Improving natural product research translation: From source to clinical trial.

While great interest in health effects of natural product (NP) including dietary supplements and foods persists, promising preclinical NP research is not consistently translating into actionable clinical trial (CT) outcomes. Generally considered the gold standard for assessing safety and efficacy, CTs, especially phase III CTs, are costly and require rigorous planning to optimize the value of the information obtained. More effective bridging from NP research to CT was the goal of a September, 2018 transdisciplinary workshop. Participants emphasized that replicability and likelihood of successful translation depend on rigor in experimental design, interpretation, and reporting across the continuum of NP research. Discussions spanned good practices for NP characterization and quality control; use and interpretation of models (computational through in vivo) with strong clinical predictive validity; controls for experimental artefacts, especially for in vitro interrogation of bioactivity and mechanisms of action; rigorous assessment and interpretation of prior research; transparency in all reporting; and prioritization of research questions. Natural product clinical trials prioritized based on rigorous, convergent supporting data and current public health needs are most likely to be informative and ultimately affect public health. Thoughtful, coordinated implementation of these practices should enhance the knowledge gained from future NP research.

Leveraging human genetic and adverse outcome pathway (AOP) data to inform susceptibility in human health risk assessment.

Estimation of susceptibility differences in human health risk assessment (HHRA) has been challenged by a lack of available susceptibility and variability data after exposure to a specific environmental chemical or pharmaceutical. With the increasingly large number of available data sources that contain polymorphism and other genetic data, human genetic variability that informs susceptibility can be better incorporated into HHRA. A recent policy, the 2016 The Frank R. Lautenberg Chemical Safety for the twenty-first Century Act, requires the US Environmental Protection Agency to evaluate new and existing toxic chemicals with explicit consideration of susceptible populations of all types (life stage, exposure, genetic, etc.). We propose using the adverse outcome pathway (AOP) construct to organize, identify, and characterize human genetic susceptibility in HHRA. We explore how publicly available human genetic datasets can be used to gain mechanistic understanding of molecular events and characterize human susceptibility for an adverse outcome. We present a computational method that implements publicly available human genetic data to prioritize AOPs with potential for human genetic variability. We describe the application of this approach across multiple described AOPs for health outcomes of interest, and by focusing on a single molecular initiating event. This contributes to a long-term goal to improve estimates of human susceptibility for use in HHRA for single and multiple chemicals.

An Intuitive Approach for Predicting Potential Human Health Risk with the Tox21 10k Library.

In vitro-in vivo extrapolation (IVIVE) analyses translating high-throughput screening (HTS) data to human relevance have been limited. This study represents the first report applying IVIVE approaches and exposure comparisons using the entirety of the Tox21 federal collaboration chemical screening data, incorporating assay response efficacy and quality of concentration-response fits, and providing quantitative anchoring to first address the likelihood of human in vivo interactions with Tox21 compounds. This likelihood was assessed using a maximum blood concentration to in vitro response ratio approach (Cmax/AC50), analogous to decision-making methods for clinical drug-drug interactions. Fraction unbound in plasma (fup) and intrinsic hepatic clearance (CLint) parameters were estimated in silico and incorporated in a three-compartment toxicokinetic (TK) model to first predict Cmax for in vivo corroboration using therapeutic scenarios. Toward lower exposure scenarios, 36 compounds of 3925 unique chemicals with curated activity in the HTS data using high-quality dose-response model fits and ≥40% efficacy gave "possible" human in vivo interaction likelihoods lower than median human exposures predicted in the United States Environmental Protection Agency's ExpoCast program. A publicly available web application has been designed to provide all Tox21-ToxCast dose-likelihood predictions. Overall, this approach provides an intuitive framework to relate in vitro toxicology data rapidly and quantitatively to exposures using either in vitro or in silico derived TK parameters and can be thought of as an important step toward estimating plausible biological interactions in a high-throughput risk-assessment framework.

httk: R Package for High-Throughput Toxicokinetics.

