Using In Vitro High‐Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards

Abstract

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.

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs)

“…We do not agree with using this type of binary approach for evidence integration. Rather, for evidence integration we would recommend authors to use a Bayesian approach to integrate the information, such as a bootstrap meta-regression used by Burgoon et al (2016) to generate a reasonable distribution of concentration-response curves for identifying the threshold. Any concentrations above the threshold would be assigned a value fo 1, and concentrations below would be assigned a value of 0.…”

“…More specifically, we started with the AOP described by Burgoon et al (2016) and combined it with information from Reactome, and the literature. We used the literature information, especially knock-out, knock-in, and knock-down information to facilitate the expert-driven development of the conditional probability tables-see the Supporting Information for more details and references.…”

“…HSD17B4 performs beta-oxidation of various fatty acids (Möller, van Grunsven, Wanders, & Adamski, 2001). It is known that inhibition of beta-oxidation leads to steatosis and steatohepatitis (Rao & Reddy, 2001;Reddy, 2001), and that loss of HSD17B4 activity, a key component of the beta-oxidation machinery, is sufficient to infer steatosis (Burgoon et al, 2016).…”

“…Thus, AOPs describe the causal chain moving from the chemical–receptor interaction to the adverse outcome. Recently, AOPs have been used to make predictions about whether or not a chemical causes steatosis or skin sensitization (Burgoon, Druwe, Painter, & Yost, ; Strickland et al., ). In fact, the skin sensitization AOP has changed how chemicals are tested under the REACH annexes (European Chemicals Agency, ).…”

“…By using causal networks, we can identify the minimally sufficient set of KEs (MinSSKE) that need to be measured to predict an adverse outcome (Burgoon et al, 2016). We can also look at the network topology and devise scenarios to identify other less statistically certain sets of KEs than the MinSSKEs that may, after taking costs into account, become more optimal to run as assay batteries.…”