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Modeling Chemical Exposure Could Help Rank Toxicity Risk

Computational method uses monitoring data to help scientists start to prioritize toxicity studies of thousands of compounds

by Elizabeth K. Wilson
November 17, 2014 | A version of this story appeared in Volume 92, Issue 46

Credit: Shutterstock
A new mathematical model estimates human exposure levels of thousands of chemicals.
Photos of household product bottles.
Credit: Shutterstock
A new mathematical model estimates human exposure levels of thousands of chemicals.

A mathematical model that draws on data from human monitoring studies may help rank levels of human exposure to thousands of household and industrial chemicals circulating in the environment. The approach could greatly facilitate efforts to unpack the compounds’ toxicities (Environ. Sci. Technol. 2014, DOI: 10.1021/es503583j). Some 80,000 chemical substances are registered in the U.S. under the Toxic Substances Control Act. Gathering comprehensive data on human exposures to each of them for toxicity testing is prohibitive, requiring blood or urine samples of large populations. John F. Wambaugh of EPA’s National Center for Computational Toxicology, in Research Triangle Park, N.C., and colleagues began instead with a database of exposure levels of just over 100 parent chemicals found in 68 human urine samples. They used that information to group thousands of other compounds into categories such as antimicrobials, personal care products, or pesticides and factored in demographic data such as age and gender. Their model led to a list of 7,968 chemicals, ranked from highest to lowest potential exposure. At the top of the list were well-known problematic compounds such as diethyl phthalate and methylparaben. But the real value of the approach, the researchers say, is that it could help forecast exposures for thousands of chemicals on the list that don’t have well-explored exposure pathways.


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