Literature DB >> 35175097

Filling in the Blanks: A New Tool to Predict Chemical Pathways from Production to Exposure.

Silke Schmidt.   

Abstract

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Year:  2022        PMID: 35175097      PMCID: PMC8852263          DOI: 10.1289/EHP10756

Source DB:  PubMed          Journal:  Environ Health Perspect        ISSN: 0091-6765            Impact factor:   9.031


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Detailed exposure and risk estimation studies for the many thousands of chemicals in our environment are not feasible. Thus, early mathematical models were developed to predict the environmental fate of understudied chemicals,[1] giving rise to the field of computational exposure science.[2] Most computational models, however, rely on multiple input parameters that are unknown for the vast majority of commercial chemicals.[3] In a study recently published in Environmental Health Perspectives,[4] researchers presented a new tool called PROduction-To-EXposure High Throughput (PROTEX-HT) that calculates model input parameters on the basis of two chemical-specific pieces of information: production tonnage and molecular structure. Chemicals travel a long road, from initial production, to uses in processes and products, and ultimately to disposal. PROTEX-HT aims to identify the emission sources, environmental media, and exposure routes of greatest concern along that road. Image: © Mark A. Leman/Getty Images. The authors compared median human exposure rates predicted by the new model with inferred exposure estimates of 95 chemicals measured in participants in the National Health and Nutrition Examination Survey (NHANES).[5] The list of chemicals included insecticides, intermediate and raw chemicals, solvents, plasticizers, and ingredients used in personal care products. The authors reported that 79% and 97% of the predictions deviated from U.S. general population exposure estimates by a factor of 10 and 100, respectively. First author Li Li, an assistant professor of environmental chemistry at the University of Nevada, Reno, notes that chemicals measured in humans at different times typically vary by a factor up to 100, according to NHANES data. PROTEX-HT, whose expected users include researchers, chemical manufacturers, and regulatory agencies, combines three existing models. The first is a substance flow analysis that simulates how chemicals move through the human socioeconomic system from production to end-of-lifetime disposal and converts chemical tonnage into emissions.[6] The second and third components describe how these emitted chemicals move and degrade within indoor[7] or outdoor[8] environments. Indoor media include air, carpet, flooring, and hard surfaces; outdoor media include air, soil, water, and sediment. Together, the three model components calculate expected chemical concentrations for each medium. Next, PROTEX-HT predicts biological concentrations within humans and other organisms from the media-specific concentrations. For broad risk estimation purposes, users of the tool can compare these predictions with existing in vitro or in vivo health reference levels from toxicological studies. This may help prioritize chemicals for more detailed analyses that require a larger number of input parameters and deliver more accurate estimates.[9,10] The model assumes that chemicals mix well enough to reach the same concentration throughout each environmental medium. This assumption may not hold for quickly degrading chemicals released by a single-point source, the authors noted.[4] The model also assumes that no residues of raw industrial chemicals are present in final consumer products. But for some chemicals in plastic production processes, such as bisphenol A, this assumption may result in underestimated concentrations of the chemical in indoor environments.[4] For Zhanyun Wang, a scientist at Empa, the Swiss Federal Laboratories for Materials Science and Technology, who was not involved in the study, PROTEX-HT is useful for ranking the relative importance of different emission sources, provided that accurate production tonnage information is available from chemical manufacturers. “I think more detailed models are necessary to estimate human exposure to chemicals, which is often of greatest concern for vulnerable subpopulations,” Wang says. Pesticide exposure, for example, may greatly exceed general population estimates for farmers and their neighbors and for people living in poor housing conditions that require regular applications of indoor pest control chemicals. PROTEX-HT estimates a “maximum allowable tonnage” for chemicals—the point beyond which a chemical’s production and use would cause unacceptable health risks. However, the authors acknowledge several points of uncertainty in these estimates, including physicochemical properties of chemicals that may complicate the simulation of emission rates, unclear toxicity thresholds, and potentially inaccurate chemical tonnage information. Miriam Diamond, a professor at the University of Toronto’s Department of Earth Sciences and School of the Environment, has published work[11] indicating that the computational models that form the basis of PROTEX-HT were not always reliable for certain types of molecules. Diamond, who was not involved in the current study, says the uncertainties surrounding PROTEX-HT preclude acting upon its maximum allowable tonnage estimates for the time being. Still, she says, “This study has assembled a rich body of knowledge into a single framework that advances our understanding of the fate of chemicals from use to exposure.” Although the current version provides exposure estimates for the general U.S. population, Li says the PROTEX framework may be adjusted to a variety of exposure simulations, including those involving vulnerable subpopulations. Going forward, the authors plan to examine in more detail potential sources of uncertainty in model predictions. Li adds, “There is always a need to further evaluate exposure models with more chemicals as more monitoring and biomonitoring data become available.”
  10 in total