Thousands of chemicals have been profiled by high-throughput screening programs such as ToxCast and Tox21; these chemicals are tested in part because most of them have limited or no data on hazard, exposure, or toxicokinetics. Toxicokinetic models aid in predicting tissue concentrations resulting from chemical exposure, and a "reverse dosimetry" approach can be used to predict exposure doses sufficient to cause tissue concentrations that have been identified as bioactive by high-throughput screening. We have created four toxicokinetic models within the R software package httk. These models are designed to be parameterized using high-throughput in vitro data (plasma protein binding and hepatic clearance), as well as structure-derived physicochemical properties and species-specific physiological data. The package contains tools for Monte Carlo sampling and reverse dosimetry along with functions for the analysis of concentration vs. time simulations. The package can currently use human in vitro data to make predictions for 553 chemicals in humans, rats, mice, dogs, and rabbits, including 94 pharmaceuticals and 415 ToxCast chemicals. For 67 of these chemicals, the package includes rat-specific in vitro data. This package is structured to be augmented with additional chemical data as they become available. Package httk enables the inclusion of toxicokinetics in the statistical analysis of chemicals undergoing high-throughput screening.

Small-molecule inhibitors targeting G-protein-coupled Rho guanine nucleotide exchange factors.

The G-protein-mediated Rho guanine nucleotide exchange factor (GEF)-Rho GTPase signaling axis has been implicated in human pathophysiology and is a potential therapeutic target. By virtual screening of chemicals that fit into a surface groove of the DH-PH domain of LARG, a G-protein-regulated Rho GEF involved in RhoA activation, and subsequent validations in biochemical assays, we have identified a class of chemical inhibitors represented by Y16 that are active in specifically inhibiting LARG binding to RhoA. Y16 binds to the junction site of the DH-PH domains of LARG with a ∼80 nM K(d) and suppresses LARG catalyzed RhoA activation dose dependently. It is active in blocking the interaction of LARG and related G-protein-coupled Rho GEFs with RhoA without a detectable effect on other DBL family Rho GEFs, Rho effectors, or a RhoGAP. In cells, Y16 selectively inhibits serum-induced RhoA activity and RhoA-mediated signaling, effects that can be rescued by a constitutively active RhoA or ROCK mutant. By suppressing RhoA activity, Y16 inhibits mammary sphere formation of MCF7 breast cancer cells but does not affect the nontransforming MCF10A cells. Significantly, Y16 works synergistically with Rhosin/G04, a Rho GTPase activation site inhibitor, in inhibiting LARG-RhoA interaction, RhoA activation, and RhoA-mediated signaling functions. Thus, our studies show that Rho GEFs can serve as selective targets of small chemicals and present a strategy of dual inhibition of the enzyme-substrate pair of GEF-RhoA at their binding interface that leads to enhanced efficacy and specificity.

A computational model predicting disruption of blood vessel development.

Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages. Signals regulating de novo blood vessel formation (vasculogenesis) and remodeling (angiogenesis) come from a variety of biological pathways linked to endothelial cell (EC) behavior, extracellular matrix (ECM) remodeling and the local generation of chemokines and growth factors. Simulating these interactions at a systems level requires sufficient biological detail about the relevant molecular pathways and associated cellular behaviors, and tractable computational models that offset mathematical and biological complexity. Here, we describe a novel multicellular agent-based model of vasculogenesis using the CompuCell3D (http://www.compucell3d.org/) modeling environment supplemented with semi-automatic knowledgebase creation. The model incorporates vascular endothelial growth factor signals, pro- and anti-angiogenic inflammatory chemokine signals, and the plasminogen activating system of enzymes and proteases linked to ECM interactions, to simulate nascent EC organization, growth and remodeling. The model was shown to recapitulate stereotypical capillary plexus formation and structural emergence of non-coded cellular behaviors, such as a heterologous bridging phenomenon linking endothelial tip cells together during formation of polygonal endothelial cords. Molecular targets in the computational model were mapped to signatures of vascular disruption derived from in vitro chemical profiling using the EPA's ToxCast high-throughput screening (HTS) dataset. Simulating the HTS data with the cell-agent based model of vascular development predicted adverse effects of a reference anti-angiogenic thalidomide analog, 5HPP-33, on in vitro angiogenesis with respect to both concentration-response and morphological consequences. These findings support the utility of cell agent-based models for simulating a morphogenetic series of events and for the first time demonstrate the applicability of these models for predictive toxicology.

