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EPA's chemical assessment researchers are crafting software and methodologies for estimating the human health and environmental impact of chemicals. These flexible tools estimate the toxicity and physical properties of compounds based on their molecular structures. To identify potential risks early in the design stage, decision makers can use the software to rapidly assess the hazard of chemicals, focusing on the area of chemical manufacturing design.
Experimental measurements of toxicity are time consuming and expensive. Software tools that can estimate toxicity provide faster and less expensive alternatives to experimental measurements of toxicity and can help chemical process designers reduce environmental and related human health impacts at the design stage.
Two software tools have been developed to aid the assessment of chemicals. Using the QSAR methodology, the Toxicity Estimation Software Tool (TEST) enables users to easily estimate the toxicity and physical properties of a chemical from its molecular structure. The Waste Reduction Algorithm (WAR) is a methodology that provides users a quantitative measure of the potential environmental impact of the waste generated in the manufacturing process of a chemical. This allows users to design a process to be as sustainable as possible by minimizing the potential environmental impact of the process.
- University of North Carolina
- Mario Negri Institute for Pharmacological Research (Milan, Italy)
- Swedish Chemicals Agency
- Technical Database Services, Inc.
Martin, T. M., Grulke, C. M., Young, D. M., Russom, C. L., Wang, N. Y., Jackson, C. R., and M. G. Barron. (2013). “Prediction of Aquatic Toxicity Mode of Action Using Linear Discriminant and Random Forest Models.” Journal of Chemical Information and Modeling.
Benfenati, E., Pardoe, S., Martin, T. M., Diaza, R. G., Lombardo, A., Manganaro, A., and A. Gissi. (2013). “Using Toxicological Evidence from QSAR Models in Practice.” ALTEX, 30, 1: 19-40.
Martin, T. M., Harten, P., Young, D. M., Muratov, E. N., Golbraikh, A., Zhu, H., and A. Tropsha. (2012). “Does rational selection of training and test sets improve the outcome of QSAR modeling?“ Journal of Chemical Information and Modeling, 52, 10: 2570-2578.
Barrett Jr., W. M., van Baten, J., and T.M. Martin. (2011). “Implementation of the waste reduction (WAR) algorithm utilizing flowsheet monitoring.” Computers & Chemical Engineering, 35, 12: 2680-2686.
Sushko, I., S. Novotarskyi, R. Körner, et al. (2010). “Applicability Domains for Classification Problems: Benchmarking of Distance to Models for AMES Mutagenicity Set." J. Chem. Inf. Model, 50: 2094–2111.
Cassano, A., A. Manganaro, T. Martin, et al. (2010). "The CAESAR Models for Developmental Toxicity." Chemistry Central Journal, 4, Suppl. 1: S4.
Zhu, H., T.M. Martin, D.M. Young, and A. Tropsha. (2009). "Combinatorial QSAR Modeling of Rat Acute Toxicity by Oral Exposure." Chemical Research in Toxicology, 22, 12: 1913–1921.
Benfenati, E., R. Benigni, D.M. Demarini, et al. (2009). "Predictive Models for Carcinogenicity and Mutagenicity: Frameworks, State-of-the-Art, and Perspectives." Journal of Environmental Science and Health Part C, 27, 2: 57–90.
Young, D.M. T.M. Martin, R. Venkatapathy, and P. Harten. (2008) "Are the Chemical Structures in Your QSAR Correct?" QSAR & Combinatorial Science, 27, 11-12: 1337–1345.
Martin, T.M., P. Harten, R. Venkatapathy, S. Das, and D.M. Young. (2008). "A Hierarchical Clustering Methodology for the Estimation of Toxicity." Toxicology Mechanisms and Methods, 18, 2: 251–266.
Martin, T.M. and D.M. Young. (2001). "Prediction of the Acute Toxicity (96-h LC50) of Organic Compounds in the Fathead Minnow (Pimephales promelas) Using a Group Contribution Method." Chemical Research in Toxicology, 14, 10: 1378–1385.