Biogeographical analysis of chemical co-occurrence data to identify priorities for mixtures research

Exposure modelling in Europe: how to pave the road for the future as part of the European Exposure Science Strategy 2020-2030

Exposure models are essential in almost all relevant contexts for exposure science. To address the numerous challenges and gaps that exist, exposure modelling is one of the priority areas of the European Exposure Science Strategy developed by the European Chapter of the International Society of Exposure Science (ISES Europe). A strategy was developed for the priority area of exposure modelling in Europe with four strategic objectives. These objectives are (1) improvement of models and tools, (2) development of new methodologies and support for understudied fields, (3) improvement of model use and (4) regulatory needs for modelling. In a bottom-up approach, exposure modellers from different European countries and institutions who are active in the fields of occupational, population and environmental exposure science pooled their expertise under the umbrella of the ISES Europe Working Group on exposure models. This working group assessed the state-of-the-art of exposure modelling in Europe by developing an inventory of exposure models used in Europe and reviewing the existing literature on pitfalls for exposure modelling, in order to identify crucial modelling-related strategy elements. Decisive actions were defined for ISES Europe stakeholders, including collecting available models and accompanying information in a living document curated and published by ISES Europe, as well as a long-term goal of developing a best-practices handbook. Alongside these actions, recommendations were developed and addressed to stakeholders outside of ISES Europe. Four strategic objectives were identified with an associated action plan and roadmap for the implementation of the European Exposure Science Strategy for exposure modelling. This strategic plan will foster a common understanding of modelling-related methodology, terminology and future research in Europe, and have a broader impact on strategic considerations globally.

Data Mining Approaches for Assessing Chemical Coexposures Using Consumer Product Purchase Data

The use of consumer products presents a potential for chemical exposures to humans. Toxicity testing and exposure models are routinely employed to estimate risks from their use; however, a key challenge is the sparseness of information concerning who uses products and which products are used contemporaneously. Our goal was to demonstrate a method to infer use patterns by way of purchase data. We examined purchase patterns for three types of personal care products (cosmetics, hair care, and skin care) and two household care products (household cleaners and laundry supplies) using data from 60,000 households collected over a one-year period in 2012. The market basket analysis methodology frequent itemset mining (FIM) was used to identify co-occurring sets of product purchases for all households and demographic groups based on income, education, race/ethnicity, and family composition. Our methodology captured robust co-occurrence patterns for personal and household products, globally and for different demographic groups. FIM identified cosmetic co-occurrence patterns captured in prior surveys of cosmetic use, as well as a trend of increased diversity of cosmetic purchases as children mature to teenage years. We propose that consumer product purchase data can be mined to inform person-oriented use patterns for high-throughput chemical screening applications, for aggregate and combined chemical risk evaluations.

High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity

High-throughput and computational tools provide a new opportunity to calculate combined bioactivity of exposure to diverse chemicals acting through a common mechanism. We used high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA's Toxicity and Exposure Forecaster (ToxCast and ExpoCast) to estimate combined estrogen receptor (ER) agonist activity of non-pharmaceutical chemical exposures for the general U.S. population. High-throughput toxicokinetic (HTTK) data provide conversion factors that relate bioactive concentrations measured in vitro (µM), to predicted population geometric mean exposure rates (mg/kg/day). These data were available for 22 chemicals with ER agonist activity and were estimated for other ER bioactive chemicals based on the geometric mean of HTTK values across chemicals. For each chemical, ER bioactivity across ToxCast assays was compared to predicted population geometric mean exposure at different levels of in vitro potency and model certainty. Dose additivity was assumed in calculating a Combined Exposure-Bioactivity Index (CEBI), the sum of exposure/bioactivity ratios. Combined estrogen bioactivity was also calculated in terms of the percent maximum bioactivity of chemical mixtures in human plasma using a concentration-addition model. Estimated CEBIs vary greatly depending on assumptions used for exposure and bioactivity. In general, CEBI values were <1 when using median of the estimated general population chemical intake rates, while CEBI were ≥1 when using the upper 95th confidence bound for those same intake rates for all chemicals. Concentration-addition model predictions of mixture bioactivity yield comparable results. Based on current in vitro bioactivity data, HTTK methods, and exposure models, combined exposure scenarios sufficient to influence estrogen bioactivity in the general population cannot be ruled out. Future improvements in screening methods and computational models could reduce uncertainty and better inform the potential combined effects of estrogenic chemicals.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

Integrating tools for non-targeted analysis research and chemical safety evaluations at the US EPA

