You are here:
Quantitative Model of Systemic Toxicity Using ToxCast and ToxRefDB (SOT)
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
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.
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.
URLs/Downloads: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)
Record Details:Record Type: DOCUMENT (PRESENTATION/POSTER)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL CENTER FOR COMPUTATIONAL TOXICOLOGY