Predictive endocrine testing in the 21st century using in vitro assays of estrogen receptor signaling responses.

Thousands of environmental chemicals are subject to regulatory review for their potential to be endocrine disruptors (ED). In vitro high-throughput screening (HTS) assays have emerged as a potential tool for prioritizing chemicals for ED-related whole-animal tests. In this study, 1814 chemicals including pesticide active and inert ingredients, industrial chemicals, food additives, and pharmaceuticals were evaluated in a panel of 13 in vitro HTS assays. The panel of in vitro assays interrogated multiple end points related to estrogen receptor (ER) signaling, namely binding, agonist, antagonist, and cell growth responses. The results from the in vitro assays were used to create an ER Interaction Score. For 36 reference chemicals, an ER Interaction Score >0 showed 100% sensitivity and 87.5% specificity for classifying potential ER activity. The magnitude of the ER Interaction Score was significantly related to the potency classification of the reference chemicals (p < 0.0001). ERα/ERß selectivity was also evaluated, but relatively few chemicals showed significant selectivity for a specific isoform. When applied to a broader set of chemicals with in vivo uterotrophic data, the ER Interaction Scores showed 91% sensitivity and 65% specificity. Overall, this study provides a novel method for combining in vitro concentration response data from multiple assays and, when applied to a large set of ER data, accurately predicted estrogenic responses and demonstrated its utility for chemical prioritization.

Real-time growth kinetics measuring hormone mimicry for ToxCast chemicals in T-47D human ductal carcinoma cells.

High-throughput screening (HTS) assays capable of profiling thousands of environmentally relevant chemicals for in vitro biological activity provide useful information on the potential for disrupting endocrine pathways. Disruption of the estrogen signaling pathway has been implicated in a variety of adverse health effects including impaired development, reproduction, and carcinogenesis. The estrogen-responsive human mammary ductal carcinoma cell line T-47D was exposed to 1815 ToxCast chemicals comprising pesticides, industrial chemicals, pharmaceuticals, personal care products, cosmetics, food ingredients, and other chemicals with known or suspected human exposure potential. Cell growth kinetics were evaluated using real-time cell electronic sensing. T-47D cells were exposed to eight concentrations (0.006-100 µM), and measurements of cellular impedance were repeatedly recorded for 105 h. Chemical effects were evaluated based on potency (concentration at which response occurs) and efficacy (extent of response). A linear growth response was observed in response to prototypical estrogen receptor agonists (17ß-estradiol, genistein, bisphenol A, nonylphenol, and 4-tert-octylphenol). Several compounds, including bisphenol A and genistein, induced cell growth comparable in efficacy to that of 17ß-estradiol, but with decreased potency. Progestins, androgens, and corticosteroids invoked a biphasic growth response indicative of changes in cell number or cell morphology. Results from this cell growth assay were compared with results from additional estrogen receptor (ER) binding and transactivation assays. Chemicals detected as active in both the cell growth and ER receptor binding assays demonstrated potencies highly correlated with two ER transactivation assays (r = 0.72; r = 0.70). While ER binding assays detected chemicals that were highly potent or efficacious in the T-47D cell growth and transactivation assays, the binding assays lacked sensitivity in detecting weakly active compounds. In conclusion, this cell-based assay rapidly detects chemical effects on T-47D growth and shows potential, in combination with other HTS assays, to detect environmentally relevant chemicals with potential estrogenic activity.

Profiling 976 ToxCast chemicals across 331 enzymatic and receptor signaling assays.

