Data-Driven Characterization of Genetic Variability in Disease Pathways and Pesticide-Induced Nervous System Disease in the United States Population.

Background: Genetic susceptibility to chemicals is incompletely characterized. However, nervous system disease development following pesticide exposure can vary in a population, implying some individuals may have higher genetic susceptibility to pesticide-induced nervous system disease.

Objectives: We aimed to build a computational approach to characterize single-nucleotide polymorphisms (SNPs) implicated in chemically induced adverse outcomes and used this framework to assess the link between differential population susceptibility to pesticides and human nervous system disease.

Potential for Machine Learning to Address Data Gaps in Human Toxicity and Ecotoxicity Characterization.

Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify impacts associated with chemical emissions and chemicals in products. However, the systematic application of ML-based approaches to fill chemical data gaps is still limited, and their potential for addressing a wide range of chemicals is unknown. We prioritized chemical-related parameters for chemical toxicity characterization to inform ML model development based on two criteria: (1) each parameter's relevance to robustly characterize chemical toxicity described by the uncertainty in characterization results attributable to each parameter and (2) the potential for ML-based approaches to predict parameter values for a wide range of chemicals described by the availability of chemicals with measured parameter data.

Probabilistic Points of Departure and Reference Doses for Characterizing Human Noncancer and Developmental/Reproductive Effects for 10,145 Chemicals.

Background: Regulatory toxicity values used to assess and manage chemical risks rely on the determination of the point of departure (POD) for a critical effect, which results from a comprehensive and systematic assessment of available toxicity studies. However, regulatory assessments are only available for a small fraction of chemicals.

Objectives: Using experimental animal data from the U.

High-content analysis shows synergistic effects of low perfluorooctanoic acid (PFOS) and perfluorooctane sulfonic acid (PFOA) mixture concentrations on human breast epithelial cell carcinogenesis.

Perfluoroalkyl substances (PFAS) have been associated with cancer, but the potential underlying mechanisms need to be further elucidated and include studies of PFAS mixtures. This mechanistic study revealed that very low concentrations (500 pM) of the binary PFOS and PFOA mixture induced synergistic effects on human epithelial breast cell (MCF-10A) proliferation. The cell proliferation was mediated by pregnane X receptor (PXR) activation, an increase in cyclin D1 and CDK6/4 levels, decrease in p21 and p53 levels, and by regulation of phosphor-Akt and β-catenin.

Advancing exposure data analytics and repositories as part of the European Exposure Science Strategy 2020-2030.

High-quality and comprehensive exposure-related data are critical for different decision contexts, including environmental and human health monitoring, and chemicals risk assessment and management. However, exposure-related data are currently scattered, frequently of unclear quality and structure, not readily accessible, and stored in various-partly overlapping-data repositories, leading to inefficient and ineffective data usage in Europe and globally. We propose strategic guidance for an integrated European exposure data production and management framework for use in science and policy, building on current and future data analysis and digitalization trends.

Toward Assessing Absolute Environmental Sustainability of Chemical Pollution.

Chemicals are widely used in modern society, which can lead to negative impacts on ecosystems. Despite the urgent relevance for global policy setting, there are no established methods to assess the absolute sustainability of chemical pressure at relevant spatiotemporal scales. We propose an absolute environmental sustainability framework (AESA) for chemical pollution where (1) the chemical pressure on ecosystems is quantified, (2) the ability for ecosystems to withstand chemical pressure (i.

Distinct genetic regions are associated with differential population susceptibility to chemical exposures.

Interactions between environmental factors and genetics underlie the majority of chronic human diseases. Chemical exposures are likely an underestimated contributor, yet gene-environment (GxE) interaction studies rarely assess their modifying effects. Here, we describe a novel method to profile the human genome and identify regions associated with differential population susceptibility to chemical exposures.

Concentration-response evaluation of ToxCast compounds for multivariate activity patterns of neural network function.

The US Environmental Protection Agency's ToxCast program has generated toxicity data for thousands of chemicals but does not adequately assess potential neurotoxicity. Networks of neurons grown on microelectrode arrays (MEAs) offer an efficient approach to screen compounds for neuroactivity and distinguish between compound effects on firing, bursting, and connectivity patterns. Previously, single concentrations of the ToxCast Phase II library were screened for effects on mean firing rate (MFR) in rat primary cortical networks.

Integration of curated and high-throughput screening data to elucidate environmental influences on disease pathways.

Addressing the complex relationship between public health and environmental exposure requires multiple types and sources of data. An important source of chemical data derives from high-throughput screening (HTS) efforts, such as the Tox21/ToxCast program, which aim to identify chemical hazard using primarily assays to probe toxicity. While most of these assays target specific genes, assessing the disease-relevance of these assays remains challenging.

Determination of chemical-disease risk values to prioritize connections between environmental factors, genetic variants, and human diseases.

Traditional methods for chemical risk assessment are too time-consuming and resource-intensive to characterize either the diversity of chemicals to which humans are exposed or how that diversity may manifest in population susceptibility differences. The advent of novel toxicological data sources and their integration with bioinformatic databases affords opportunities for modern approaches that consider gene-environment (GxE) interactions in population risk assessment. Here, we present an approach that systematically links multiple data sources to relate chemical risk values to diseases and gene-disease variants.

Associations between access to healthcare, environmental quality, and end-stage renal disease survival time: Proportional-hazards models of over 1,000,000 people over 14 years.

Prevalence of end-stage renal disease (ESRD) in the US increased by 74% from 2000 to 2013. To investigate the role of the broader environment on ESRD survival time, we evaluated average distance to the nearest hospital by county (as a surrogate for access to healthcare) and the Environmental Quality Index (EQI), an aggregate measure of ambient environmental quality composed of five domains (air, water, land, built, and sociodemographic), at the county level across the US. Associations between average hospital distance, EQI, and survival time for 1,092,281 people diagnosed with ESRD between 2000 and 2013 (age 18+, without changes in county residence) from the US Renal Data System were evaluated using proportional-hazards models adjusting for gender, race, age at first ESRD service date, BMI, alcohol and tobacco use, and rurality.