Identifying chemicals based on receptor binding/bioactivation/mechanistic explanation associated with potential to elicit hepatotoxicity and to support structure activity relationship-based read-across.

The liver is the most common target organ in toxicology studies. The development of chemical structural alerts for identifying hepatotoxicity will play an important role in in silico model prediction and help strengthen the identification of analogs used in structure activity relationship (SAR)- based read-across. The aim of the current study is development of an SAR-based expert-system decision tree for screening of hepatotoxicants across a wide range of chemistry space and proposed modes of action for clustering of chemicals using defined core chemical categories based on receptor-binding or bioactivation. The decision tree is based on âˆ¼ 1180 different chemicals that were reviewed for hepatotoxicity information. Knowledge of chemical receptor binding, metabolism and mechanistic information were used to group these chemicals into 16 different categories and 102 subcategories: four categories describe binders to 9 different receptors, 11 categories are associated with possible reactive metabolites (RMs) and there is one miscellaneous category. Each chemical subcategory has been associated with possible modes of action (MOAs) or similar key structural features. This decision tree can help to screen potential liver toxicants associated with core structural alerts of receptor binding and/or RMs and be used as a component of weight of evidence decisions based on SAR read-across, and to fill data gaps.

Structure-activity relationship read-across and transcriptomics for branched carboxylic acids.

The purpose of this study was to use chemical similarity evaluations, transcriptional profiling, in vitro toxicokinetic data, and physiologically based pharmacokinetic (PBPK) models to support read-across for a series of branched carboxylic acids using valproic acid (VPA), a known developmental toxicant, as a comparator. The chemicals included 2-propylpentanoic acid (VPA), 2-ethylbutanoic acid, 2-ethylhexanoic acid (EHA), 2-methylnonanoic acid, 2-hexyldecanoic acid, 2-propylnonanoic acid (PNA), dipentyl acetic acid or 2-pentylheptanoic acid, octanoic acid (a straight chain alkyl acid), and 2-ethylhexanol. Transcriptomics was evaluated in 4 cell types (A549, HepG2, MCF7, and iCell cardiomyocytes) 6 h after exposure to 3 concentrations of the compounds, using the L1000 platform. The transcriptional profiling data indicate that 2- or 3-carbon alkyl substituents at the alpha position of the carboxylic acid (EHA and PNA) elicit a transcriptional profile similar to the one elicited by VPA. The transcriptional profile is different for the other chemicals tested, which provides support for limiting read-across from VPA to much shorter and longer acids. Molecular docking models for histone deacetylases, the putative target of VPA, provide a possible mechanistic explanation for the activity cliff elucidated by transcriptomics. In vitro toxicokinetic data were utilized in a PBPK model to estimate internal dosimetry. The PBPK modeling data show that as the branched chain increases, predicted plasma Cmax decreases. This work demonstrates how transcriptomics and other mode of action-based methods can improve read-across.

Read-across and new approach methodologies applied in a 10-step framework for cosmetics safety assessment - A case study with parabens.

Parabens are esters of para-hydroxybenzoic acid that have been used as preservatives in many types of products for decades including agrochemicals, pharmaceuticals, food and cosmetics. This illustrative case study with propylparaben (PP) demonstrates a 10-step read-across (RAX) framework in practice. It aims at establishing a proof-of-concept for the value added by new approach methodologies (NAMs) in read-across (RAX) for use in a next-generation risk assessment (NGRA) in order to assess consumer safety after exposure to PP-containing cosmetics. In addition to structural and physico-chemical properties, in silico information, toxicogenomics, in vitro toxicodynamic, toxicokinetic data from PBK models, and bioactivity data are used to provide evidence of the chemical and biological similarity of PP and analogues and to establish potency trends for observed effects in vitro. The chemical category under consideration is short (C1-C4) linear chain n-alkyl parabens: methylparaben, ethylparaben, propylparaben and butylparaben. The goal of this case study is to illustrate how a practical framework for RAX can be used to fill a hypothetical data gap for reproductive toxicity of the target chemical PP.

A 10-step framework for use of read-across (RAX) in next generation risk assessment (NGRA) for cosmetics safety assessment.

This paper presents a 10-step read-across (RAX) framework for use in cases where a threshold of toxicological concern (TTC) approach to cosmetics safety assessment is not possible. RAX builds on established approaches that have existed for more than two decades using chemical properties and in silico toxicology predictions, by further substantiating hypotheses on toxicological similarity of substances, and integrating new approach methodologies (NAM) in the biological and kinetic domains. NAM include new types of data on biological observations from, for example, in vitro assays, toxicogenomics, metabolomics, receptor binding screens and uses physiologically-based kinetic (PBK) modelling to inform about systemic exposure. NAM data can help to substantiate a mode/mechanism of action (MoA), and if similar chemicals can be shown to work by a similar MoA, a next generation risk assessment (NGRA) may be performed with acceptable confidence for a data-poor target substance with no or inadequate safety data, based on RAX approaches using data-rich analogue(s), and taking account of potency or kinetic/dynamic differences.