Understanding potential health risks is a significant challenge due to the large numbers of diverse chemicals with poorly characterized exposures and mechanisms of toxicities. The present study analyzes 976 chemicals (including failed pharmaceuticals, alternative plasticizers, food additives, and pesticides) in Phases I and II of the U.S. EPA's ToxCast project across 331 cell-free enzymatic and ligand-binding high-throughput screening (HTS) assays. Half-maximal activity concentrations (AC50) were identified for 729 chemicals in 256 assays (7,135 chemical-assay pairs). Some of the most commonly affected assays were CYPs (CYP2C9 and CYP2C19), transporters (mitochondrial TSPO, norepinephrine, and dopaminergic), and GPCRs (aminergic). Heavy metals, surfactants, and dithiocarbamate fungicides showed promiscuous but distinctly different patterns of activity, whereas many of the pharmaceutical compounds showed promiscuous activity across GPCRs. Literature analysis confirmed >50% of the activities for the most potent chemical-assay pairs (54) but also revealed 10 missed interactions. Twenty-two chemicals with known estrogenic activity were correctly identified for the majority (77%), missing only the weaker interactions. In many cases, novel findings for previously unreported chemical-target combinations clustered with known chemical-target interactions. Results from this large inventory of chemical-biological interactions can inform read-across methods as well as link potential targets to molecular initiating events in adverse outcome pathways for diverse toxicities.

Evaluation of the herbicide glyphosate, (aminomethyl)phosphonic acid, and glyphosate-based formulations for genotoxic activity using in vitro assays.

Glyphosate, the most heavily used herbicide world-wide, is applied to plants in complex formulations that promote absorption. The National Toxicology Program reported in 1992 that glyphosate, administered to rats and mice at doses up to 50,000 ppm in feed for 13 weeks, showed little evidence of toxicity, and no induction of micronuclei was observed in the mice in this study. Subsequently, mechanistic studies of glyphosate and glyphosate-based formulations (GBFs) that have focused on DNA damage and oxidative stress suggest that glyphosate may have genotoxic potential. However, few of these studies directly compared glyphosate to GBFs, or effects among GBFs. To address these data gaps, we tested glyphosate, glyphosate isopropylamine (IPA), and (aminomethyl)phosphonic acid (AMPA, a microbial metabolite of glyphosate), 9 high-use agricultural GBFs, 4 residential-use GBFs, and additional herbicides (metolachlor, mesotrione, and diquat dibromide) present in some of the GBFs in bacterial mutagenicity tests, and in human TK6 cells using a micronucleus assay and a multiplexed DNA damage assay. Our results showed no genotoxicity or notable cytotoxicity for glyphosate or AMPA at concentrations up to 10 mM, while all GBFs and herbicides other than glyphosate were cytotoxic, and some showed genotoxic activity. An in vitro to in vivo extrapolation of results for glyphosate suggests that it is of low toxicological concern for humans. In conclusion, these results demonstrate a lack of genotoxicity for glyphosate, consistent with observations in the NTP in vivo study, and suggest that toxicity associated with GBFs may be related to other components of these formulations.

Cdc42 regulates extracellular matrix remodeling in three dimensions.

Extracellular matrix (ECM) actively participates in normal cell regulation and in the process of tumor progression. The Rho GTPase Cdc42 has been shown to regulate cell-ECM interaction in conventional two-dimensional culture conditions by using dominant mutants of Cdc42 in immortalized cell lines that may introduce nonspecific effects. Here, we employ three-dimensional culture systems for conditional gene targeted primary mouse embryonic fibroblasts that better simulate the reciprocal and adaptive interactions between cells and surrounding matrix to define the role of Cdc42 signaling pathways in ECM organization. Cdc42 deficiency leads to a defect in global cell-matrix interactions reflected by a decrease in collagen gel contraction. The defect is associated with an altered cell-matrix interaction that is evident by morphologic changes and reduced focal adhesion complex formation. The matrix defect is also associated with a reduction in synthesis and activation of matrix metalloproteinase 9 (MMP9) and altered fibronectin deposition patterning. A Cdc42 mutant rescue experiment found that downstream of Cdc42, p21-activated kinase (PAK), but not Par6 or WASP, may be involved in regulating collagen gel contraction and fibronectin organization. Thus, in addition to the previously implicated roles in intracellular regulation of actin organization, proliferation, and vesicle trafficking, Cdc42 is essential in ECM remodeling in three dimensions.

Update on EPA's ToxCast program: providing high throughput decision support tools for chemical risk management.

