Toxicokinetic models serve a vital role in risk assessment by bridging the gap between chemical exposure and potentially toxic endpoints. While the fraction of a chemical that remains unbound by proteins in plasma (Fub) and intrinsic metabolic clearance rate (CLint) have a strong impact on toxicokinetics, limited data are available on these two parameters for environmentally relevant chemicals, including the nearly 8000 chemicals tested for in vitro bioactivity in the Tox21 program. Quantitative structure-activity relationships (QSAR) for these two important toxicokinetic parameters were developed to offer reliable in silico predictions for a diverse array of chemicals. Models were constructed with curated in vitro assay data for both pharmaceutical chemicals and environmentally relevant chemicals (ToxCast screening initiative). Machine learning algorithms, such as random forest and support vector machines, were coupled with open source molecular descriptors to provide reasonable estimates of Fub and CLint. Applicability domains defined the optimal chemical space for predictions, which covered environmental chemicals well. The application of these QSARs in the construction of toxicokinetic models for high throughput risk-based prioritization was showcased. By predicting the chemical-specific activity:exposure ratios with a combination of in vitro and in silico methods, it is possible to identify those chemicals with the highest probability of triggering an adverse outcome, thus prioritizing them for further experimental testing. Although the presented research has been subject the U.S. Environmental Protection Agency’s administrative review, the presented work is that of the authors and does not necessarily represent Agency policy.

Quantitative structure-activity relationships predictions of toxicokinetic parameters for risk-based prioritization

254th National Meeting (2017)

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