Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high-throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline-based meta-regression can be used to integrate data across multiple assay replicates to generate a concentration-response curve. We used this analysis to calculate an in vitro point of departure of 0.751 μM and risk-specific in vitro concentrations of 0.29 μM and 0.28 μM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta-regression, may allow risk assessors to identify points of departure and risk-specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.
Overall, the evidence for multipollutant effects was often heterogeneous, and the limited number of studies inhibited making a conclusion about the nature of the relationship between pollutant combinations and cardiovascular disease.
Environmental and human health risk assessments benefit from using data that cross multiple scientific domains. Although individual data points may often be readily understood, the total picture can be difficult to envision. This is especially true with gaps in the data (e.g., with emerging substances such as engineered nanomaterials [ENM]), such that simply presenting only known information can result in a skewed picture. This study describes a method for building knowledge maps (KM) to visually summarize factors relevant to risk assessment in a relatively easy to interpret format. The KMs were created in the context of the comprehensive environmental assessment (CEA) approach for research planning and risk management of environmental contaminants. Recent applications of CEA to emerging substances such as engineered nanomaterials that have numerous data gaps have suggested that a more visually based depiction of information would improve the approach. We developed KM templates as a pilot project, to represent pertinent aspects of conceptual domains, and to highlight gaps in available information for one particular portion of a specific CEA application: the comparison of environmental transport, transformation, and fate of multiwalled carbon nanotubes (MWCNTs) and decabromodiphenyl ether as flame retardants. The results are 3 KM templates representing Physical Properties, Transport, and Transformation. The 3 templates were applied to both substances, resulting in a total of 6 KMs. In addition to presenting the KMs, this paper details the process used to generate them, to aid KM development for other sections of CEA applied to MWCNTs, or to apply the process to new CEA applications.
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