Article title: Transcriptional profiling efficacy to define biological activity similarity for cosmetic ingredients' safety assessment based on next-generation read-across.

The objective of this work was to use transcriptional profiling to assess the biological activity of structurally related chemicals to define their biological similarity and with that, substantiate the validity of a read-across approach usable in risk assessment. Two case studies are presented, one with 4 short alkyl chain parabens: methyl (MP), ethyl (EP), butyl (BP), and propylparaben (PP), as well as their main metabolite, p-hydroxybenzoic acid (pHBA) with the assumption that propylparaben was the target chemical; and a second one with caffeine and its main metabolites theophylline, theobromine and paraxanthine where CA was the target chemical. The comprehensive transcriptional response of MCF7, HepG2, A549 and ICell cardiomyocytes was evaluated (TempO-Seq) after exposure to vehicle-control, each paraben or pHBA, CA or its metabolites, at 3 non-cytotoxic concentrations, for 6 h. Differentially expressed genes (FDR ≥0.05, and fold change ±1.2≥) were identified for each chemical, at each concentration, and used to determine similarities. Each of the chemicals is able to elicit changes in the expression of a number of genes, as compared to controls. Importantly, the transcriptional profile elicited by each of the parabens shares a high degree of similarity across the group. The highest number of genes commonly affected was between butylparaben and PP. The transcriptional profile of the parabens is similar to the one elicited by estrogen receptor agonists, with BP being the closest structural and biological analogue for PP. In the CA case, the transcriptional profile elicited of all four methylxanthines had a high degree of similarity across the cell types, with CA and theophylline being the most active. The most robust response was obtained in the cardiomyocytes with the highest transcriptional profile similarity between CA and TP. The transcriptional profile of the methylxanthines is similar to the one elicited by inhibitors of phosphatidylinositol 3-kinase as well as other kinase inhibitors. Overall, our results support the approach of incorporating transcriptional profiling in well-designed in vitro tests as one robust stream of data to support biological similarity driven read-across procedures and strengthening the traditional structure-based approaches useful in risk assessment.

Incorporation of in vitro techniques for botanicals dietary supplement safety assessment - Towards evaluation of developmental and reproductive toxicity (DART).

As complex mixtures, botanicals present unique challenges when assessing safe use, particularly when endpoint gaps exist that cannot be fully resolved by existing toxicological literature. Here we explore in vitro gene expression as well receptor binding and enzyme activity as alternative assays to inform on developmental and reproductive toxicity (DART) relevant modes of action, since DART data gaps are common for botanicals. Specifically, botanicals suspected to have DART effects, in addition to those with a significant history of use, were tested in these assays. Gene expression changes in a number of different cell types were analysed using the connectivity mapping approach (CMap) to identify modes of action through a functional read across approach. Taken together with ligand affinity data obtained using a set of molecular targets customised towards known DART relevant modes of action, it was possible to inform DART risk using functional analogues, potency comparisons and a margin of internal exposure approach.

Use of connectivity mapping to support read across: A deeper dive using data from 186 chemicals, 19 cell lines and 2 case studies.

We previously demonstrated that the Connectivity Map (CMap) (Lamb et al., 2006) concept can be successfully applied to a predictive toxicology paradigm to generate meaningful MoA-based connections between chemicals (De Abrew et al., 2016). Here we expand both the chemical and biological (cell lines) domain for the method and demonstrate two applications, both in the area of read across. In the first application we demonstrate CMap's utility as a tool for testing biological relevance of source chemicals (analogs) during a chemistry led read across exercise. In the second application we demonstrate how CMap can be used to identify functionally relevant source chemicals (analogs) for a structure of interest (SOI)/target chemical with minimal knowledge of chemical structure. Finally, we highlight four factors: promiscuity of chemical, dose, cell line and timepoint as having significant impact on the output. We discuss the biological relevance of these four factors and incorporate them into a work flow.

Grouping 34 Chemicals Based on Mode of Action Using Connectivity Mapping.