Tens-of-thousands of chemicals are registered in the U.S. for use in countless processes and products. Recent evidence suggests that many of these chemicals are measureable in environmental and/or biological systems, indicating the potential for widespread exposures. Traditional public health research tools, including in vivo studies and targeted analytical chemistry methods, have been unable to meet the needs of screening programs designed to evaluate chemical safety. As such, new tools have been developed to enable rapid assessment of potentially harmful chemical exposures and their attendant biological responses. One group of tools, known as "non-targeted analysis" (NTA) methods, allows the rapid characterization of thousands of never-before-studied compounds in a wide variety of environmental, residential, and biological media. This article discusses current applications of NTA methods, challenges to their effective use in chemical screening studies, and ways in which shared resources (e.g., chemical standards, databases, model predictions, and media measurements) can advance their use in risk-based chemical prioritization. A brief review is provided of resources and projects within EPA's Office of Research and Development (ORD) that provide benefit to, and receive benefits from, NTA research endeavors. A summary of EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) is also given, which makes direct use of ORD resources to benefit the global NTA research community. Finally, a research framework is described that shows how NTA methods will bridge chemical prioritization efforts within ORD. This framework exists as a guide for institutions seeking to understand the complexity of chemical exposures, and the impact of these exposures on living systems.

Variability of pyrethroid concentrations on hard surface kitchen flooring in occupied housing

Pyrethroids are a class of neurotoxic insecticides, and some studies have used single-time wiping of hard surface flooring to estimate indoor pyrethroid concentrations. Considering that human activities may affect concentrations, knowledge of temporal variability is needed to reduce the uncertainty of exposure estimates that are calculated using wipe sampling of pyrethroids in occupied housing. During weeks one, two, and six of a 6-week study, two wipe samples of hard surface kitchen flooring were collected in each of 50 occupied residences and used to estimate the temporal variability of eight pyrethroids and six pyrethroid degradation products. Beginning 1 month prior to sample collection, the participants kept pesticide use diaries. All pyrethroids were widely distributed among the houses, and co-occurrence of multiple pyrethroids was common structured. Application diaries and detection frequencies appeared unconnected, but the applications were correlated with measurable changes in pyrethroid concentrations. In general, degradation products were detected less frequently and at lower concentrations than their parent pyrethroids. Estimates of the intraclass correlation coefficient (ICC) for individual pyrethroids ranged from 0.55 (bifenthrin) to 0.80 (deltamethrin), and two sampling events at each residence would have been sufficient to estimate the mean concentration of most pyrethroids with an ICC of 0.80.

A Method for Identifying Prevalent Chemical Combinations in the U.S. Population

BACKGROUND

Through the food and water they ingest, the air they breathe, and the consumer products with which they interact at home and at work, humans are exposed to tens of thousands of chemicals, many of which have not been evaluated to determine their potential toxicities. Furthermore, while current chemical testing tends to focus on individual chemicals, the exposures that people actually experience involve mixtures of chemicals. Unfortunately, the number of mixtures that can be formed from the thousands of environmental chemicals is enormous, and testing all of them would be impossible.

OBJECTIVES

We seek to develop and demonstrate a method for identifying those mixtures that are most prevalent in humans.

METHODS

We applied frequent itemset mining, a technique traditionally used for market basket analysis, to biomonitoring data from the 2009-2010 cycle of the continuous National Health and Nutrition Examination Survey (NHANES) to identify combinations of chemicals that frequently co-occur in people.

RESULTS

We identified 90 chemical combinations consisting of relatively few chemicals that occur in at least 30% of the U.S. population, as well as three supercombinations consisting of relatively many chemicals that occur in a small but nonnegligible proportion of the U.S. population.

CONCLUSIONS

We demonstrated how FIM can be used in conjunction with biomonitoring data to narrow a large number of possible chemical combinations down to a smaller set of prevalent chemical combinations. https://doi.org/10.1289/EHP1265.

High-Throughput Analysis of Ovarian Cycle Disruption by Mixtures of Aromatase Inhibitors

BACKGROUND

Combining computational toxicology with ExpoCast exposure estimates and ToxCast™ assay data gives us access to predictions of human health risks stemming from exposures to chemical mixtures.

OBJECTIVES

We explored, through mathematical modeling and simulations, the size of potential effects of random mixtures of aromatase inhibitors on the dynamics of women's menstrual cycles.

METHODS

We simulated random exposures to millions of potential mixtures of 86 aromatase inhibitors. A pharmacokinetic model of intake and disposition of the chemicals predicted their internal concentration as a function of time (up to 2 y). A ToxCast™ aromatase assay provided concentration-inhibition relationships for each chemical. The resulting total aromatase inhibition was input to a mathematical model of the hormonal hypothalamus-pituitary-ovarian control of ovulation in women.

