The database makes the poison: How the selection of datasets in QSAR models impacts toxicant prediction of higher tier endpoints.

As the United States and the European Union continue their steady march towards the acceptance of new approach methodologies (NAMs), we need to ensure that the available tools are fit for purpose. Critics will be well-positioned to caution against NAMs acceptance and adoption if the tools turn out to be inadequate. In this paper, we focus on Quantitative Structure Activity-Relationship (QSAR) models and highlight how the training database affects quality and performance of these models. Our analysis goes to the point of asking, "are the endpoints extracted from the experimental studies in the database trustworthy, or are they false negatives/positives themselves?" We also discuss the impacts of chemistry on QSAR models, including issues with 2-D structure analyses when dealing with isomers, metabolism, and toxicokinetics. We close our analysis with a discussion of challenges associated with translational toxicology, specifically the lack of adverse outcome pathways/adverse outcome pathway networks (AOPs/AOPNs) for many higher tier endpoints. We recognize that it takes a collaborate effort to build better and higher quality QSAR models especially for higher tier toxicological endpoints. Hence, it is critical to bring toxicologists, statisticians, and machine learning specialists together to discuss and solve these challenges to get relevant predictions.

The physiological and biochemical basis of potency thresholds modeled using human estrogen receptor alpha: implications for identifying endocrine disruptors.

The endocrine system functions by interactions between ligands and receptors. Ligands exhibit potency for binding to and interacting with receptors. Potency is the product of affinity and efficacy. Potency and physiological concentration determine the ability of a ligand to produce physiological effects. The kinetic behavior of ligand-receptor interactions conforms to the laws of mass action. The laws of mass action define the relationship between the affinity of a ligand and the fraction of cognate receptors that it occupies at any physiological concentration. We previously identified the minimum ligand potency required to produce clinically observable estrogenic agonist effects via the human estrogen receptor-alpha (ERα). By examining data on botanical estrogens and dietary supplements, we demonstrated that ERα ligands with potency lower than one one-thousandth that of the primary endogenous hormone 17ß-estradiol (E2) do not produce clinically observable estrogenic effects. This allowed us to propose a Human-Relevant Potency Threshold (HRPT) for ERα ligands of 1 × 10-4 relative to E2. Here, we test the hypothesis that the HRPT for ERα arises from the receptor occupancy by the normal metabolic milieu of endogenous ERα ligands. The metabolic milieu comprises precursors to hormones, metabolites of hormones, and other normal products of metabolism. We have calculated fractional receptor occupancies for ERα ligands with potencies below and above the previously established HRPT when normal circulating levels of some endogenous ERα ligands and E2 were also present. Fractional receptor occupancy calculations showed that individual ERα ligands with potencies more than tenfold higher than the HRPT can compete for occupancy at ERα against individual components of the endogenous metabolic milieu and against mixtures of those components at concentrations found naturally in human blood. Ligands with potencies less than tenfold higher than the HRPT were unable to compete successfully for ERα. These results show that the HRPT for ERα agonism (10-4 relative to E2) proposed previously is quite conservative and should be considered strong evidence against the potential for disruption of the estrogenic pathway. For chemicals with potency 10-3 of E2, the potential for estrogenic endocrine disruption must be considered equivocal and subject to the presence of corroborative evidence. Most importantly, this work demonstrates that the endogenous metabolic milieu is responsible for the observed ERα agonist HRPT, that this HRPT applies also to ERα antagonists, and it provides a compelling mechanistic explanation for the HRPT that is grounded in basic principles of molecular kinetics using well characterized properties and concentrations of endogenous components of normal metabolism.

Kinetically-derived maximal dose (KMD) indicates lack of human carcinogenicity of ethylbenzene.

The kinetically-derived maximal dose (KMD) is defined as the maximal external dose at which kinetics are unchanged relative to lower doses, e.g., doses at which kinetic processes are not saturated. Toxicity produced at doses above the KMD can be qualitatively different from toxicity produced at lower doses. Here, we test the hypothesis that neoplastic lesions reported in the National Toxicology Program's (NTP) rodent cancer bioassay with ethylbenzene are a high-dose phenomenon secondary to saturation of elimination kinetics. To test this, we applied Bayesian modeling on kinetic data for ethylbenzene from rats and humans to estimate the Vmax and Km for the Michaelis-Menten equation that governs the elimination kinetics. Analysis of the Michaelis-Menten elimination curve generated from those Vmax and Km values indicated KMD ranges for venous ethylbenzene of 8-17 mg/L in rats and 10-18 mg/L in humans. Those venous concentrations are produced by inhalation concentrations of around 200 ppm ethylbenzene, which is well above typical human exposures. These KMD estimates support the hypothesis that neoplastic lesions seen in the NTP rodent bioassay occur secondary to saturation of ethylbenzene elimination pathways and are not relevant for human risk assessment. Thus, ethylbenzene does not pose a credible cancer risk to humans under foreseeable exposure conditions. Cancer risk assessments focused on protecting human health should avoid endpoint data from rodents exposed to ethylbenzene above the KMD range and future toxicological testing should focus on doses below the KMD range.

