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Human health and the environment: Predicting plasma protein binding and metabolic clearance rates of environmentally relevant chemicals.
Citation:
Ingle, B., B. Veber, J. Nichols, AND R. Tornero-Velez. Human health and the environment: Predicting plasma protein binding and metabolic clearance rates of environmentally relevant chemicals. American Chemical Society Nat'l Meeting, San Diego, CA, San Diego, CA, March 13 - 17, 2016.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
In silico methods provide a rapid, inexpensive means of screening a wide array of environmentally relevant pollutants, pesticides, fungicides and consumer products for further toxicity testing. Physiologically based pharmacokinetic (PBPK) models bridge the gap between in vitro assays and in vivo models by accounting for many factors that control the ability of a xenobiotic to trigger potentially toxic adverse outcome pathways, including the fraction of a chemical unbound to plasma protein (Fub) and the intrinsic hepatic clearance rate (Clint). Limited Fub and Clint experimental data for environmentally relevant chemicals requires unique application of in silico techniques to the prediction of these chemical specific PBPK parameters. Quantitative structure-activity relationship (QSAR) models were constructed with large pharmaceutical training sets and then evaluated with environmentally relevant chemicals in the ToxCast dataset. Key similarities and differences between pharmaceuticals and the chemicals of diverse use within the ToxCast set were evaluated within the context of an applicability domain and corresponding reliability estimates. Both the Fub and Clint values predicted by these QSAR are mediated by multiple proteins/enzymes, and a consideration of the relevant biochemical context served to elucidate the strengths and weaknesses of the models. Thus, the impact of ionization states on plasma protein binding was explored, as was the relationship between semi-empirical C-H bond energies and metabolic rates. The presented in silico models for Fub and Clint delineate the chemicals space where reliable predictions can be made for the diverse array of environmentally relevant chemicals., which is critical to understand when constructing high-throughput toxicity models.
