Advancing quantitative hazard banding using expanded probabilistic reference doses and high-throughput screening data...

Data unavailability in health reference values poses a significant challenge in chemical risk assessment. Hazard banding (HB) offers a practical solution by categorizing chemicals into bands that represent increasing levels of health hazard. However, the strength and validity of these bands are often based on health-based guidance values from small sample sizes due to data scarcity. This study aims to develop quantitative hazard bandings using an expanded dataset of probabilistic reference dose values (pRfD) and endocrine-related quantitative high-throughput screening (qHTS) data. We hypothesize that quantitative hazard bandings can accurately reflect chemical hazards' nature and severity. pRfD values (n = 10,145) and oral equivalent doses (OEDs) from qHTS data (n = 3,520) were categorized into quintiles to derive five hazard bands (HB_pRfD and HB_{qHTS_endo}). We evaluated the relationships between the assigned hazard bands and GHS health statements using heatmap and correlation analyses. Our findings suggest that HB_pRfD effectively captures the inherent severity of chemical hazards, while HB_{qHTS_endo} shows lower confidence in hazard characterization. Despite challenges with the discordance between animal toxicity data and qHTS data, integrating in vitro mechanistic evidence with animal data may improve predictive accuracy for higher-tier toxicity endpoints.