Understanding and overcoming the technical challenges in using in silico predictions in regulatory decisions of complex toxicological endpoints - A pesticide perspective for regulatory toxicologists with a focus on machine learning models.

There are many challenges that must be overcome before in silico toxicity predictions are ripe for regulatory decision-making. Today, mandates in the United States of America and the European Union to avoid animal usage in toxicity testing is driving the need to consider alternative technologies, including Quantitative Structure Activity Relationship (QSAR) models, and read across approaches. However, when adopting new methods, it is critical that both new approach developers as well as regulatory users understand the strengths and challenges with these new approaches. In this paper, we identify potential sources of bias in machine learning methods specific to toxicity predictions, that may impact the overall performance of in silico models. We also discuss ways to mitigate these biases. Based on our experiences, the most prevalent sources of bias include class imbalance (differing numbers of "toxic" vs "nontoxic" compounds), limited numbers of chemicals within a particular chemistry, and biases within the studies that make up the database used for model building, as well as model evaluation biases. While this is already complex for repeated dose toxicity, in reproduction and developmental toxicity a further level of complexity is introduced by the need to evaluate effects on individual animal and litter basis (e.g., a hierarchal structure). We also discuss key considerations developers and regulators need to make when they use machine learning models to predict chemical safety. Our objective is for our paper to serve as a desk reference for model developers and regulators as they evaluate machine learning models and as they make decisions using these models.

Range of the perfluorooctanoate (PFOA) safe dose for human health: An international collaboration.

Many government agencies and expert groups have estimated a dose-rate of perfluorooctanoate (PFOA) that would protect human health. Most of these evaluations are based on the same studies (whether of humans, laboratory animals, or both), and all note various uncertainties in our existing knowledge. Nonetheless, the values of these various, estimated, safe-doses vary widely, with some being more than 100,000 fold different. This sort of discrepancy invites scrutiny and explanation. Otherwise what is the lay public to make of this disparity? The Steering Committee of the Alliance for Risk Assessment (2022) called for scientists interested in attempting to understand and narrow these disparities. An advisory committee of nine scientists from four countries was selected from nominations received, and a subsequent invitation to scientists internationally led to the formation of three technical teams (for a total of 24 scientists from 8 countries). The teams reviewed relevant information and independently developed ranges for estimated PFOA safe doses. All three teams determined that the available epidemiologic information could not form a reliable basis for a PFOA safe dose-assessment in the absence of mechanistic data that are relevant for humans at serum concentrations seen in the general population. Based instead on dose-response data from five studies of PFOA-exposed laboratory animals, we estimated that PFOA dose-rates 10-70 ng/kg-day are protective of human health.

A novel approach to calculating the kinetically derived maximum dose.

The kinetically derived maximal dose (KMD) provides a toxicologically relevant upper range for the determination of chemical safety. Here, we describe a new way of calculating the KMD that is based on sound Bayesian, theoretical, biochemical, and toxicokinetic principles, that avoids the problems of relying upon the area under the curve (AUC) approach that has often been used. Our new, mathematically rigorous approach is based on converting toxicokinetic data to the overall, or system-wide, Michaelis-Menten curve (which is the slope function for the toxicokinetic data) using Bayesian methods and using the "kneedle" algorithm to find the "knee" or "elbow"-the point at which there is diminishing returns in the velocity of the Michaelis-Menten curve (or acceleration of the toxicokinetic curve). Our work fundamentally reshapes the KMD methodology, placing it within the well-established Michaelis-Menten theoretical framework by defining the KMD as the point where the kinetic rate approximates the Michaelis-Menten asymptote at higher concentrations. By putting the KMD within the Michaelis-Menten framework, we leverage existing biochemical and pharmacological concepts such as "saturation" to establish the region where the KMD is likely to exist. The advantage of defining KMD as a region, rather than as an inflection point along the curve, is that a region reflects uncertainty and clarifies that there is no single point where the curve is expected to "break;" rather, there is a region where the curve begins to taper off as it approaches the asymptote (Vmax in the Michaelis-Menten equation).

Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology.

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

Community Approaches for Integrating Environmental Exposures into Human Models of Disease.

BACKGROUND: A critical challenge in genomic medicine is identifying the genetic and environmental risk factors for disease. Currently, the available data links a majority of known coding human genes to phenotypes, but the environmental component of human disease is extremely underrepresented in these linked data sets. Without environmental exposure information, our ability to realize precision health is limited, even with the promise of modern genomics. Achieving integration of gene, phenotype, and environment will require extensive translation of data into a standard, computable form and the extension of the existing gene/phenotype data model. The data standards and models needed to achieve this integration do not currently exist. OBJECTIVES: Our objective is to foster development of community-driven data-reporting standards and a computational model that will facilitate the inclusion of exposure data in computational analysis of human disease. To this end, we present a preliminary semantic data model and use cases and competency questions for further community-driven model development and refinement. DISCUSSION: There is a real desire by the exposure science, epidemiology, and toxicology communities to use informatics approaches to improve their research workflow, gain new insights, and increase data reuse. Critical to success is the development of a community-driven data model for describing environmental exposures and linking them to existing models of human disease. https://doi.org/10.1289/EHP7215.

Assessment of Transcriptomic and Apical Responses of Daphnia magna Exposed to a Polyethylene Microplastic in a 21-d Chronic Study.

There is global concern regarding the fate and effects of microplastics in the environment, particularly in aquatic systems. In the present study, ethylene acrylic acid copolymer particles were evaluated in a chronic toxicity study with the aquatic invertebrate Daphnia magna. The study design included a natural particle control treatment (silica) to differentiate any potential physical effects of a particle from the intrinsic toxicity of the test material. In addition to the standard endpoints of survival, growth, and reproduction, the transcriptomic profiles of control and ethylene acrylic acid copolymer-exposed D. magna were evaluated at the termination of the 21-d toxicity study. No significant effects on D. magna growth, survival, or reproduction were observed in comparison with both particle and untreated control groups. Significant transcriptomic alterations were induced at the highest treatment level of 2.3 × 1012 particles of the ethylene acrylic acid copolymer/L in key pathways linked to central metabolism and energy reserves, oxidative stress, and ovulation and molting, indicating a global transcriptomic response pattern. To put the results in perspective is challenging at this time, because, to date, microplastic environmental monitoring approaches have not been equipped to detect particles in the nanosize range. However, our results indicate that ethylene acrylic acid copolymer microplastics in the upper nanosize range are not expected to adversely affect D. magna growth, survival, or reproductive outcomes at concentrations of up to 1012 particles/L. Environ Toxicol Chem 2020;39:1578-1589. © 2020 SETAC.

Organizing mechanism-related information on chemical interactions using a framework based on the aggregate exposure and adverse outcome pathways.

This paper presents a framework for organizing and accessing mechanistic data on chemical interactions. The framework is designed to support the assessment of risks from combined chemical exposures. The framework covers interactions between chemicals that occur over the entire source-to-outcome continuum including interactions that are studied in the fields of chemical transport, environmental fate, exposure assessment, dosimetry, and individual and population-based adverse outcomes. The framework proposes to organize data using a semantic triple of a chemical (subject), has impact (predicate), and a causal event on the source-to-outcome continuum of a second chemical (object). The location of the causal event on the source-to-outcome continuum and the nature of the impact are used as the basis for a taxonomy of interactions. The approach also builds on concepts from the Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP). The framework proposes the linking of AEPs of multiple chemicals and the AOP networks relevant to those chemicals to form AEP-AOP networks that describe chemical interactions that cannot be characterized using AOP networks alone. Such AEP-AOP networks will aid the construction of workflows for both experimental design and the systematic review or evaluation performed in risk assessments. Finally, the framework is used to link the constructs of existing component-based approaches for mixture toxicology to specific categories in the interaction taxonomy.

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs).

Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert-driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.

Different as night and day: Behavioural and life history responses to varied photoperiods in Daphnia magna.

