Reducing uncertainty in dose-response assessments by incorporating Bayesian benchmark dose modeling and in vitro data on population variability.

ABSTRACT: There are two primary sources of uncertainty in the interpretability of toxicity values, like the reference dose (RfD): estimates of the point of departure (POD) and the absence of chemical-specific human variability data. We hypothesize two solutions-employing Bayesian benchmark dose (BBMD) modeling to refine POD determination and combining high-throughput toxicokinetic modeling with population-based toxicodynamic in vitro data to characterize chemical-specific variability. These hypotheses were tested by deriving refined probabilistic estimates for human doses corresponding to a specific effect size (M) in the Ith population percentile (HD_M ^I) across 19 Superfund priority chemicals. HD_M ^I values were further converted to biomonitoring equivalen

SUBMITTER: Lu EH 

PROVIDER: S-EPMC11787958 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature