
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Environmental Toxicology and Ecotoxicology
- Keywords
- Tox21, environmental chemicals, high-throughput screening, stress response pathways, automated data processing, chemical profiling
- MeSH terms
- Animals, Environmental Monitoring, Environmental Pollutants, Humans, Robotics, United States, United States Environmental Protection Agency
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