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Collaborative Evaluation of In Silico Predictions for High Throughput Toxicokinetics
Citation:
Wambaugh, J., N. Sipes, J. Arnot, T. Brown, D. Dawson, S. Davidson, M. Devito, J. DiBella, S. Ferguson, M. Goldsmith, Chris Grulke, R. Judson, M. Lawless, K. Mansouri, G. Patlewicz, E. Papa, P. Pradeep, A. Sangion, R. Sayre, R. Tornero-Velez, AND B. Wetmore. Collaborative Evaluation of In Silico Predictions for High Throughput Toxicokinetics. QSAR 2021 International Workshop on QSAR in Environmental and Health Sciences, Virtual, NC, June 07 - 10, 2021. https://doi.org/10.23645/epacomptox.15077418
Impact/Purpose:
Presentation to the QSAR 2021 International Workshop on QSAR in Environmental and Health Sciences June 2021.
Description:
Chemical hazard, exposure, and the shape of the dose-response curve are typically evaluated to predict potential human health risk. The dose-response curve and estimates of internal exposure (dose) depend upon chemical-specific toxicokinetics – the absorption, distribution, metabolism, and excretion (ADME) of chemicals. Unfortunately, this information is not available for most of the chemicals in commerce and the environment. However, a combination of in vitro experimentation, in silico methods, and generic toxicokinetic modeling has allowed for “high throughput toxicokinetics (HTTK)” to predict these properties for drug leads. The past decade has witnessed an explosion of in vitro toxicokinetic data for the non-pharmaceutical chemical space and new tools are being developed to make predictions based upon these new data. The advent of a new database for in vivo chemical concentration vs. time data (Sayre et al., 2020) allows empirical evaluation of the new tools. In this collaborative trial we systematically compare a variety of tools in order to establish overall predictivity as well as estimate chemical-specific domains (that is, some tools may be more appropriate for certain chemical classes). The tools consist of several in silico QSAR predictions and their usage in a variety of public and commercially-available HTTK models. The end goal is to improve predictive models and develop consensus predictions that include quantified, chemical-specific assessment of uncertainty. Carefully evaluated in silico HTTK methods represent a powerful new approach methodology to inform of potential risk posed by thousands of data-poor chemicals. This abstract may not reflect U.S. EPA policy.
URLs/Downloads:
DOI: Collaborative Evaluation of In Silico Predictions for High Throughput Toxicokinetics
WAMBAUGH_QSAR2021_TKQSARCOLLABORATIVE2.PDF (PDF, NA pp, 345.767 KB, about PDF)