RESULTS

Above 10% inhibition of estradiol synthesis by aromatase inhibitors, noticeable (eventually reversible) effects on ovulation were predicted. Exposures to individual chemicals never led to such effects. In our best estimate, ∼10% of the combined exposures simulated had mild to catastrophic impacts on ovulation. A lower bound on that figure, obtained using an optimistic exposure scenario, was 0.3%.

CONCLUSIONS

These results demonstrate the possibility to predict large-scale mixture effects for endocrine disrupters with a predictive toxicology approach that is suitable for high-throughput ranking and risk assessment. The size of the effects predicted is consistent with an increased risk of infertility in women from everyday exposures to our chemical environment. https://doi.org/10.1289/EHP742.

Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives

The development of treatments involving combinations of drugs is a promising approach towards combating complex or multifactorial disorders. However, the large number of compound combinations that can be generated, even from small compound collections, means that exhaustive experimental testing is infeasible. The ability to predict the behaviour of compound combinations in biological systems, whittling down the number of combinations to be tested, is therefore crucial. Here, we review the current state-of-the-art in the field of compound combination modelling, with the aim to support the development of approaches that, as we hope, will finally lead to an integration of chemical with systems-level biological information for predicting the effect of chemical mixtures.

Synergy Maps: exploring compound combinations using network-based visualization

Background

The phenomenon of super-additivity of biological response to compounds applied jointly, termed synergy, has the potential to provide many therapeutic benefits. Therefore, high throughput screening of compound combinations has recently received a great deal of attention. Large compound libraries and the feasibility of all-pairs screening can easily generate large, information-rich datasets. Previously, these datasets have been visualized using either a heat-map or a network approach—however these visualizations only partially represent the information encoded in the dataset.

Results

A new visualization technique for pairwise combination screening data, termed “Synergy Maps”, is presented. In a Synergy Map, information about the synergistic interactions of compounds is integrated with information about their properties (chemical structure, physicochemical properties, bioactivity profiles) to produce a single visualization. As a result the relationships between compound and combination properties may be investigated simultaneously, and thus may afford insight into the synergy observed in the screen. An interactive web app implementation, available at http://richlewis42.github.io/synergy-maps, has been developed for public use, which may find use in navigating and filtering larger scale combination datasets. This tool is applied to a recent all-pairs dataset of anti-malarials, tested against Plasmodium falciparum, and a preliminary analysis is given as an example, illustrating the disproportionate synergism of histone deacetylase inhibitors previously described in literature, as well as suggesting new hypotheses for future investigation.

Conclusions

Synergy Maps improve the state of the art in compound combination visualization, by simultaneously representing individual compound properties and their interactions. The web-based tool allows straightforward exploration of combination data, and easier identification of correlations between compound properties and interactions.

Assessment of chemical coexposure patterns based upon phthalate biomonitoring data within the 2007/2008 National Health and Nutrition Examination Survey

As regulatory initiatives increasingly call for an understanding of the cumulative risks from chemical mixtures, evaluating exposure data from large biomonitoring programs, which may inform these cumulative risk assessments, will improve the understanding of occurrence and patterns of coexposures. Here we have analyzed the urinary metabolite data for six phthalates (di-butyl phthalate; di-isobutyl phthalate; butyl-benzyl phthalate; bis(2-ethylhexyl) phthalate; di-isononyl phthalate; and di-isodecyl phthalate) in the 2007/2008 National Health and Nutrition Examination Survey (NHANES) data set. For the total data set (N=2604), the co-occurrence of multiple phthalates at the upper percentile of exposure was infrequent. There were no individuals in the NHANES sample who were exposed to >95th percentiles for all six phthalates. For 75% of individuals, none of the six phthalates were above the 95th percentile of their respective exposure distributions. These data suggest that high exposure to multiple phthalates is infrequent in the NHANES population. This analysis solely focused on the pattern of contribution of individual phthalates to total exposure. It did not address the pattern of contribution to potential risk. The approach presented could potentially be used to provide insight into understanding the coexposure patterns for other chemicals.

Regional risk assessment for point source pollution based on a water quality model of the Taipu River, China

Point source pollution is one of the main threats to regional environmental health. Based on a water quality model, a methodology to assess the regional risk of point source pollution is proposed. The assessment procedure includes five parts: (1) identifying risk source units and estimating source emissions using Monte Carlo algorithms; (2) observing hydrological and water quality data of the assessed area, and evaluating the selected water quality model; (3) screening out the assessment endpoints and analyzing receptor vulnerability with the Choquet fuzzy integral algorithm; (4) using the water quality model introduced in the second step to predict pollutant concentrations for various source emission scenarios and analyzing hazards of risk sources; and finally, (5) using the source hazard values and receptor vulnerability scores to estimate overall regional risk. The proposed method, based on the Water Quality Analysis Simulation Program (WASP), was applied in the region of the Taipu River, which is in the Taihu Basin, China. Results of source hazard and receptor vulnerability analysis allowed us to describe aquatic ecological, human health, and socioeconomic risks individually, and also integrated risks in the Taipu region, from a series of risk curves. Risk contributions of sources to receptors were ranked, and the spatial distribution of risk levels was presented. By changing the input conditions, we were able to estimate risks for a range of scenarios. Thus, the proposed procedure may also be used by decisionmakers for long-term dynamic risk prediction.

