Chemical Mixtures in Household Environments: In Silico Predictions and In Vitro Testing of Potential Joint Action on PPARγ in Human Liver Cells

Nanoplastics increase the toxicity of a pharmaceutical, at environmentally relevant concentrations - A mixture design with Daphnia magna

In aquatic environments, nanoplastics (NPls) can adsorb pharmaceuticals. However, throughout the scientific community, there is scarce knowledge about the interactive effects of the mixture nanoplastics (NPls) with pharmaceuticals to aquatic organisms. Therefore, this study aimed to investigate if the pharmaceutical diphenhydramine (DPH) toxicological effects alters when in presence of polystyrene NPls (PSNPls). To achieve this, Daphnia magna immobilization and different biochemical biomarkers (48-hours exposure) were assessed. Synergistic interactions occurred at environmentally relevant concentrations, PSNPls+DPH induced oxidative damage, whereas no effect was observed at single exposures. With the increase of PSNPls concentration, the DPH concentration causing 50% of effect (EC50) for organisms' immobilization decreased to 0.001 mg/L. In silico analysis suggested that the DPH toxicity to D. magna occurs via the sodium-dependent serotonin transporter. The results showed interactive effects between PSNPls and DPH (implying harmful effects on D. magna), allowing more thoughtful decisions by society and policymakers regarding plastics and pharmaceuticals.

Wrangling Whole Mixtures Risk Assessment: Recent Advances in Determining Sufficient Similarity

Human health risk assessments for complex mixtures can address real-world exposures and protect public health. While risk assessors typically prefer whole mixture approaches over component-based approaches, data from the precise exposure of interest are often unavailable and surrogate data from a sufficiently similar mixture(s) are required. This review describes recent advances in determining sufficient similarity of whole, complex mixtures spanning the comparison of chemical features, bioactivity profiles, and statistical evaluation to determine "thresholds of similarity". Case studies, including water disinfection byproducts, botanical ingredients, and wildfire emissions, are used to highlight tools and methods. Limitations to application of sufficient similarity in risk-based decision making are reviewed and recommendations presented for developing best practice guidelines.

Complex Mixtures and Multiple Stressors: Evaluating Combined Chemical Exposures and Cumulative Toxicity

The problem of chemical mixtures in the environment encompasses biological, analytical, logistical, and regulatory challenges, among others [...].

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.