Nearly all animal species have utilized photoperiod to cue seasonal behaviours and life history traits. We investigated photoperiod responses in keystone species, Daphnia magna, to identify molecular processes underlying ecologically important behaviours and traits using functional transcriptomic analyses. Daphnia magna were photoperiod-entrained immediately posthatch to a standard control photoperiod of 16 light/ 8 dark hours (16L:8D) relative to shorter (4L:20D, 8L:16D, 12L:12L) and longer (20L:4D) day length photoperiods. Short-day photoperiods induced significantly increased light-avoidance behaviours relative to controls. Correspondingly, significant differential transcript expression for genes involved in glutamate signalling was observed, a critical signalling pathway in arthropod light-avoidance behaviour. Additionally, period circadian protein and proteins coding F-box/LRR-repeat domains were differentially expressed which are recognized to establish circadian rhythms in arthropods. Indicators of metabolic rate increased in short-day photoperiods which corresponded with broadscale changes in transcriptional expression across system-level energy metabolism pathways. The most striking observations included significantly decreased neonate production at the shortest day length photoperiod (4L:20D) and significantly increased male production across short-day and equinox photoperiods (4L:20D, 8L:16D and 12L:12D). Transcriptional expression consistent with putative mechanisms of male production was observed including photoperiod-dependent expression of transformer-2 sex-determining protein and small nuclear ribonucleoprotein particles (snRNPs) which control splice variant expression for genes like transformer. Finally, increased transcriptional expression of glutamate has also been shown to induce male production in Daphnia pulex via photoperiod-sensitive mechanisms. Overall, photoperiod entrainment affected molecular pathways that underpin critical behavioural and life history traits in D. magna providing fundamental insights into biological responses to this primary environmental cue.

Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach.

Exposure to certain chemicals such as disinfectants through inhalation is suspected to be involved in the development of pulmonary fibrosis, a lung disease in which lung tissue becomes damaged and scarred. Pulmonary fibrosis is known to be regulated by transforming growth factor ß (TGF-ß) and peroxisome proliferator-activated receptor gamma (PPARγ). Here, we developed an adverse outcome pathway (AOP) to better define the linkage of PPARγ antagonism to the adverse outcome of pulmonary fibrosis. We then conducted a systematic analysis to identify potential chemicals involved in this AOP, using the ToxCast database and deep learning artificial neural network models. We identified chemicals bearing a potential inhalation hazard and exposure hazards from the database that could be related to this AOP. For chemicals that were not present in the ToxCast database, multilayer perceptron models were developed based on the ToxCast assays related to the AOP. The reactivity of ToxCast untested chemicals was then predicted using these deep learning models. Both approaches identified a set of chemicals that could be used to validate the AOP. This study suggests that chemicals categorized using an existing database such as ToxCast can be used to validate an AOP and that deep learning approaches can be used to characterize a range of potential active chemicals for an AOP of interest.

Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment.

An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway-based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850-1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Chemical hazard prediction and hypothesis testing using quantitative adverse outcome pathways.

Current efforts in chemical safety are focused on utilizing human in vitro or alternative animal data in biological pathway context. However, it remains unclear how biological pathways, and toxicology data developed in that context, can be used to quantitatively facilitate decision-making.  The objective of this work is to determine if hypothesis testing using Adverse Outcome Pathways (AOPs) can provide quantitative chemical hazard predictions.  Current methods for predicting hazards of chemicals in a biological pathway context were extensively reviewed, specific case studies examined and computational modeling used to demonstrate quantitative hazard prediction based on an AOP. Since AOPs are chemically agnostic, we propose that AOPs function as hypotheses for how specific chemicals may cause adverse effects via specific pathways. Three broad approaches were identified for testing the hypothesis with AOPs, semi-quantitative weight of evidence, probabilistic, and mechanistic modeling. We then demonstrate how these approaches could be used to test hypotheses using high throughput in vitro data and alternative animal data. Finally, we discuss standards in development and documentation that would facilitate use in a regulatory context. We conclude that quantitative AOPs provide a flexible hypothesis framework for predicting chemical hazards. It accommodates a wide range of approaches that are useful at many stages and build upon one another to become increasingly quantitative.

Building a developmental toxicity ontology.

BACKGROUND: As more information is generated about modes of action for developmental toxicity and more data are generated using high-throughput and high-content technologies, it is becoming necessary to organize that information. This report discussed the need for a systematic representation of knowledge about developmental toxicity (i.e., an ontology) and proposes a method to build one based on knowledge of developmental biology and mode of action/ adverse outcome pathways in developmental toxicity. METHODS: This report is the result of a consensus working group developing a plan to create an ontology for developmental toxicity that spans multiple levels of biological organization. RESULTS: This report provide a description of some of the challenges in building a developmental toxicity ontology and outlines a proposed methodology to meet those challenges. As the ontology is built on currently available web-based resources, a review of these resources is provided. Case studies on one of the most well-understood morphogens and developmental toxicants, retinoic acid, are presented as examples of how such an ontology might be developed. DISCUSSION: This report outlines an approach to construct a developmental toxicity ontology. Such an ontology will facilitate computer-based prediction of substances likely to induce human developmental toxicity.

