Improving Exposure Models and Integrating Exposure and Risk Information for High-Throughput Chemical Screening (Prioritization) and Higher Tiered Assessments
Jon A. Arnot (PI), James M. Armitage, Trevor N. Brown. ARC Arnot Research and Consulting
Thousands of chemicals require ecological and human health assessment; however, relatively few measured data required for these assessments exist. There are practical limitations to testing and monitoring all of these chemicals; therefore, predictive models are required. Uncertainty in chemical exposure, hazard, and risk assessment exists whether the data are measured or predicted. In an effort to meet the American Chemistry Council’s Long-Range Research Initiative (ACC-LRI) goals to (i)advance scientific tools for chemical assessment (method development) and (ii) establish a basis for safe chemical use to enhance public confidence and decision-making (application, evaluation, testing, or “ground-truthing” of methods), this project aims to improve models and data (key determinants) for chemical assessment.
Through this project we seek to advance exposure science through the development and evaluation of chemical property databases and through the development, testing, and improvement of predictive quantitative structure–activity relationship (QSAR) and mass balance models for chemical assessment. We also seek to integrate exposure and hazard information for risk assessment through the development and evaluation of new tools for linking the data (i.e., new data and models for metabolism/biotransformation and degradation rates) and to improve chemical safety testing technologies (i.e., develop and apply in vitro models to better interpret in vitro bioassay data, such as ToxCast data). We propose to evaluate (ground-truth) models used for chemical assessments to: (i) address uncertainty in the models and available measured data, (ii) further establish a scientific foundation for applying models for decision-making, (iii)foster public (and regulatory) confidence, and (iv) to help prioritize future research needs. The specific components that comprise this project are as follows:
1. Evaluate dermal permeation models for chemicals in consumer products: We will develop and evaluate a database of measured dermal permeation coefficients (KP) for organic chemicals and evaluate KP models that can be used in ExpoDat, U.S. EPA’s SHEDS-HT model, and other high-throughput exposure models. This research will discuss the uncertainties in current KP data and models and recommend research needs to improve KP data and models.
2. Develop models to predict biotransformation (metabolism) rates from chemical structure to improve (i) Oral Equivalent Dose (OED) estimates derived from in vitro bioassays, (ii) exposure estimates, and (iii) bioaccumulation assessment: We will use in vitro-to-in vivo extrapolation (IVIVE) models to estimate liver clearance (CLH) from newly developed and evaluated in vitro biotransformation rate assay datasets. We will apply QSAR methods to the in vitro and CLH datasets to develop “validated” QSARs following OECD guidance. We will examine relationships between in vitro rate data (e.g., EPA’s ToxCast) and our in vivo whole body human biotransformation rate estimates and QSARs. We will use our biotransformation rate databases and QSARs to parameterize a bioaccumulation model for an air-breathing organism (e.g., human) to demonstrate the key role biotransformation has lowering bioaccumulation (exposure dose) potential.
3. Improve physiologically based pharmacokinetic (PBPK) models: We will develop and evaluate a generic PBPK model for humans for neutral and ionogenic organic chemicals that can be parameterized and used for “data poor chemicals” with data and QSARs we are developing from recent and on-going related research.
4. Model evaluations (ground-truthing): We will further evaluate (ground-truth) exposure models using select legacy pollutants (relatively data rich chemicals for model evaluation purposes) for both far-field (RAIDAR) and near-field (RAIDAR-ICE) models. These model evaluations will include benchmarking exposure estimates with biomonitoring data.
5. Improve EPA’s ToxCast in vitro bioactivity estimates: We will parameterize an in vitro mass balance model we recently developed with ToxCast data and test parameters to calculate more reliable dissolved and cell/tissue concentrations corresponding with bioactivity. This work will better inform all applications of ToxCast data.
6. Improve QSARs for partitioning and environmental degradation: We seek to advance chemical assessment science by developing and evaluating QSARs for chemical partitioning properties that are “internally consistent” and by developing and evaluating QSARs for environmental aerobic biodegradation half-lives using empirical and expert knowledge to train and test the QSARs.
Implications: This research builds capacity to evaluate and better understand chemical hazard, exposure, and potential risk to humans and the environment through the development and evaluation of measured datasets and predictive models.
Collaborations: U.S. Environmental Protection Agency
Key words: hazard assessment, exposure and risk estimation, multimedia mass balance models, QSARs, in vitro, model evaluation, biotransformation
Project start and end dates: April 1, 2015 – March 31, 2018
Peer-reviewed publication(s):
Brown, TN; Armitage, JM; Kircanski, I; Egeghy, P; Isaacs, K. Arnot JA. A review of screening-level dermal permeation data and models. In prep.
Other publication(s): None to date.
Conference and Workshop Presentations:
Falls, A; Armitage, JM; Gouin, T; Bonnell, M; Arnot, JA. Applying and evaluating the RAIDAR model to address data gaps for chemical exposure assessment: A case study for dechlorane plus. Society of Environmental Toxicology and Chemistry (SETAC) Conference, Salt Lake City, November 1-5, 2015.
Abstract creation date: September 2015
This abstract was prepared by the principal investigator for the project. Please see www.americanchemistry.com/lri for more information about the LRI.