The field of toxicology is on the cusp of a major transformation in how the safety and hazard of chemicals are evaluated for potential effects on human health and the environment. Brought on by the recognition of the limitations of the current paradigm in terms of cost, time, and throughput, combined with the ever increasing power of modern biological tools to probe mechanisms of chemical-biological interactions at finer and finer resolutions, 21st century toxicology is rapidly taking shape. A key element of the new approach is a focus on the molecular and cellular pathways that are the targets of chemical interactions. By understanding toxicity in this manner, we begin to learn how chemicals cause toxicity, as opposed to merely what diseases or health effects they might cause. This deeper understanding leads to increasing confidence in identifying which populations might be at risk, significant susceptibility factors, and key influences on the shape of the dose-response curve. The U. S. Environmental Protection Agency (EPA) initiated the ToxCast, or "toxicity forecaster", program 5 years ago to gain understanding of the strengths and limitations of the new approach by starting to test relatively large numbers (hundreds) of chemicals against an equally large number of biological assays. Using computational approaches, the EPA is building decision support tools based on ToxCast in vitro screening results to help prioritize chemicals for further investigation, as well as developing predictive models for a number of health outcomes. This perspective provides a summary of the initial, proof of concept, Phase I of ToxCast that has laid the groundwork for the next phases and future directions of the program.

Microphysiological Systems Evaluation: Experience of TEX-VAL Tissue Chip Testing Consortium.

Much has been written and said about the promise and excitement of microphysiological systems, miniature devices that aim to recreate aspects of human physiology on a chip. The rapid explosion of the offerings and persistent publicity placed high expectations on both product manufacturers and regulatory agencies to adopt the data. Inevitably, discussions of where this technology fits in chemical testing paradigms are ongoing. Some end-users became early adopters, whereas others have taken a more cautious approach because of the high cost and uncertainties of their utility. Here, we detail the experience of a public-private collaboration established for testing of diverse microphysiological systems. Collectively, we present a number of considerations on practical aspects of using microphysiological systems in the context of their applications in decision-making. Specifically, future end-users need to be prepared for extensive on-site optimization and have access to a wide range of imaging and other equipment. We reason that cells, related reagents, and the technical skills of the research staff, not the devices themselves, are the most critical determinants of success. Extrapolation from concentration-response effects in microphysiological systems to human blood or oral exposures, difficulties with replicating the whole organ, and long-term functionality remain as critical challenges. Overall, we conclude that it is unlikely that a rodent- or human-equivalent model is achievable through a finite number of microphysiological systems in the near future; therefore, building consensus and promoting the gradual incorporation of these models into tiered approaches for safety assessment and decision-making is the sensible path to wide adoption.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making.

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

Zebrafish: as an integrative model for twenty-first century toxicity testing.

The zebrafish embryo is a useful small model for investigating vertebrate development because of its transparency, low cost, transgenic and morpholino capabilities, conservation of cell signaling, and concordance with mammalian developmental phenotypes. From these advantages, the zebrafish embryo has been considered as an alternative model for traditional in vivo developmental toxicity screening. The use of this organism in conjunction with traditional in vivo developmental toxicity testing has the potential to reduce cost and increase throughput of testing the chemical universe, prioritize chemicals for targeted toxicity testing, generate predictive models of developmental toxicants, and elucidate mechanisms and adverse outcome pathways for abnormal development. This review gives an overview of the zebrafish embryo for pre dictive toxicology and 21st century toxicity testing. Developmental eye defects were selected as an example to evaluate data from the U.S. Environmental Protection Agency's ToxCast program comparing responses in zebrafish embryos with those from pregnant rats and rabbits for a subset of 24 environmental chemicals across >600 in vitro assay targets. Cross-species comparisons implied a common basis for biological pathways associated with neuronal defects, extracellular matrix remodeling, and mitotic arrest.

Predictive modeling of biological responses in the rat liver using in vitro Tox21 bioactivity: Benefits from high-throughput toxicokinetics.