Introducing WikiPathways as a Data-Source to Support Adverse Outcome Pathways for Regulatory Risk Assessment of Chemicals and Nanomaterials.

A paradigm shift is taking place in risk assessment to replace animal models, reduce the number of economic resources, and refine the methodologies to test the growing number of chemicals and nanomaterials. Therefore, approaches such as transcriptomics, proteomics, and metabolomics have become valuable tools in toxicological research, and are finding their way into regulatory toxicity. One promising framework to bridge the gap between the molecular-level measurements and risk assessment is the concept of adverse outcome pathways (AOPs). These pathways comprise mechanistic knowledge and connect biological events from a molecular level toward an adverse effect outcome after exposure to a chemical. However, the implementation of omics-based approaches in the AOPs and their acceptance by the risk assessment community is still a challenge. Because the existing modules in the main repository for AOPs, the AOP Knowledge Base (AOP-KB), do not currently allow the integration of omics technologies, additional tools are required for omics-based data analysis and visualization. Here we show how WikiPathways can serve as a supportive tool to make omics data interoperable with the AOP-Wiki, part of the AOP-KB. Manual matching of key events (KEs) indicated that 67% could be linked with molecular pathways. Automatic connection through linkage of identifiers between the databases showed that only 30% of AOP-Wiki chemicals were found on WikiPathways. More loose linkage through gene names in KE and Key Event Relationships descriptions gave an overlap of 70 and 71%, respectively. This shows many opportunities to create more direct connections, for example with extended ontology annotations, improving its interoperability. This interoperability allows the needed integration of omics data linked to the molecular pathways with AOPs. A new AOP Portal on WikiPathways is presented to allow the community of AOP developers to collaborate and populate the molecular pathways that underlie the KEs of AOP-Wiki. We conclude that the integration of WikiPathways and AOP-Wiki will improve risk assessment because omics data will be linked directly to KEs and therefore allow the comprehensive understanding and description of AOPs. To make this assessment reproducible and valid, major changes are needed in both WikiPathways and AOP-Wiki.

A decision analysis framework for estimating the potential hazards for drinking water resources of chemicals used in hydraulic fracturing fluids.

Despite growing concerns over the potential for hydraulic fracturing to impact drinking water resources, there are limited data available to identify chemicals used in hydraulic fracturing fluids that may pose public health concerns. In an effort to explore these potential hazards, a multi-criteria decision analysis (MCDA) framework was employed to analyze and rank selected subsets of these chemicals by integrating data on toxicity, frequency of use, and physicochemical properties that describe transport in water. Data used in this analysis were obtained from publicly available databases compiled by the United States Environmental Protection Agency (EPA) as part of a larger study on the potential impacts of hydraulic fracturing on drinking water. Starting with nationwide hydraulic fracturing chemical usage data from EPA's analysis of the FracFocus Chemical Disclosure Registry 1.0, MCDAs were performed on chemicals that had either noncancer toxicity values (n=37) or cancer-specific toxicity values (n=10). The noncancer MCDA was then repeated for subsets of chemicals reported in three representative states (Texas, n=31; Pennsylvania, n=18; and North Dakota, n=20). Within each MCDA, chemicals received scores based on relative toxicity, relative frequency of use, and physicochemical properties (mobility in water, volatility, persistence). Results show a relative ranking of these chemicals based on hazard potential, and provide preliminary insight into chemicals that may be more likely than others to impact drinking water resources. Comparison of nationwide versus state-specific analyses indicates regional differences in the chemicals that may be of more concern to drinking water resources, although many chemicals were commonly used and received similar overall hazard rankings. Several chemicals highlighted by these MCDAs have been reported in groundwater near areas of hydraulic fracturing activity. This approach is intended as a preliminary analysis, and represents one possible method for integrating data to explore potential public health impacts.

Using In Vitro High-Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards.

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high-throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline-based meta-regression can be used to integrate data across multiple assay replicates to generate a concentration-response curve. We used this analysis to calculate an in vitro point of departure of 0.751 µM and risk-specific in vitro concentrations of 0.29 µM and 0.28 µM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta-regression, may allow risk assessors to identify points of departure and risk-specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.