Prioritizing Chemicals and Data Requirements for Screening-Level Exposure and Risk Assessment

Arnot, Jon A.; Brown, Trevor N.; Wania, Frank; Breivik, Knut; McLachlan, Michael S.

doi:10.1289/ehp.1205355

Cited by 92 publications

(109 citation statements)

References 25 publications

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“…For example, the influence of external exposure conditions can be modeled using chemical fate and speciation models (Mackay and Webster, 2003;Fu et al, 2009;Rendal et al, 2011;Arnot et al, 2012;Hendren et al, 2013). A wide variety of chemical structure-based in silico prediction models are available that allow evaluating certain ADME characteristics (Kulkarni et al, 2005;Gleeson, 2008;Yang et al, 2012;Piechota et al, 2013) or a chemical's ability to interact with a particular MIE-associated target (Ellison et al, 2011;Enoch and Cronin, 2012;Rana et al, 2012;Vedani et al, 2012;Vinken, 2013;Wu et al, 2013).…”

Section: Integration With Computational Modelsmentioning

confidence: 99%

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

et al. 2015

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To elucidate the effects of chemicals on populations of different species in the environment, efficient testing and modeling approaches are needed that consider multiple stressors and allow reliable extrapolation of responses across species. An adverse outcome pathway (AOP) is a concept that provides a framework for organizing knowledge about the progression of toxicity events across scales of biological organization that lead to adverse outcomes relevant for risk assessment. In this paper, we focus on exploring how the AOP concept can be used to guide research aimed at improving both our understanding of chronic toxicity, including delayed toxicity as well as epigenetic and transgenerational effects of chemicals, and our ability to predict adverse outcomes. A better understanding of the influence of subtle toxicity on individual and population fitness would support a broader integration of sublethal endpoints into risk assessment frameworks. Detailed mechanistic knowledge would facilitate the development of alternative testing methods as well as help prioritize higher tier toxicity testing. We argue that targeted development of AOPs supports both of these aspects by promoting the elucidation of molecular mechanisms and their contribution to relevant toxicity outcomes across biological scales. We further discuss information requirements and challenges in application of AOPs for chemical- and site-specific risk assessment and for extrapolation across species. We provide recommendations for potential extension of the AOP framework to incorporate information on exposure, toxicokinetics and situation-specific ecological contexts, and discuss common interfaces that can be employed to couple AOPs with computational modeling approaches and with evolutionary life history theory. The extended AOP framework can serve as a venue for integration of knowledge derived from various sources, including empirical data as well as molecular, quantitative and evolutionary-based models describing species responses to toxicants. This will allow a more efficient application of AOP knowledge for quantitative chemical- and site-specific risk assessment as well as for extrapolation across species in the future.

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Section: Integration With Computational Modelsmentioning

confidence: 99%

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

et al. 2015

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“…8,9 Mechanistic mass balance models that combine environmental fate and human food chain bioaccumulation models for simulating far-field human exposure to chemicals in outdoor environmental media (food, water, and air) have been developed 10,11 and applied to thousands of chemicals. 12 Significant human exposure to chemicals also occurs indoors and thus near-field exposure pathways (e.g., inhalation, nondietary ingestion, and dermal permeation) need to be considered when estimating total exposure. 13−17 Models and metrics to quantify chemical fate 18,19 and human exposure indoors 20,21 have been developed.…”

Section: Introductionmentioning

confidence: 99%

Model for Screening-Level Assessment of Near-Field Human Exposure to Neutral Organic Chemicals Released Indoors

Zhang

Arnot

Wania

2014

Environ. Sci. Technol.

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Screening organic chemicals for hazard and risk to human health requires near-field human exposure models that can be readily parametrized with available data. The integration of a model of human exposure, uptake, and bioaccumulation into an indoor mass balance model provides a quantitative framework linking emissions in indoor environments with human intake rates (iRs), intake fractions (iFs) and steady-state concentrations in humans (C) through consideration of dermal permeation, inhalation, and nondietary ingestion exposure pathways. Parameterized based on representative indoor and adult human characteristics, the model is applied here to 40 chemicals of relevance in the context of human exposure assessment. Intake fractions and human concentrations (C(U)) calculated with the model based on a unit emission rate to air for these 40 chemicals span 2 and 5 orders of magnitude, respectively. Differences in priority ranking based on either iF or C(U) can be attributed to the absorption, biotransformation and elimination processes within the human body. The model is further applied to a large data set of hypothetical chemicals representative of many in-use chemicals to show how the dominant exposure pathways, iF and C(U) change as a function of chemical properties and to illustrate the capacity of the model for high-throughput screening. These simulations provide hypotheses for the combination of chemical properties that may result in high exposure and internal dose. The model is further exploited to highlight the role human contaminant uptake plays in the overall fate of certain chemicals indoors and consequently human exposure.

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“…Major differences between RAIDAR and both USEtox and PRoTEGE exists for DDT, gamma-Hexachlorocyclohexane, Hexabromocyclododecane, Hexachlorobenzene, octaBDE, Pentachlorophenol, pentaBDE, Tetrabromobisphenol A, Benzene, 1-methoxy-4-(2-propen-1-yl) and formaldehyde. To clarify, for most chemicals (31 of 41), the RAIDAR results are calculated using EU Technical Guidance Document emission factor estimates and global production volume estimates as outlined in Arnot et al (2012); hence, model comparisons of the relative rankings are not directly comparable. There were only four chemicals across these 3 models where RAIDAR was parameterized with US emission data.…”

Section: Resultsmentioning

confidence: 99%

Comparison of modeling approaches to prioritize chemicals based on estimates of exposure and exposure potential

Mitchell

Arnot

Jolliet

et al. 2013

Science of The Total Environment

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While only limited data are available to characterize the potential toxicity of over 8 million commercially available chemical substances, there is even less information available on the exposure and use-scenarios that are required to link potential toxicity to human and ecological health outcomes. Recent improvements and advances such as high throughput data gathering, high performance computational capabilities, and predictive chemical inherency methodology make this an opportune time to develop an exposure-based prioritization approach that can systematically utilize and link the asymmetrical bodies of knowledge for hazard and exposure. In response to the US EPA’s need to develop novel approaches and tools for rapidly prioritizing chemicals, a “Challenge” was issued to several exposure model developers to aid the understanding of current systems in a broader sense and to assist the US EPA’s effort to develop an approach comparable to other international efforts. A common set of chemicals were prioritized under each current approach. The results are presented herein along with a comparative analysis of the rankings of the chemicals based on metrics of exposure potential or actual exposure estimates. The analysis illustrates the similarities and differences across the domains of information incorporated in each modeling approach. The overall findings indicate a need to reconcile exposures from diffuse, indirect sources (far-field) with exposures from directly, applied chemicals in consumer products or resulting from the presence of a chemical in a microenvironment like a home or vehicle. Additionally, the exposure scenario, including the mode of entry into the environment (i.e. through air, water or sediment) appears to be an important determinant of the level of agreement between modeling approaches.

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Prioritizing Chemicals and Data Requirements for Screening-Level Exposure and Risk Assessment

Cited by 92 publications

References 25 publications

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology

Model for Screening-Level Assessment of Near-Field Human Exposure to Neutral Organic Chemicals Released Indoors

Comparison of modeling approaches to prioritize chemicals based on estimates of exposure and exposure potential

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