Computational methods are needed to more efficiently leverage data from in vitro cell-based models to predict what occurs within whole body systems after chemical insults. This study set out to test the hypothesis that in vitro high-throughput screening (HTS) data can more effectively predict in vivo biological responses when chemical disposition and toxicokinetic (TK) modeling are employed. In vitro HTS data from the Tox21 consortium were analyzed in concert with chemical disposition modeling to derive nominal, aqueous, and intracellular estimates of concentrations eliciting 50% maximal activity. In vivo biological responses were captured using rat liver transcriptomic data from the DrugMatrix and TG-Gates databases and evaluated for pathway enrichment. In vivo dosing data were translated to equivalent body concentrations using HTTK modeling. Random forest models were then trained and tested to predict in vivo pathway-level activity across 221 chemicals using in vitro bioactivity data and physicochemical properties as predictor variables, incorporating methods to address imbalanced training data resulting from high instances of inactivity. Model performance was quantified using the area under the receiver operator characteristic curve (AUC-ROC) and compared across pathways for different combinations of predictor variables. All models that included toxicokinetics were found to outperform those that excluded toxicokinetics. Biological interpretation of the model features revealed that rather than a direct mapping of in vitro assays to in vivo pathways, unexpected combinations of multiple in vitro assays predicted in vivo pathway-level activities. To demonstrate the utility of these findings, the highest-performing model was leveraged to make new predictions of in vivo biological responses across all biological pathways for remaining chemicals tested in Tox21 with adequate data coverage (n = 6617). These results demonstrate that, when chemical disposition and toxicokinetics are carefully considered, in vitro HT screening data can be used to effectively predict in vivo biological responses to chemicals.

Benchmark Concentrations for Untargeted Metabolomics Versus Transcriptomics for Liver Injury Compounds in In Vitro Liver Models.

Interpretation of untargeted metabolomics data from both in vivo and physiologically relevant in vitro model systems continues to be a significant challenge for toxicology research. Potency-based modeling of toxicological responses has served as a pillar of interpretive context and translation of testing data. In this study, we leverage the resolving power of concentration-response modeling through benchmark concentration (BMC) analysis to interpret untargeted metabolomics data from differentiated cultures of HepaRG cells exposed to a panel of reference compounds and integrate data in a potency-aligned framework with matched transcriptomic data. For this work, we characterized biological responses to classical human liver injury compounds and comparator compounds, known to not cause liver injury in humans, at 10 exposure concentrations in spent culture media by untargeted liquid chromatography-mass spectrometry analysis. The analyte features observed (with limited metabolites identified) were analyzed using BMC modeling to derive compound-induced points of departure. The results revealed liver injury compounds produced concentration-related increases in metabolomic response compared to those rarely associated with liver injury (ie, sucrose, potassium chloride). Moreover, the distributions of altered metabolomic features were largely comparable with those observed using high throughput transcriptomics, which were further extended to investigate the potential for in vitro observed biological responses to be observed in humans with exposures at therapeutic doses. These results demonstrate the utility of BMC modeling of untargeted metabolomics data as a sensitive and quantitative indicator of human liver injury potential.

Profiling the Tox21 Chemical Collection for Acetylcholinesterase Inhibition.

BACKGROUND: Inhibition of acetylcholinesterase (AChE), a biomarker of organophosphorous and carbamate exposure in environmental and occupational human health, has been commonly used to identify potential safety liabilities. So far, many environmental chemicals, including drug candidates, food additives, and industrial chemicals, have not been thoroughly evaluated for their inhibitory effects on AChE activity. AChE inhibitors can have therapeutic applications (e.g., tacrine and donepezil) or neurotoxic consequences (e.g., insecticides and nerve agents). OBJECTIVES: The objective of the current study was to identify environmental chemicals that inhibit AChE activity using in vitro and in silico models. METHODS: To identify AChE inhibitors rapidly and efficiently, we have screened the Toxicology in the 21st Century (Tox21) 10K compound library in a quantitative high-throughput screening (qHTS) platform by using the homogenous cell-based AChE inhibition assay and enzyme-based AChE inhibition assays (with or without microsomes). AChE inhibitors identified from the primary screening were further tested in monolayer or spheroid formed by SH-SY5Y and neural stem cell models. The inhibition and binding modes of these identified compounds were studied with time-dependent enzyme-based AChE inhibition assay and molecular docking, respectively. RESULTS: A group of known AChE inhibitors, such as donepezil, ambenonium dichloride, and tacrine hydrochloride, as well as many previously unreported AChE inhibitors, such as chelerythrine chloride and cilostazol, were identified in this study. Many of these compounds, such as pyrazophos, phosalone, and triazophos, needed metabolic activation. This study identified both reversible (e.g., donepezil and tacrine) and irreversible inhibitors (e.g., chlorpyrifos and bromophos-ethyl). Molecular docking analyses were performed to explain the relative inhibitory potency of selected compounds. CONCLUSIONS: Our tiered qHTS approach allowed us to generate a robust and reliable data set to evaluate large sets of environmental compounds for their AChE inhibitory activity. https://doi.org/10.1289/EHP6993.

