Using In Vitro High-Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards
Burgoon, L., I. Druwe, K. Painter, AND E. Yost. Using In Vitro High-Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards. RISK ANALYSIS. John Wiley & Sons, Inc, Hoboken, NJ, 36(5):1-11, (2016).
This manuscript describes new methods for using high throughput screening, adverse outcome pathways, and ontologies to prioritize chemicals and for incorporating these technologies into chemical prioritization and risk screening.
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. By coupling qHTS data with adverse outcome pathways (AOPs) we can use ontologies to make predictions about potential hazards and to identify those assays which are sufficient to infer these same hazards. Once those assays are identified, we can use bootstrap natural spline-based metaregression to integrate the evidence across multiple replicates or assays (if a combination of assays are together necessary to be sufficient). In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene (B[k]F) may induce DNA damage and steatosis using qHTS data and two separate AOPs. We also demonstrate how bootstrap natural spline-based metaregression can be used to integrate the data across multiple assay replicates to generate a concentration-response curve. We used this analysis to calculate an internal point of departure of 0.751µM and risk-specific concentrations of 0.378µM for both 1:1,000 and 1:10,000 additive risk for B[k]F induced DNA damage based on the p53 assay. Based on the available evidence, we have moderately high confidence in this conclusion. Although we have identified that B[k]F may induce steatosis, based on a single HSD17B4 assay, we have little confidence in this result. Overall, these case study results suggest that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap metaregression, will allow risk assessors to identify points of departure and risk-specific concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we see the need for reverse dosimetry to estimate external doses, which can be used for risk screening purposes, such as through margin of exposure methods.
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