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An Intuitive Approach for Predicting Human Risk with the Tox21 10k Library
Sipes, N., J. Wambaugh, R. Pearce, S. Auerbach, B. Wetmore, J. Hsieh, A. Shapiro, D. Sboboda, M. DeVito, AND S. Ferguson. An Intuitive Approach for Predicting Human Risk with the Tox21 10k Library. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, (51):10786-10796, (2017).
This manuscript describes a collaboration between NTP and EPA to use in silico tools to predict chemical properties for the entire Tox21 library of chemicals such that EPA's high throughput toxicokinetics (httk) tool could be used to predict plasma concentration. The plasma concentrations resulting from exposure rates predicted by the ExpoCast project were compared with Tox21 high throughput screening data. The manuscript will be submitted to Environmental Science and Technology.
In vitro to in vivo extrapolation (IVIVE) analyses translating high-throughput screening (HTS) data to human relevance have been limited. This is the first time IVIVE approaches and exposure comparisons have explored the entire Tox21 federal collaboration’s 10,000 chemical dataset, incorporated assay response efficacy and quality of the concentration-response fits, and have quantitative anchoring to first address the likelihood of human in vivo interactions. This likelihood was assessed using a maximum blood concentration to in vitro response ratio approach (Cmax/AC50), similar to methods useful in decision-making for clinical drug-target interactions. Fraction unbound to plasma (fup) and intrinsic hepatic clearance (CLint¬¬) parameters were estimated in silico and incorporated into a 3-compartment toxicokinetic model to first predict Cmax for in vivo corroboration using therapeutic scenarios. Toward lower exposure scenarios, 36 compounds of 3,925 with clean HTS data gave ‘possible’ human in vivo interaction likelihoods that were lower than human exposures predicted in EPA’s ExpoCast program. A publicly available web application has been designed to provide all Tox21/ToxCast dose likelihood predictions. Overall, this approach provides an intuitive framework to relate in vitro toxicology data rapidly and quantitatively to exposures using either in vitro or in silico derived toxicokinetic (TK) parameters and can be thought of as a pillar toward a first estimation of plausible biological interaction in a high throughput risk assessment framework.
Record Details:Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL CENTER FOR COMPUTATIONAL TOXICOLOGY