An empirical approach to sufficient similarity: combining exposure data and mixtures toxicology data

When assessing risks posed by environmental chemical mixtures, whole mixture approaches are preferred to component approaches. When toxicological data on whole mixtures as they occur in the environment are not available, Environmental Protection Agency guidance states that toxicity data from a mixture considered "sufficiently similar" to the environmental mixture can serve as a surrogate. We propose a novel method to examine whether mixtures are sufficiently similar, when exposure data and mixture toxicity study data from at least one representative mixture are available. We define sufficient similarity using equivalence testing methodology comparing the distance between benchmark dose estimates for mixtures in both data-rich and data-poor cases. We construct a "similar mixtures risk indicator"(SMRI) (analogous to the hazard index) on sufficiently similar mixtures linking exposure data with mixtures toxicology data. The methods are illustrated using pyrethroid mixtures occurrence data collected in child care centers (CCC) and dose-response data examining acute neurobehavioral effects of pyrethroid mixtures in rats. Our method shows that the mixtures from 90% of the CCCs were sufficiently similar to the dose-response study mixture. Using exposure estimates for a hypothetical child, the 95th percentile of the (weighted) SMRI for these sufficiently similar mixtures was 0.20 (i.e., where SMRI <1, less concern; >1, more concern).

Critical consideration of the multiplicity of experimental and organismic determinants of pyrethroid neurotoxicity: a proof of concept

Pyrethroids (PYR) are pesticides with high insecticidal activity that may disrupt neuronal excitability in target and nontarget species. The accumulated evidence consistently showed that this neurophysiologic action is followed by alterations in motor, sensorimotor, neuromuscular, and thermoregulatory responses. Nevertheless, there are some equivocal results regarding the potency of PYR in lab animals. The estimation of potency is an important step in pesticide chemical risk assessment. In order to identify the variables influencing neurobehavioral findings across PYR studies, evidence on experimental and organismic determinants of acute PYR-induced neurotoxicity was reviewed in rodents. A comprehensive analysis of these studies was conducted focusing on test material and dosing conditions, testing conditions, animal models, and other determinants such as testing room temperature. Variations in the severity of the neurotoxicity, under lab-controlled conditions, was explained based upon factors including influence of animal species and age, test material features such as chemical structure and stereochemistry, and dosing conditions such as vehicle, route of exposure, and dose volume. If not controlled, the interplay of these factors may lead to large variance in potency estimation. This review examined the scope of acute toxicological data required to determine the safety of pesticide products, and factors and covariates that need to be controlled in order to ensure that predictivity and precaution are balanced in a risk assessment process within a reasonable time-frame, using acute PYR-induced neurotoxicity in rodents as an exemplar.

A pharmacokinetic model of cis- and trans-permethrin disposition in rats and humans with aggregate exposure application

Permethrin is a broad-spectrum pyrethroid insecticide and among the most widely used insecticides in homes and crops. Managing the risks for pesticides such as permethrin depends on the ability to consider diverse exposure scenarios and their relative risks. Physiologically based pharmacokinetic models of delta methrin disposition were modified to describe permethrin kinetics in the rat and human. Unlike formulated deltamethrin which consists of a single stereoisomer, permethrin is formulated as a blend of cis- and trans-diastereomers. We assessed time courses for cis-permethrin and trans-permethrin in several tissues (brain, blood, liver, and fat) in the rat following oral administration of 1 and 10mg/kg permethrin (cis/trans: 40/60). Accurate simulation of permethrin in the rat suggests that a generic model structure is promising for modeling pyrethroids. Human in vitro data and appropriate anatomical information were used to develop a provisional model of permethrin disposition with structures for managing oral, dermal, and inhalation routes of exposure. The human permethrin model was used to evaluate dietary and residential exposures in the U.S. population as estimated by EPA's Stochastic Human Exposure and Dose Simulation model. Simulated cis- and trans-DCCA, metabolites of permethrin, were consistent with measured values in the National Health and Nutrition Examination Survey, indicating that the model holds promise for assessing population exposures and quantifying dose metrics.