Ensuring the safety of chemicals

Anastas, Paul T.; Teichman, Kevin Y.; Hubal, Elaine Cohen

doi:10.1038/jes.2010.28

Cited by 20 publications

(16 citation statements)

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“…3 In addition, there is no highquality in vivo toxicity data for roughly three quarters of these chemicals and there is not even limited toxicity data for one third of these chemicals. [4][5][6][7] However, as described by Judson 8 there are multiple approaches for characterizing the likely differential toxicities of green alternatives, including quantitative structure-activity relationships (QSARs) and highthroughput screening (HTS). The cross-agency Tox21 consortium 9 and the Toxicity Forecasting (ToxCast) program 10 of the United States Environmental Protection Agency (EPA) to date have evaluated over 8000 chemicals using HTS to identify potential hazard as quantified by in vitro bioactivity.…”

Section: Introductionmentioning

confidence: 99%

High-throughput screening of chemicals as functional substitutes using structure-based classification models

et al. 2017

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Section: Introductionmentioning

confidence: 99%

High-throughput screening of chemicals as functional substitutes using structure-based classification models

et al. 2017

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“…Historically, the estimated human health impact of these chemicals has been assessed through in vivo animal studies. Animal studies, however, are costly, laborious, impractical for evaluating large numbers of chemicals, and are being progressively eliminated due to their controversial nature (Anastas et al, 2010). However, over the past several years, the focus has switched to high-throughput in vitro screening (HTS) in order to identify chemical hazards and prioritize chemicals for additional in vivo testing (O'Brien et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays

Capuzzi

Politi

Isayev

et al. 2016

Front. Environ. Sci.

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The ability to determine which environmental chemicals pose the greatest potential threats to human health remains one of the major concerns in regulatory toxicology. Computational methods that can accurately predict a chemical's toxic potential in silico are increasingly sought-after to replace in vitro high-throughput screening (HTS) as well as controversial and costly in vivo animal studies. To this end, we have built Quantitative Structure-Activity Relationship (QSAR) models of 12 stress response and nuclear receptor signaling pathways toxicity assays as part of the 2014 Tox21 Challenge. Our models were built using the Random Forest, Deep Neural Networks and various combinations of descriptors and balancing protocols. All of our models were statistically significant for each of the 12 assays with the balanced accuracy in the range between 0.58 and 0.82. Our results also show that models built with Deep Neural Networks had higher accuracy than those developed with simple machine learning algorithms and that dataset balancing led to a significant accuracy decrease.

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“…It has been acknowledged globally that there is a need to develop a better understanding and management strategy with regard to the risk of chemicals to wildlife and human health (Anastas et al. ). Identifying the chemicals of greatest concern is a global challenge and has been highlighted as a priority topic to be solved by the Society of Environmental Toxicology and Chemistry (Brooks et al.…”

Section: Introductionmentioning

confidence: 99%

Which metal represents the greatest risk to freshwater ecosystem in bohai region of china?

Lü

Johnson

et al. 2017

Ecosyst Health Sustain

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Abstract. Metals discharged from industrial effluents, agricultural wastewater, and sewage runoff by rapid urbanization are of concern as contaminants of freshwater ecosystem because of their persistence and high toxicity to aquatic organisms. This study attempted to identify which metal posed the greatest risk to freshwater ecosystem in the Bohai Region, China. The metals arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), zinc (Zn), lead (Pb), nickel (Ni), mercury (Hg), iron (Fe), and manganese (Mn) were compared against norfloxacin and gamma-hexachlorocyclohexane (lindane). By comparing the median reported environmental and ecotoxicity concentrations, it showed that Cu, Fe, Zn, Mn, and Cr were the top five metals of concern. Of these, Cu was deemed to represent the highest risk and Hg the lowest risk. The risks for all metals were higher than those for norfloxacin and lindane. Almost all the metals except Hg had water concentrations that exceeded levels where ecotoxicity effects had been recorded in the literature. A comparison with the measurements across the UK rivers suggested that all metals examined had water concentrations about 5-to 10-fold higher than the UK median values except for Cu, Fe, Cd, and Pb. The Fuyang River, a tributary of the Haihe River Basin, seemed to be the location with the highest metal concentrations. However, comparing the post-2010 period to [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009], concentrations of all the metals had fallen except for Fe and Mn, so risks have decreased over the last 7 yr with the greatest improvements for Cd and Pb. While metals still pose high risks to freshwater ecosystem in this region, there is encouragement that some control measures are taken into effect.

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Ensuring the safety of chemicals

Cited by 20 publications

References 4 publications

High-throughput screening of chemicals as functional substitutes using structure-based classification models

High-throughput screening of chemicals as functional substitutes using structure-based classification models

QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays

Which metal represents the greatest risk to freshwater ecosystem in bohai region of china?

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