Connectivity mapping is a method used in the pharmaceutical industry to find connections between small molecules, disease states, and genes. The concept can be applied to a predictive toxicology paradigm to find connections between chemicals, adverse events, and genes. In order to assess the applicability of the technique for predictive toxicology purposes, we performed gene array experiments on 34 different chemicals: bisphenol A, genistein, ethinyl-estradiol, tamoxifen, clofibrate, dehydorepiandrosterone, troglitazone, diethylhexyl phthalate, flutamide, trenbolone, phenobarbital, retinoic acid, thyroxine, 1α,25-dihydroxyvitamin D3, clobetasol, farnesol, chenodeoxycholic acid, progesterone, RU486, ketoconazole, valproic acid, desferrioxamine, amoxicillin, 6-aminonicotinamide, metformin, phenformin, methotrexate, vinblastine, ANIT (1-naphthyl isothiocyanate), griseofulvin, nicotine, imidacloprid, vorinostat, 2,3,7,8-tetrachloro-dibenzo-p-dioxin (TCDD) at the 6-, 24-, and 48-hour time points for 3 different concentrations in the 4 cell lines: MCF7, Ishikawa, HepaRG, and HepG2 GEO (super series accession no.: GSE69851). The 34 chemicals were grouped in to predefined mode of action (MOA)-based chemical classes based on current literature. Connectivity mapping was used to find linkages between each chemical and between chemical classes. Cell line-specific linkages were compared with each other and to test whether the method was platform and user independent, a similar analysis was performed against publicly available data. The study showed that the method can group chemicals based on MOAs and the inter-chemical class comparison alluded to connections between MOAs that were not predefined. Comparison to the publicly available data showed that the method is user and platform independent. The results provide an example of an alternate data analysis process for high-content data, beneficial for predictive toxicology, especially when grouping chemicals for read across purposes.

Toward Good Read-Across Practice (GRAP) guidance.

Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislations such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document aims to summarize the state-of-the-art, summarizes insights learned from reviewing ECHA published decisions as far as the relative successes/pitfalls surrounding read-across under REACH and compile the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA's published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015.

Dose- and Time-Dependent Transcriptional Response of Ishikawa Cells Exposed to Genistein.

To further define the utility of the Ishikawa cells as a reliable in vitro model to determine the potential estrogenic activity of chemicals of interest, transcriptional changes induced by genistein (GES) in Ishikawa cells at various doses (10 pM, 1 nM, 100 nM, and 10 µM) and time points (8, 24, and 48 h) were identified using a comprehensive microarray approach. Trend analysis indicated that the expression of 5342 unique genes was modified by GES in a dose- and time-dependent manner (P ≤ 0.0001). However, the majority of gene expression changes induced in Ishikawa cells were elicited by the highest dose of GES evaluated (10 µM). The GES' estrogenic activity was identified by comparing the Ishikawa cells' response to GES versus 17 α-ethynyl estradiol (EE, at equipotent doses, ie, 10 µM vs 1 µM, respectively) and was defined by changes in the expression of 284 unique genes elicited by GES and EE in the same direction, although the magnitude of the change for some genes was different. Further, comparing the response of the Ishikawa cells exposed to high doses of GES and EE versus the response of the juvenile rat uterus exposed to EE, we identified 66 unique genes which were up- or down regulated in a similar manner in vivo as well as in vitro Genistein elicits changes in multiple molecular pathways affecting various biological processes particularly associated with cell organization and biogenesis, regulation of translation, cell proliferation, and intracellular transport; processes also affected by estrogen exposure in the uterus of the rat. These results indicate that Ishikawa cells are capable of generating a biologically relevant estrogenic response and offer an in vitro model to assess this mode of action.

A strategy for safety assessment of chemicals with data gaps for developmental and/or reproductive toxicity.

Alternative methods for full replacement of in vivo tests for systemic endpoints are not yet available. Read across methods provide a means of maximizing utilization of existing data. A limitation for the use of read across methods is that they require analogs with test data. Repeat dose data are more frequently available than are developmental and/or reproductive toxicity (DART) studies. There is historical precedent for using repeat dose data in combination with a database uncertainty factor (UF) to account for missing DART data. We propose that use of the DART decision tree (Wu et al., 2013), in combination with a database UF, provides a path forward for DART data gap filling that better utilizes all of the data. Our hypothesis was that chemical structures identified by the DART tree as being related to structures with known DART toxicity would potentially have lower DART NOAELs compared to their respective repeat dose NOAELs than structures that lacked this association. Our analysis supports this hypothesis and as a result also supports that the DART decision tree can be used as part of weight of evidence in the selection of an appropriate DART database UF factor.

A novel transcriptomics based in vitro method to compare and predict hepatotoxicity based on mode of action.