NAM-supported read-across: From case studies to regulatory guidance in safety assessment.

The use of new approach methodologies (NAMs) in support of read-across (RAx) approaches for regulatory purposes is a main goal of the EU-ToxRisk project. To bring this forward, EU-ToxRisk partners convened a workshop in close collaboration with regulatory representatives from key organizations including European regulatory agencies, such as the European Chemicals Agency (ECHA) and the European Food Safety Authority (EFSA), as well as the Scientific Committee on Consumer Safety (SCCS), national agencies from several European countries, Japan, Canada and the USA, as well as the Organisation for Economic Cooperation and Development (OECD). More than a hundred people actively participated in the discussions, bringing together diverse viewpoints across academia, regulators and industry. The discussion was organized starting from five practical cases of RAx applied to specific problems that offered the oppor-tunity to consider real examples. There was general consensus that NAMs can improve confidence in RAx, in particular in defining category boundaries as well as characterizing the similarities/dissimilarities between source and target substances. In addition to describing dynamics, NAMs can be helpful in terms of kinetics and metabolism that may play an important role in the demonstration of similarity or dissimilarity among the members of a category. NAMs were also noted as effective in providing quanti-tative data correlated with traditional no observed adverse effect levels (NOAELs) used in risk assessment, while reducing the uncertainty on the final conclusion. An interesting point of view was the advice on calibrating the number of new tests that should be carefully selected, avoiding the allure of "the more, the better". Unfortunately, yet unsurprisingly, there was no single approach befitting every case, requiring careful analysis delineating the optimal approach. Expert analysis and assessment of each specific case is still an important step in the process.

Characterizing cleft palate toxicants using ToxCast data, chemical structure, and the biomedical literature.

Cleft palate has been linked to both genetic and environmental factors that perturb key events during palatal morphogenesis. As a developmental outcome, it presents a challenging, mechanistically complex endpoint for predictive modeling. A data set of 500 chemicals evaluated for their ability to induce cleft palate in animal prenatal developmental studies was compiled from Toxicity Reference Database and the biomedical literature, which included 63 cleft palate active and 437 inactive chemicals. To characterize the potential molecular targets for chemical-induced cleft palate, we mined the ToxCast high-throughput screening database for patterns and linkages in bioactivity profiles and chemical structural descriptors. ToxCast assay results were filtered for cytotoxicity and grouped by target gene activity to produce a "gene score." Following unsuccessful attempts to derive a global prediction model using structural and gene score descriptors, hierarchical clustering was applied to the set of 63 cleft palate positives to extract local structure-bioactivity clusters for follow-up study. Patterns of enrichment were confirmed on the complete data set, that is, including cleft palate inactives, and putative molecular initiating events identified. The clusters corresponded to ToxCast assays for cytochrome P450s, G-protein coupled receptors, retinoic acid receptors, the glucocorticoid receptor, and tyrosine kinases/phosphatases. These patterns and linkages were organized into preliminary decision trees and the resulting inferences were mapped to a putative adverse outcome pathway framework for cleft palate supported by literature evidence of current mechanistic understanding. This general data-driven approach offers a promising avenue for mining chemical-bioassay drivers of complex developmental endpoints where data are often limited.