A multi-tiered hierarchical Bayesian approach to derive toxic equivalency factors for dioxin-like compounds.

In 2005, the World Health Organization (WHO) re-evaluated Toxic Equivalency factors (TEFs) developed for dioxin-like compounds believed to act through the Ah receptor based on an updated database of relative estimated potency (REP)(REP database). This re-evalution identified the need to develop a consistent approach for dose-response modeling. Further, the WHO Panel discussed the significant heterogeneity of experimental datasets and dataset quality underlying the REPs in the database. There is a critical need to develop a quantitative, and quality weighted approach to characterize the TEF for each congener. To address this, a multi-tiered approach that combines Bayesian dose-response fitting and meta-regression with a machine learning model to predict REPS' quality categorizations was developed to predict the most likely relationship between each congener and its reference and derive model-predicted TEF uncertainty distributions. As a proof of concept, this 'Best-Estimate TEF workflow' was applied to the REP database to derive TEF point-estimates and characterizations of uncertainty for all congeners. Model-TEFs were similar to the 2005 WHO TEFs, with the data-poor congeners having larger levels of uncertainty. This transparent and reproducible computational workflow incorporates WHO expert panel recommendations and represents a substantial improvement in the TEF methodology.