The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Update on EPA’s ToxCast Program: Providing High Throughput Decision Support Tools for Chemical Risk Management
Abstract
The field of toxicology is on the cusp of a major transformation in how the safety and hazard of chemicals are evaluated for potential effects on human health and the environment. Brought on by the recognition of the limitations of the current paradigm in terms of cost, time, and throughput, combined with the ever increasing power of modern biological tools to probe mechanisms of chemical-biological interactions at finer and finer resolutions, 21st century toxicology is rapidly taking shape. A key element of the new approach is a focus on the molecular and cellular pathways that are the targets of chemical interactions. By understanding toxicity in this manner, we begin to learn how chemicals cause toxicity, as opposed to merely what diseases or health effects they might cause. This deeper understanding leads to increasing confidence in identifying which populations might be at risk, significant susceptibility factors, and key influences on the shape of the dose-response curve. The U. S. Environmental Protection Agency (EPA) initiated the ToxCast, or "toxicity forecaster", program 5 years ago to gain understanding of the strengths and limitations of the new approach by starting to test relatively large numbers (hundreds) of chemicals against an equally large number of biological assays. Using computational approaches, the EPA is building decision support tools based on ToxCast in vitro screening results to help prioritize chemicals for further investigation, as well as developing predictive models for a number of health outcomes. This perspective provides a summary of the initial, proof of concept, Phase I of ToxCast that has laid the groundwork for the next phases and future directions of the program.
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Cites
The KEGG resource for deciphering the genome
Kanehisa, M. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh063 · Cited by 5,104
Ankley, G. T., Bennett, R. S., Erickson, R. J., Hoff, D. J., Hornung, M. W., Johnson, R. D., Mount, D. R., Nichols, J. W., Russom, C. L., Schmieder, P. K., et al. (2009) · Environmental Toxicology and Chemistry · doi:10.1002/etc.34 · Cited by 2,734
Reactome: a database of reactions, pathways and biological processes
Croft, D., O'Kelly, G., Wu, G., Haw, R., Gillespie, M., Matthews, L., Caudy, M., Garapati, P., Gopinath, G., Jassal, B., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq1018 · Cited by 1,751
Toxicity testing in the 21st century: A vision and a strategy
Gibb, S. (2008) · Reproductive Toxicology · doi:10.1016/j.reprotox.2007.10.013 · Cited by 1,257
Pathway Commons, a web resource for biological pathway data
Cerami, E. G., Gross, B. E., Demir, E., Rodchenkov, I., Babur, O., Anwar, N., Schultz, N., Bader, G. D., & Sander, C. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq1039 · Cited by 1,101
The ToxCast Program for Prioritizing Toxicity Testing of Environmental Chemicals
Dix, D. J., Houck, K. A., Martin, M. T., Richard, A. M., Setzer, R. W., & Kavlock, R. J. (2007) · Toxicological Sciences · doi:10.1093/toxsci/kfl103 · Cited by 1,012
Self-citationInglese, J., Auld, D. S., Jadhav, A., Johnson, R. L., Simeonov, A., Yasgar, A., Zheng, W., & Austin, C. P. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0604348103 · Cited by 792
Transforming Environmental Health Protection
Collins, F. S., Gray, G. M., & Bucher, J. R. (2008) · Science · doi:10.1126/science.1154619 · Cited by 630
O’Brien, P. J., Irwin, W., Diaz, D., Howard-Cofield, E., Krejsa, C. M., Slaughter, M. R., Gao, B., Kaludercic, N., Angeline, A., Bernardi, P., et al. (2006) · Archives of Toxicology · doi:10.1007/s00204-006-0091-3 · Cited by 609
Judson, R. S., 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. (2010) · Environmental Health Perspectives · doi:10.1289/ehp.0901392 · Cited by 608
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EDC-2: the endocrine society's second scientific statement on endocrine-disrupting chemicals
Gore, A. C., Chappell, V. A., Fenton, S. E., Flaws, J. A., Nadal, A., Prins, G. S., Toppari, J., & Zoeller, R. T. (2015) · Endocrine Reviews · doi:10.1210/er.2015-1010 · Cited by 2,407
QSAR Modeling: Where have you been? Where are you going to?
Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm4004285 · Cited by 2,164
Self-citationImpact of food processing and detoxification treatments on mycotoxin contamination
Karlovsky, P., Suman, M., Berthiller, F., De Meester, J., Eisenbrand, G., Perrin, I., Oswald, I. P., Speijers, G., Chiodini, A., Recker, T., et al. (2016) · Mycotoxin Research · doi:10.1007/s12550-016-0257-7 · Cited by 689
OPERA models for predicting physicochemical properties and environmental fate endpoints
Mansouri, K., Grulke, C. M., Judson, R. S., & Williams, A. J. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0263-1 · Cited by 624
Self-citationThe Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology
Richard, A. M., Huang, R., Waidyanatha, S., Shinn, P., Collins, B. J., Thillainadarajah, I., Grulke, C. M., Williams, A. J., Lougee, R. R., Judson, R. S., et al. (2021) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00264 · Cited by 323
Self-citationJudson, R., Houck, K., Martin, M., Richard, A. M., Knudsen, T. B., Shah, I., Little, S., Wambaugh, J., Woodrow Setzer, R., Kothya, P., et al. (2016) · Toxicological Sciences · doi:10.1093/toxsci/kfw092 · Cited by 232
Self-citationThe US Federal Tox21 Program: A strategic and operational plan for continued leadership
Thomas, R. (2018) · ALTEX · doi:10.14573/altex.1803011 · Cited by 229
Ciallella, H. L., & Zhu, H. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.8b00393 · Cited by 227
Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms
Kleinstreuer, N. C., Yang, J., Berg, E. L., Knudsen, T. B., Richard, A. M., Martin, M. T., Reif, D. M., Judson, R. S., Polokoff, M., Dix, D. J., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2914 · Cited by 211
Self-citationWu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193
Wittwehr, C., Aladjov, H., Ankley, G., Byrne, H. J., de Knecht, J., Heinzle, E., Klambauer, G., Landesmann, B., Luijten, M., MacKay, C., et al. (2017) · Toxicological Sciences · doi:10.1093/toxsci/kfw207 · Cited by 161
Zhu, H., Zhang, J., Kim, M. T., Boison, A., Sedykh, A., & Moran, K. (2014) · Chemical Research in Toxicology · doi:10.1021/tx500145h · Cited by 154
Williams, A. J., Gaines, L. G. T., Grulke, C. M., Lowe, C. N., Sinclair, G. F. B., Samano, V., Thillainadarajah, I., Meyer, B., Patlewicz, G., & Richard, A. M. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.850019 · Cited by 77
Self-citationTest driving ToxCast: endocrine profiling for 1858 chemicals included in phase II
Filer, D., Patisaul, H. B., Schug, T., Reif, D., & Thayer, K. (2014) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2014.09.021 · Cited by 71
Self-citationNyffeler, J., Willis, C., Harris, F. R., Foster, M. J., Chambers, B., Culbreth, M., Brockway, R. E., Davidson-Fritz, S., Dawson, D., Shah, I., et al. (2023) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2023.116513 · Cited by 67
Bouhifd, M. (2015) · ALTEX · doi:10.14573/altex.1502091 · Cited by 69
High-Throughput Screening to Identify Chemical Cardiotoxic Potential
Krishna, S., Berridge, B., & Kleinstreuer, N. (2021) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00382 · Cited by 52
Self-citationThe opportunities of mining historical and collective data in drug discovery
Wassermann, A. M., Lounkine, E., Davies, J. W., Glick, M., & Camargo, L. M. (2015) · Drug Discovery Today · doi:10.1016/j.drudis.2014.11.004 · Cited by 34
Metadata
- Authors
- Kavlock, R., Chandler, K., Houck, K., Hunter, S., Judson, R., Kleinstreuer, N., Knudsen, T., Martin, M., Padilla, S., Reif, D., Richard, A., Rotroff, D., Sipes, N., & Dix, D.
- Venue
- Chemical Research in Toxicology
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-05-15
- Volume
- 25
- Issue
- 7
- Pages
- 1287-1302
- ISSN
- 0893-228X (ISSN-L)
- 1520-5010
- OpenAlex
- W2012133139
- Cited by
- 494
- References
- 46
- FWCI
- 48.60
- Type
- journal article
- Language
- en
- Open access
- closed
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