High-content data have the potential to inform mechanism of action for toxicants. However, most data to support this notion have been generated in vivo. Because many cell lines and primary cells maintain a differentiated cell phenotype, it is possible that cells grown in culture may also be useful in predictive toxicology via high-content approaches such as whole-genome microarray. We evaluated global changes in gene expression in primary rat hepatocytes exposed to two concentrations of ten hepatotoxicants: acetaminophen (APAP), ß-naphthoflavone (BNF), chlorpromazine (CPZ), clofibrate (CLO), bis(2-ethylhexyl)phthalate (DEHP), diisononyl phthalate (DINP), methapyrilene (MP), valproic acid (VPA), phenobarbital (PB) and WY14643 at two separate time points. These compounds were selected to cover a range of mechanisms of toxicity, with some overlap in expected mechanism to address the question of how predictive gene expression analysis is, for a given mode of action. Gene expression microarray analysis was performed on cells after 24h and 48h of exposure to each chemical using Affymetrix microarrays. Cluster analysis suggests that the primary hepatocyte model was capable of responding to these hepatotoxicants, with changes in gene expression that appear to be mode of action-specific. Among the different methods used for analysis of the data, a combination method that used pathways (MOAs) to filter total probesets provided the most robust analysis. The analysis resulted in the phthalates clustering closely together, with the two other peroxisome proliferators, CLO and WY14643, eliciting similar responses at the whole-genome and pathway levels. The Cyp inducers PB, MP, CPZ and BNF also clustered together. VPA and APAP had profiles that were unique. A similar analysis was performed on externally available (TG-GATES) in vivo data for 6 of the chemicals (APAP, CLO, CPZ, MP, MP and WY14643) and compared to the in vitro result. These results indicate that transcription profiling using an in vitro assay may offer pertinent biological data to support predictions of in vivo hepatotoxicity potential.

Framework for identifying chemicals with structural features associated with the potential to act as developmental or reproductive toxicants.

Developmental and reproductive toxicity (DART) end points are important hazard end points that need to be addressed in the risk assessment of chemicals to determine whether or not they are the critical effects in the overall risk assessment. These hazard end points are difficult to predict using current in silico tools because of the diversity of mechanisms of action that elicit DART effects and the potential for narrow windows of vulnerability. DART end points have been projected to consume the majority of animals used for compliance with REACH; thus, additional nonanimal predictive tools are urgently needed. This article presents an empirically based decision tree for determining whether or not a chemical has receptor-binding properties and structural features that are consistent with chemical structures known to have toxicity for DART end points. The decision tree is based on a detailed review of 716 chemicals (664 positive, 16 negative, and 36 with insufficient data) that have DART end-point data and are grouped into defined receptor binding and chemical domains. When tested against a group of chemicals not included in the training set, the decision tree is shown to identify a high percentage of chemicals with known DART effects. It is proposed that this decision tree could be used both as a component of a screening system to identify chemicals of potential concern and as a component of weight-of-evidence decisions based on structure-activity relationships (SAR) to fill data gaps without generating additional test data. In addition, the chemical groupings generated could be used as a starting point for the development of hypotheses for in vitro testing to elucidate mode of action and ultimately in the development of refined SAR principles for DART that incorporate mode of action (adverse outcome pathways).

Effects of transplacental 17-α-ethynyl estradiol or bisphenol A on the developmental profile of steroidogenic acute regulatory protein in the rat testis.

Previous research from our laboratory has determined the transcript profiles for developing fetal rat female and male reproductive tracts following transplacental exposure to estrogens. Prenatal exposure to bisphenol A (BPA) or 17-α-ethynyl estradiol (EE) significantly affects steroidogenic acute regulatory (StAR) protein transcript levels in the developing male rat reproductive tract. The purpose of this study was to establish the intratesticular distribution and temporal expression pattern of StAR, a key gene involved in steroidogenesis. Beginning on gestation day (GD) 11, pregnant Sprague-Dawley rats were exposed daily to 10µg/kg/day EE and fetal testes were harvested at GD16, 18, or 20. Quantitative reverse transcriptase PCR (QRT-PCR) demonstrated no significant difference in StAR transcript levels present at GD16. However, at GD18, StAR transcripts were significantly decreased following exposure. Immunohistochemistry demonstrated similar StAR protein levels in interstitial region of GD16 testes and an obvious decrease in StAR protein levels in the interstitial region of GD18 testes. Moreover, starting at GD11 additional dams were dosed with 0.001 or 0.1 µg/kg/day EE or 0.02, 0.5, 400 mg/kg/day BPA via subcutaneous injections. QRT-PCR validated previous microarray dose-related decreases in StAR transcripts at GD20, whereas immunohistochemistry results demonstrated decreases in StAR protein levels in the interstitial region at the highest EE and BPA doses only. Neither EE nor BPA exposure caused morphological changes in the developing seminiferous cords, Sertoli cells, gonocytes, or the interstitial region or Leydig cells at GD16-20. High levels of estrogens decrease StAR expression in the fetal rat testis during late gestation.

