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Quantitative Model of Systemic Toxicity Using ToxCast and ToxRefDB (SOT)
Citation:
Truong, L., G. Ouedraogo, S. Loisel-Joubert, AND M. Martin. Quantitative Model of Systemic Toxicity Using ToxCast and ToxRefDB (SOT). Presented at SOT 2014 Annual Meeting, Phoenix, AZ, March 23 - 27, 2014. https://doi.org/10.23645/epacomptox.5193358
Impact/Purpose:
In collaboration with L’Oreal, a quantitative model of systemic toxicity was developed using no effect levels (NEL) from ToxRefDB for 633 chemicals with HTS data, chemical fingerprints, and a subset with reverse toxicokinetic (RtK) data.
Description:
EPA’s ToxCast program profiles the bioactivity of chemicals in a diverse set of ~700 high throughput screening (HTS) assays. In collaboration with L’Oreal, a quantitative model of systemic toxicity was developed using no effect levels (NEL) from ToxRefDB for 633 chemicals with HTS data, chemical fingerprints, and a subset with reverse toxicokinetic (RtK) data. Floor and ceiling performance baselines (95% Confidence Intervals) were estimated to be 5 and 3 orders of magnitude uncertainty (OMU), respectively based on historical NEL distributions and reproducibility across study type and species. An initial read-across model was developed using chemical fingerprints to identify structurally similar neighbor NEL values resulting in 4.6 OMU, a 1/5th reduction in model uncertainty based on our performance baselines. HTS data was then incorporated into the model using 74 groups of assays based on biology (ie: response data, gene families), technology annotation (assay mechanisms, signal directions), and assay confounders (oxidative stress, cytotoxicity). For each assay grouping, a mean activity value was computed and adjusted for confounders. Incorporating HTS data with read-across resulted in a 4.2 OMU, a total reduction in model uncertainty of 2/5th. RtK steady-state concentrations were then incorporated to adjust in vitro concentration (uM) to in vivo dose (mg/kg/day). Although RtK values were only available for a subset of the total chemical set (211), including RtK further lowered the overall model uncertainty to 3.7 OMU, roughly 3/5th of the total model uncertainty we expect to be able to reduce. Herein, we have identified a model that incorporates HTS (dynamics), read-across (chemistry) and RtK (kinetics) to predict systemic NEL harnessing and incorporating the power of both new and existing data. This abstract does not necessarily represent EPA policy.
URLs/Downloads:
DOI: Quantitative Model of Systemic Toxicity Using ToxCast and ToxRefDB (SOT)
L_Truong_et_al_SOT_poster_abstract (PDF, NA pp, 56 KB, about PDF)
L_Truong_et_al_SOT_poster (PDF, NA pp, 1080 KB, about PDF)