The volume and variety of manufactured chemicals is increasing, although little is known about the risks associated with the frequency and extent of human exposure to most chemicals. The EPA and the recent signing of the Lautenberg Act have both signaled the need for high-throughput methods to characterize and screen chemicals based on exposure potential, such that more comprehensive toxicity research can be informed. Prior work of Mitchell et al. using multicriteria decision analysis tools to prioritize chemicals for further research is enhanced here, resulting in a high-level chemical prioritization tool for risk-based screening. Reliable exposure information is a key gap in currently available engineering analytics to support predictive environmental and health risk assessments. An elicitation with 32 experts informed relative prioritization of risks from chemical properties and human use factors, and the values for each chemical associated with each metric were approximated with data from EPA's CP_CAT database. Three different versions of the model were evaluated using distinct weight profiles, resulting in three different ranked chemical prioritizations with only a small degree of variation across weight profiles. Future work will aim to include greater input from human factors experts and better define qualitative metrics.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7076565 | PMC |
http://dx.doi.org/10.1111/risa.13001 | DOI Listing |
J Hazard Mater
January 2025
School of Environmental Studies, China University of Geosciences, Wuhan, Hubei 430074, China; National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China. Electronic address:
Activated sludge enriches vast amounts of micropollutants (MPs) when wastewater is treated, posing potential environmental risks. While standard methods typically focus on target analysis of known compounds, the identity, structure, and concentration of transformation products (TPs) of MPs remain less understood. Here, we employed a novel approach that integrates machine learning for the quantification of nontarget TPs with advanced target, suspect, and nontarget screening strategies.
View Article and Find Full Text PDFIntegr Environ Assess Manag
January 2025
United States Geological Survey, Upper Midwest Water Science Center, Madison, WI, United States.
Aircraft anti-icers and pavement deicers improve the safety of airport operations during winter precipitation events. Runoff containing these products can contribute elevated biochemical oxygen demand (BOD) to receiving streams. We monitored runoff from Milwaukee Mitchell International Airport at one upstream site, three outfall sites, and one downstream site from 2005 to 2022 for BOD, chemical oxygen demand (COD), and freezing point depressants used in deicing and anti-icing fluids to determine the primary sources of BOD and COD in the receiving stream.
View Article and Find Full Text PDFIntegr Environ Assess Manag
January 2025
European Centre for Ecotoxicology and Toxicology of Chemicals, Brussels, Belgium.
SimpleTreat has become a common tool used in ecological risk assessments to estimate the removal efficiency of a chemical from a secondary wastewater treatment plant and hence inform on release to the environment. Organization A, Organization B, and Organization C performed a comparative study of SimpleTreat predictions and parameter selection methodologies across the three organizations. SimpleTreat versions 3.
View Article and Find Full Text PDFDigit Discov
January 2025
School of Natural and Environmental Sciences, Newcastle University Newcastle Upon Tyne NE1 7RU UK
FEgrow is an open-source software package for building congeneric series of compounds in protein binding pockets. For a given ligand core and receptor structure, it employs hybrid machine learning/molecular mechanics potential energy functions to optimise the bioactive conformers of supplied linkers and functional groups. Here, we introduce significant new functionality to automate, parallelise and accelerate the building and scoring of compound suggestions, such that it can be used for automated design.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400 Illkirch, France.
Designing chemically novel and synthesizable ligands from the largest possible chemical space is a major issue in modern drug discovery to identify early hits that are easily amenable to medicinal chemistry optimization. Starting from the sole three-dimensional structure of a protein binding site, we herewith describe a fully automated active learning protocol to propose the commercial chemical reagents and one-step organic chemistry reactions necessary to enumerate target-specific primary hits from ultralarge chemical spaces. When applied in different scenarios (single transform and multiple transforms) addressing chemical spaces of various sizes (from 670 million to 4.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!