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

Egeghy, Peter; Sheldon, Linda; Isaacs, Kristin; Özkaynak, Halûk; Goldsmith, Michael‐Rock; Wambaugh, John F.; Judson, Richard S.; Buckley, Timothy J.

doi:10.1289/ehp.1509748

Cited by 78 publications

(57 citation statements)

References 49 publications

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“…Chemical contents within a given PCP type varied over several orders of magnitude yielding a large variation in applied doses, however, this variability could be lessened when comparing across chemicals with similar function as they may have similar chemical properties (Egeghy et al, 2015). For leave-on products there is a much smaller variation in PiF across products than there is in chemical content, however, applying the PiF can still change estimated intakes by an order of magnitude.…”

Section: Discussionmentioning

confidence: 99%

“…However, there is limited data available on chemical contents in products (not just PCPs) which are needed to estimate intakes (Egeghy et al, 2012); for example in this study, intakes were estimated for 325 of the chemicals that had chemical content data available, among the 518 PCP chemicals with estimated PiFs. Forthcoming efforts to estimate chemical contents in products will aid in increasing the amount of chemicals for which screening level exposures can be estimated (Egeghy et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

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High-throughput exposure modeling to support prioritization of chemicals in personal care products

et al. 2016

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We demonstrate the application of a high-throughput modeling framework to estimate exposure to chemicals used in personal care products (PCPs). As a basis for estimating exposure, we use the product intake fraction (PiF), defined as the mass of chemical taken by an individual or population per mass of a given chemical used in a product. We calculated use- and disposal- stage PiFs for 518 chemicals for five PCP archetypes. Across all product archetypes the use- and disposal- stage PiFs ranged from 10(-5) to 1 and 0 to 10(-3), respectively. There is a distinction between the use-stage PiF for leave-on and wash-off products which had median PiFs of 0.5 and 0.02 across the 518 chemicals, respectively. The PiF is a function of product characteristics and physico-chemical properties and is maximized when skin permeability is high and volatility is low such that there is no competition between skin and air losses from the applied product. PCP chemical contents (i.e. concentrations) were available for 325 chemicals and were combined with PCP usage characteristics and PiF yielding intakes summed across a demonstrative set of products ranging from 10(-8)-30 mg/kg/d, with a median of 0.1 mg/kg/d. The highest intakes were associated with body lotion. Bioactive doses derived from high-throughput in vitro toxicity data were combined with the estimated PiFs to demonstrate an approach to estimate bioactive equivalent chemical content and to screen chemicals for risk.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

High-throughput exposure modeling to support prioritization of chemicals in personal care products

et al. 2016

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“…QSAR models provide accurate predictions of measured endpoints instead of an independent ranking of biological activity. These quantitative approaches have also been used in other tasks, such as optimization of pharmacokinetics and toxicity profile ( Maltarollo et al, 2015 ; Egeghy et al, 2016 ; Chemi et al, 2017 ) and virtual screening ( Brogi et al, 2013 ; Melo-Filho et al, 2016 ; Neves et al, 2016 ; Zaccagnini et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

et al. 2018

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Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.

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“…In all, the currently available correlation methods to estimate D do not provide sufficient coverage of chemicals encapsulated in consumer products in different use scenarios (ie, ambient temperatures). Developing low‐tier, high‐throughput methods to estimate exposure to chemical in consumer products across a variety of chemical‐material combinations is a recent focus in various science‐policy fields such as computational exposure science and life cycle assessment (LCA) . Addressing the lack of methods to estimate D for a variety of chemical‐product scenarios, this study aims to develop a more comprehensive correlation method to estimate D for wide range of organic compounds in multiple solid materials.…”

Section: Introductionmentioning

confidence: 99%

A quantitative property-property relationship for the internal diffusion coefficients of organic compounds in solid materials

2017

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Indoor releases of organic chemicals encapsulated in solid materials are major contributors to human exposures and are directly related to the internal diffusion coefficient in solid materials. Existing correlations to estimate the diffusion coefficient are only valid for a limited number of chemical-material combinations. This paper develops and evaluates a quantitative property-property relationship (QPPR) to predict diffusion coefficients for a wide range of organic chemicals and materials. We first compiled a training dataset of 1103 measured diffusion coefficients for 158 chemicals in 32 consolidated material types. Following a detailed analysis of the temperature influence, we developed a multiple linear regression model to predict diffusion coefficients as a function of chemical molecular weight (MW), temperature, and material type (adjusted R of .93). The internal validations showed the model to be robust, stable and not a result of chance correlation. The external validation against two separate prediction datasets demonstrated the model has good predicting ability within its applicability domain (Rext2>.8), namely MW between 30 and 1178 g/mol and temperature between 4 and 180°C. By covering a much wider range of organic chemicals and materials, this QPPR facilitates high-throughput estimates of human exposures for chemicals encapsulated in solid materials.

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

Cited by 78 publications

References 49 publications

High-throughput exposure modeling to support prioritization of chemicals in personal care products

High-throughput exposure modeling to support prioritization of chemicals in personal care products

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

A quantitative property-property relationship for the internal diffusion coefficients of organic compounds in solid materials

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