An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes

Wu, Yiming; Song, Zidong; Little, John C.; Zhong, Min; Li, Hongwan; Xu, Ying

doi:10.1016/j.envint.2021.106748

Cited by 19 publications

(7 citation statements)

References 131 publications

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“…However, the relationship between indoor exposure to phthalate and metabolisms has been not established, and the prediction of internal exposure based on the external exposure to PAEs is difficult. Recently, some researchers have attempted to explore the issue of how PAEs entered human bodies based on some PBPK (physiologically based pharmacokinetic) models cooperated with intakes of dermal penetration and inhalation, as well as some experimental data [27][28][29]. However, the detrimental health outcomes of different intake pathways still cannot be compared in the mechanism.…”

Section: Introductionmentioning

confidence: 99%

Modeling di (2-ethylhexyl) Phthalate (DEHP) and Its Metabolism in a Body’s Organs and Tissues through Different Intake Pathways into Human Body

Kang

et al. 2022

IJERPH

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Phthalate esters (PAEs) are ubiquitous in indoor environments as plasticizers in indoor products. Residences are often exposed to indoor PAEs in the form of gas, particles, settled dust, and surface phases. To reveal the mechanism behind the accumulation of PAEs in different tissues or organs such as the liver and the lungs when a person exposed to indoor PAEs with different phases, a whole-body physiologically based pharmacokinetic model for PAEs is employed to characterize the dynamic process of phthalates by different intake pathways, including oral digestion, dermal adsorption, and inhalation. Among three different intake pathways, dermal penetration distributed the greatest accumulation of DEHP in most of the organs, while the accumulative concentration through oral ingestion was an order of magnitude lower than the other two doses. Based on the estimated parameters, the variation of di-ethylhexyl phthalate (DEHP) and mono (2-ethylhexyl) phthalate (MEHP) concentration in the venous blood, urine, the liver, the thymus, the pancreas, the spleen, the lungs, the brain, the heart, and the kidney for different intake scenarios was simulated. The simulated results showed a different accumulation profile of DEHP and MEHP in different organs and tissues and demonstrated that the different intake pathways will result in different accumulation distributions of DEHP and MEHP in organs and tissues and may lead to different detrimental health outcomes.

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Section: Introductionmentioning

confidence: 99%

Modeling di (2-ethylhexyl) Phthalate (DEHP) and Its Metabolism in a Body’s Organs and Tissues through Different Intake Pathways into Human Body

Kang

et al. 2022

IJERPH

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“…The acute oral MRL for DnBP is 500 mg per kg BW per d and for DEHP is 3.0 mg per kg BW per d. 63 For DEHP, this is clearly exceeded by the exposure of a child to DEHP in dust and also for adults by inhalation of particle-phase DEHP, indicating a potential high-risk scenario. The exposure results obtained from the DustEx tool could further be linked to physiologicallybased pharmacokinetic (PBPK) modeling approaches to predict body burden and compare with in vitro toxicity data for further risk management and chemical prioritization, as demonstrated by Wu et al (2021). 64 However, as discussed above, these exposure estimates have to be evaluated within the limitations of the tool and its parameters.…”

Section: Exposure Assessment With the Dustex Toolmentioning

confidence: 99%

“…The exposure results obtained from the DustEx tool could further be linked to physiologicallybased pharmacokinetic (PBPK) modeling approaches to predict body burden and compare with in vitro toxicity data for further risk management and chemical prioritization, as demonstrated by Wu et al (2021). 64 However, as discussed above, these exposure estimates have to be evaluated within the limitations of the tool and its parameters.…”

Section: Exposure Assessment With the Dustex Toolmentioning

confidence: 99%

A rapid micro chamber method to measure SVOC emission and transport model parameters

Wang

Eichler

et al. 2023

Environ. Sci.: Processes Impacts

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“…According to a recent review, 542 papers utilizing invitroDB have been published since 2006, covering topics such as toxic potential of chemicals, identification of contaminants for environmental monitoring, and computational toxicity prediction. The majority of invitroDB-based ML applications developed to date focused on relatively few target-specific endpoints and cytotoxicity. − Endocrine receptor systems, in particular, androgen and estrogen receptors, as well as carcinogenicity, hepatic steatosis, hepatotoxicity, immunotoxicity, developmental toxicity, neurotoxicity, and cardiotoxicity were the most widely studied adverse outcomes …”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data

Arturi

Hollender

2023

Environ. Sci. Technol.

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Nontarget high-resolution mass spectrometry screening (NTS HRMS/MS) can detect thousands of organic substances in environmental samples. However, new strategies are needed to focus time-intensive identification efforts on features with the highest potential to cause adverse effects instead of the most abundant ones. To address this challenge, we developed MLinvitroTox, a machine learning framework that uses molecular fingerprints derived from fragmentation spectra (MS2) for a rapid classification of thousands of unidentified HRMS/MS features as toxic/nontoxic based on nearly 400 target-specific and over 100 cytotoxic endpoints from ToxCast/Tox21. Model development results demonstrated that using customized molecular fingerprints and models, over a quarter of toxic endpoints and the majority of the associated mechanistic targets could be accurately predicted with sensitivities exceeding 0.95. Notably, SIRIUS molecular fingerprints and xboost (Extreme Gradient Boosting) models with SMOTE (Synthetic Minority Oversampling Technique) for handling data imbalance were a universally successful and robust modeling configuration. Validation of MLinvitroTox on MassBank spectra showed that toxicity could be predicted from molecular fingerprints derived from MS2 with an average balanced accuracy of 0.75. By applying MLinvitroTox to environmental HRMS/MS data, we confirmed the experimental results obtained with target analysis and narrowed the analytical focus from tens of thousands of detected signals to 783 features linked to potential toxicity, including 109 spectral matches and 30 compounds with confirmed toxic activity.

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An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes

Cited by 19 publications

References 131 publications

Modeling di (2-ethylhexyl) Phthalate (DEHP) and Its Metabolism in a Body’s Organs and Tissues through Different Intake Pathways into Human Body

Modeling di (2-ethylhexyl) Phthalate (DEHP) and Its Metabolism in a Body’s Organs and Tissues through Different Intake Pathways into Human Body

A rapid micro chamber method to measure SVOC emission and transport model parameters

Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data

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