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In Silico Prediction of Toxicokinetic Parameters for Environmentally Relevant Chemicals with Application to Risk-Based Prioritization
Ingle, B., B. Veber, J. Nichols, J. Wambaugh, AND R. Tornero-Velez. In Silico Prediction of Toxicokinetic Parameters for Environmentally Relevant Chemicals with Application to Risk-Based Prioritization. 2017 ISES Annual Meeting, Research Triangle Park, NC, October 15 - 19, 2017.
The highlighted research focuses on the development of a QSAR for plasma protein binding and intrinsic metabolic clearance, which it is highly relevant to the conference. The QSAR models feed into high-throughput toxicokinetic models that bridge the gap between exposure estimates and in vitro toxicity. The abstract will allow for the presentation of ongoing research from NERL/CED/HEDMB at the International Society for Exposure Science (ISES) 27th Annual Meeting. Not only will the presentation increase exposure for the research, it will also foster discussions with experts in the field of toxiokinetics and computational toxicology regarding next steps and possible improvements. It is a perfect opportunity to showcase work of NERL/CED and CSS/RED.
Toxicokinetic (TK) models can help bridge the gap between chemical exposure and measured toxicity endpoints, thereby addressing an important component of chemical risk assessments. The fraction of a chemical unbound by plasma proteins (Fub) and metabolic clearance rate (CLint) are critical TK parameters, accounting for aspects of the distribution, metabolism and excretion that determine in vivo tissue concentrations. Yet, limited TK data are available for environmentally relevant chemicals, including approximately 8000 chemicals with in vitro bioactivity data collected by Tox21. Quantitative structure-activity relationships (QSAR) for Fub and CLint were developed with in vitro assay data for both pharmaceuticals and chemicals in the ToxCast screening initiative using machine learning algorithms and open source descriptors. The models were shown to offer reliable in silico predictions of Fub and CLint for a diverse array of chemicals within the applicability domains. Incorporating the QSARs into TK models allowed a high throughput risk-based prioritization scheme informed by the margin between bioactive doses and human exposure. These QSAR models aid in the identification and prioritization of those chemicals with the highest probability of triggering adverse outcomes.