1.  Tracking chemicals in products around the world: introduction of a dynamic substance flow analysis model and application to PCBs.

Authors:  Li Li; Frank Wania
Journal:  Environ Int       Date:  2016-07-16       Impact factor: 9.621

2.  A Model for Risk-Based Screening and Prioritization of Human Exposure to Chemicals from Near-Field Sources.

Authors:  Li Li; John N Westgate; Lauren Hughes; Xianming Zhang; Babak Givehchi; Liisa Toose; James M Armitage; Frank Wania; Peter Egeghy; Jon A Arnot
Journal:  Environ Sci Technol       Date:  2018-11-27       Impact factor: 9.028

3.  Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways.

Authors:  Caroline L Ring; Jon A Arnot; Deborah H Bennett; Peter P Egeghy; Peter Fantke; Lei Huang; Kristin K Isaacs; Olivier Jolliet; Katherine A Phillips; Paul S Price; Hyeong-Moo Shin; John N Westgate; R Woodrow Setzer; John F Wambaugh
Journal:  Environ Sci Technol       Date:  2018-12-24       Impact factor: 9.028

4.  High throughput heuristics for prioritizing human exposure to environmental chemicals.

Authors:  John F Wambaugh; Anran Wang; Kathie L Dionisio; Alicia Frame; Peter Egeghy; Richard Judson; R Woodrow Setzer
Journal:  Environ Sci Technol       Date:  2014-10-24       Impact factor: 9.028

5.  Towards a systematic understanding of the dynamic fate of polychlorinated biphenyls in indoor, urban and rural environments.

Authors:  Li Li; Jon A Arnot; Frank Wania
Journal:  Environ Int       Date:  2018-05-01       Impact factor: 9.621

6.  Policies for chemical hazard and risk priority setting: can persistence, bioaccumulation, toxicity, and quantity information be combined?

Authors:  Jon A Arnot; Don Mackay
Journal:  Environ Sci Technol       Date:  2008-07-01       Impact factor: 9.028

7.  Development and Evaluation of a Holistic and Mechanistic Modeling Framework for Chemical Emissions, Fate, Exposure, and Risk.

Authors:  Li Li; Alessandro Sangion; Frank Wania; James M Armitage; Liisa Toose; Lauren Hughes; Jon A Arnot
Journal:  Environ Health Perspect       Date:  2021-12-09       Impact factor: 9.031

8.  Incorporating High-Throughput Exposure Predictions With Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing.

Authors:  Barbara A Wetmore; John F Wambaugh; Brittany Allen; Stephen S Ferguson; Mark A Sochaski; R Woodrow Setzer; Keith A Houck; Cory L Strope; Katherine Cantwell; Richard S Judson; Edward LeCluyse; Harvey J Clewell; Russell S Thomas; Melvin E Andersen
Journal:  Toxicol Sci       Date:  2015-08-06       Impact factor: 4.849

9.  Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment.

Authors:  Peter P Egeghy; Linda S Sheldon; Kristin K Isaacs; Halûk Özkaynak; Michael-Rock Goldsmith; John F Wambaugh; Richard S Judson; Timothy J Buckley
Journal:  Environ Health Perspect       Date:  2015-11-06       Impact factor: 9.031

  10 in total

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