Case studies to test: A framework for using structural, reactivity, metabolic and physicochemical similarity to evaluate the suitability of analogs for SAR-based toxicological assessments.

A process for evaluating analogs for use in SAR (Structure-Activity Relationship) assessments was previously published (Wu et al. 2010). Subsequently, this process has been updated to include a decision tree for estrogen binding (from US EPA) and flags for developmental and reproductive toxicity (DART). This paper presents the results of blinded case studies designed to test this updated framework. The results of these case studies support the conclusion that the process outlined by Wu et al. (2010) can be successfully applied to develop surrogate values for risk assessment. The read across results generated by the process were shown to be protective when compared to the actual toxicity data. Successful application of the approach requires significant expertise as well as discipline to not overstep the boundaries of the defined analogs and the rating system. The end result of this rigor can be the inability to read across all endpoints for all chemicals resulting in data gaps that cannot be filled using read across, however, this reflects the current state of the science and is preferable to making non-protective decisions. Future work will be targeted towards expanding read across capabilities. Two examples of a broader category approach are also shown.

Predicting developmental toxicity through toxicogenomics.

Global analysis of gene expression in target cells or tissues in response to a toxicant holds significant promise for predictive toxicology. Toxicants elicit a characteristic pattern of gene expression that is dependent on mechanism of action. These mechanism-specific transcript profiles can be used as the basis for predictive toxicology. Potential applications include prioritizing chemicals for testing and customizing testing approaches based on the chemical. Results that are useful in this predictive context can be obtained from animal or in vitro models. Gene expression analysis can also be used to elucidate the shape of the dose-response curve at exposure levels below the no observed adverse effect level, an important need in risk assessment. In this review, we will illustrate each of these points using our research on estrogen and an estrogenic mode of action as a model for how to use gene expression data in a predictive way. Although gene expression in response to estrogens is tissue, life stage, and sex specific, it is feasible to identify transcript profiles that are diagnostic of this mode of action.

The genomic response of Ishikawa cells to bisphenol A exposure is dose- and time-dependent.

A reliable in vitro model to determine the potential estrogenic activity of chemicals of interest is still unavailable. To further investigate the usefulness of a human-derived cell line, we determined the transcriptional changes induced by bisphenol A (BPA) in Ishikawa cells at various doses (1 nM, 100 nM, 10 microM, and 100 microM) and time points (8, 24 and 48 h) by comparing the response of approximately 38,500 human genes and ESTs between treatment groups and controls (vehicle-treated). By trend analysis, we determined that the expression of 2794 genes was modified by BPA in a dose- and time-dependent manner (p< or =0.0001). However, the majority of gene expression changes induced in Ishikawa cells were elicited by the highest doses of BPA evaluated (10-100 microM), while the genomic response of the cells exposed to low doses of BPA was essentially negligible. By comparing the Ishikawa cells' response to BPA vs.17 alpha-ethynyl estradiol we determined that the change in the expression of 307 genes was identical in the direction of the change, although the magnitude of the change for some genes was different. Further, the response of Ishikawa cells to high doses of BPA shared similarities to the estrogenic response of the rat uterus, specifically, 362 genes were regulated in a similar manner in vivo as well as in vitro. Gene ontology analysis indicated that BPA results in changes to multiple molecular pathways affecting various biological processes particularly associated with cell organization and biogenesis, regulation of translation, cell proliferation, and intracellular transport; processes also affected by estrogen exposure in the uterus of the rat. These results indicate that Ishikawa cells are capable of generating a biologically relevant estrogenic response after exposure to chemicals with varied estrogenic activity, and offer an in vitro model to assess this mode of action.

The genomic response of a human uterine endometrial adenocarcinoma cell line to 17alpha-ethynyl estradiol.

We have determined the gene expression profile induced by 17 alpha-ethynyl estradiol (EE) in Ishikawa cells, a human uterine-derived estrogen-sensitive cell line, at various doses (1 pM, 100 pM, 10 nM, and 1 microM) and time points (8, 24, and 48 h). The transcript profiles were compared between treatment groups and controls (vehicle-treated) using high-density oligonucleotide arrays to determine the expression level of approximately 38,500 human genes. By trend analysis, we determined that the expression of 2560 genes was modified by exposure to EE in a dose- and time-dependent manner (p