Citation:

Dawson, D., B. Ingle, K. Phillips, J. Nichols, J. Wambaugh, AND R. Tornero-Velez. Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 55(9):6505-6517, (2021). https://doi.org/10.1021/acs.est.0c06117

Impact/Purpose:

The highlighted research focuses on the development of open source QSAR models for intrinsic metabolic clearance and plasma protein binding. A case study shows how the QSAR models will feed into high-throughput toxicokinetic models that bridge the gap between exposure estimates and potential in vitro toxicity. Development of these models allows for transparent, reliable and efficient predictions for toxicokinetic parameters for environmentally relevant chemicals, which is needed for initial risk-prioritization screening. The work is relevant and accessible for program offices, regional partners, and the general public.

Description:

The intrinsic metabolic clearance rate (Clint) and the fraction of the chemical unbound in plasma (fup) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure–activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

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