An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes.
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TL;DR: In this article, a probabilistic exposure model was developed to generate the distribution of multi-route exposures based on product phthalate concentrations, chemical properties, and human activities.
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About: This article is published in Environment International. The article was published on 10 Jul 2021. and is currently open access. The article focuses on the topics: DPHP & Phthalate.
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Citations
Application of ToxCast/Tox21 data for toxicity mechanism-based evaluation and prioritization of environmental chemicals: Perspective and limitations.
TL;DR: The ToxCast/Tox21 database as mentioned in this paper contains extensive data from over 1400 assays with numerous biological targets and activity data for over 9000 chemicals, which can be used for various purposes in the field of chemical prioritization and toxicity prediction.
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Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data.
TL;DR: MLinvitroTox as mentioned in this paper is 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.
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Modeling di (2-ethylhexyl) Phthalate (DEHP) and Its Metabolism in a Body’s Organs and Tissues through Different Intake Pathways into Human Body
TL;DR: A whole-body physiologically based pharmacokinetic model for PAEs 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.
Long-Term Exposure to Di(2- Ethylhexyl) Phthalate, Diisononyl Phthalate, and a Mixture of Phthalates Alters Estrous Cyclicity and/or Impairs Gestational Index and Birth Rate in Mice.
TL;DR: In this article , the effects of long-term, dietary phthalate exposure on estrous cyclicity and fertility in female mice were investigated, and the results indicated that chronic dietary exposure to phthalates altered estrous cycle and reduced fertility.
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Emerging Nitrate Contamination in Groundwater: Changing Phase in a Fast-Growing State of India.
C. D. Aju,Achu A. L,Mohammed Maharoof P,M.C. Raicy,Rajesh Reghunath,Girish Gopinath +5 more
TL;DR: This study assesses nitrate contamination in Kerala, India's groundwater, identifying zones of high nitrate concentrations and evaluating human health risks, particularly for children, through oral ingestion and dermal contact pathways, highlighting a substantial increase in risk over the years.
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References
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TL;DR: The preliminary data for 144 adults with no known exposure to DEHA suggests that adipic acid is also the main in vivo urinary metabolite, while MEHA, MEHHA, and MEOHA are only minor metabolites, and the use of these specific metabolites for assessing the exposure of DEHA may be limited to highly exposed populations.
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Evaluation and Optimization of Pharmacokinetic Models for in Vitro to in Vivo Extrapolation of Estrogenic Activity for Environmental Chemicals.
Warren Casey,Xiaoqing Chang,David G. Allen,Patricia Ceger,Neepa Choksi,Jui-Hua Hsieh,Barbara A. Wetmore,Stephen S. Ferguson,Michael J. DeVito,Catherine S. Sprankle,Nicole Kleinstreuer +10 more
TL;DR: Three pharmacokinetic models with varying complexities to extrapolate in vitro to in vivo dosimetry for a group of 29 ER agonists were evaluated, finding the simplest had the best overall performance for predicting both oral and injection LELs from guideline uterotrophic studies, and can be parameterized entirely using freely available in silico tools.
Development of a human physiologically based pharmacokinetic (PBPK) model for phthalate (DEHP) and its metabolites: A bottom up modeling approach.
TL;DR: A detailed human PBPK model for the DEHP and its major metabolites is developed by using a bottom-up modelling approach with the integration of a in vitro metabolic data and shows model good predictability power.
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Drug Distribution Part 2. Predicting Volume of Distribution from Plasma Protein Binding and Membrane Partitioning
Ken Korzekwa,Swati Nagar +1 more
TL;DR: Vss can be predicted for most drugs from plasma protein binding and microsomal partitioning according to a simple relationship that estimates the volume of distribution with the fraction of drug unbound in both plasma andmicrosomes.
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