SHEDS-HT: An Integrated Probabilistic Exposure Model for Prioritizing Exposures to Chemicals with Near-Field and Dietary Sources

United States Environmental Protection Agency (USEPA) researchers are developing a strategy for high-throughput (HT) exposure-based prioritization of chemicals under the ExpoCast program. These novel modeling approaches for evaluating chemicals based on their potential for biologically relevant human exposures will inform toxicity testing and prioritization for chemical risk assessment. Based on probabilistic methods and algorithms developed for The Stochastic Human Exposure and Dose Simulation Model for Multimedia, Multipathway Chemicals (SHEDS-MM), a new mechanistic modeling approach has been developed to accommodate high-throughput (HT) assessment of exposure potential. In this SHEDS-HT model, the residential and dietary modules of SHEDS-MM have been operationally modified to reduce the user burden, input data demands, and run times of the higher-tier model, while maintaining critical features and inputs that influence exposure. The model has been implemented in R; the modeling framework links chemicals to consumer product categories or food groups (and thus exposure scenarios) to predict HT exposures and intake doses. Initially, SHEDS-HT has been applied to 2507 organic chemicals associated with consumer products and agricultural pesticides. These evaluations employ data from recent USEPA efforts to characterize usage (prevalence, frequency, and magnitude), chemical composition, and exposure scenarios for a wide range of consumer products. In modeling indirect exposures from near-field sources, SHEDS-HT employs a fugacity-based module to estimate concentrations in indoor environmental media. The concentration estimates, along with relevant exposure factors and human activity data, are then used by the model to rapidly generate probabilistic population distributions of near-field indirect exposures via dermal, nondietary ingestion, and inhalation pathways. Pathway-specific estimates of near-field direct exposures from consumer products are also modeled. Population dietary exposures for a variety of chemicals found in foods are combined with the corresponding chemical-specific near-field exposure predictions to produce aggregate population exposure estimates. The estimated intake dose rates (mg/kg/day) for the 2507 chemical case-study spanned 13 orders of magnitude. SHEDS-HT successfully reproduced the pathway-specific exposure results of the higher-tier SHEDS-MM for a case-study pesticide and produced median intake doses significantly correlated (<i>p</i> < 0.0001, <i>R</i>² = 0.39) with medians inferred using biomonitoring data for 39 chemicals from the National Health and Nutrition Examination Survey (NHANES). Based on the favorable performance of SHEDS-HT with respect to these initial evaluations, we believe this new tool will be useful for HT prediction of chemical exposure potential

Measuring Physicochemical Properties to Inform the Scope of Existing QSAR/QSPR Models

Presented at the Annual Society of Toxicology meetin

Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction

<div><p>Developing physiologically-based pharmacokinetic (PBPK) models for chemicals can be resource-intensive, as neither chemical-specific parameters nor <i>in vivo</i> pharmacokinetic data are easily available for model construction. Previously developed, well-parameterized, and thoroughly-vetted models can be a great resource for the construction of models pertaining to new chemicals. A PBPK knowledgebase was compiled and developed from existing PBPK-related articles and used to develop new models. From 2,039 PBPK-related articles published between 1977 and 2013, 307 unique chemicals were identified for use as the basis of our knowledgebase. Keywords related to species, gender, developmental stages, and organs were analyzed from the articles within the PBPK knowledgebase. A correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on pharmacokinetic-relevant molecular descriptors. Chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, multiple chemicals were selected to represent exact matches, close analogues, or non-analogues of the target case study chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals and their analogues were used to construct new models, and model predictions were compared to observed values. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities. Using suitable correlation metrics, we demonstrated that models of chemical analogues in the PBPK knowledgebase can guide the construction of PBPK models for other chemicals.</p></div

Case study with ethylbenzene.

<p>Comparing blood concentrations of ethylbenzene (triangle symbols) from rats exposed to 100 ppm ethylbenzene for four hours [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref029" target="_blank">29</a>] and simulated blood concentrations of ethylbenzene (solid lines) based on the (A) ethylbenzene PBPK model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref058" target="_blank">58</a>]; (B) xylene PBPK model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref058" target="_blank">58</a>]; (C) toluene PBPK model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref058" target="_blank">58</a>]; (D) benzene PBPK model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref058" target="_blank">58</a>]; (E) dichloromethane PBPK model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref059" target="_blank">59</a>]; and (F) methyl iodide PBPK model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref060" target="_blank">60</a>].</p

Trends of PBPK literatures.

<p>(A) The 2,039 PBPK-related articles are placed into one of three categories: (1) unique chemical PBPK papers (grey), pioneering articles in which specific chemical names have appeared for the first time; (2) non-unique chemical PBPK papers (yellow), articles in which chemical names have appeared in previous publications; or (3) PBPK related papers (green), articles that are not associated with specific chemical names. (B) Linear regression of the number of articles in three categories over time.</p

Case study with gefitinib.

<p>Comparing simulated (solid lines) and experimentally observed (triangle symbols) blood concentrations for compounds. PBPK models were extracted from the gefitinib study [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref061" target="_blank">61</a>], and executed to predict pharmacokinetics of gefitinib’s close-analogues (itraconazole, cocaine, diclofenac, 3,3'-diindolylmethane) and non-analoguse (perchlorate, phosphorothioate oligonucleotide, melamine, carbamateon). The experimental observations were extracted from PBPK literature listed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.t003" target="_blank">Table 3</a>.</p

Partition coefficients and metabolic parameters from existing PBPK models for ethylbenzene and selected analogues.

<p>Partition coefficients and metabolic parameters from existing PBPK models for ethylbenzene and selected analogues.</p

Model parameters for selected gefitinib’s close-analogues and non-analogues.

<p>Model parameters for selected gefitinib’s close-analogues and non-analogues.</p

Keywords extraction from PBPK literatures.

<p>The abstracts in the PBPK knowledgebase were analyzed to identify PBPK-associated word-stems: (A) Frequency of the top 10 species; (B) Frequency of the top 10 life stages; (C) Frequency of the top 10 compartments.</p

Physicochemical molecular descriptors.

<p>Summary of the values of eight physicochemical molecular descriptors, calculated using the Molecular Operating Environment (MOE), for 307 chemicals in the PBPK knowledgebase. The eight descriptors are molecular weight (MW), hydrogen bond acceptor count (hba), hydrogen bond donor count (hbd), number of rotatable bonds (nRotB), polar surface area or topological polar surface area (PSA), octanol:water partition coefficient (LogP), log transformation of solubility (logS), and area of van der Waal surface (vdw_area). (A) The original calculated descriptor values; (B) The normalized descriptor values using <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.e001" target="_blank">Eq 1</a> from the Methods section.</p