Publications by authors named "Jorge Naciff"

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 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.

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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.

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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.

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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.

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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.

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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.

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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).

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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.

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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).

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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.

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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.

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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.

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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.

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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.

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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).

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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.

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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.

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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 View Article and Find Full Text PDF

The rat uterus responds to acute estrogen treatment with a series of well-characterized physiological responses; however, the gene expression changes required to elicit these responses have not been fully characterized. In order to understand early events induced by estrogen exposure in vivo, we evaluated the temporal gene expression in the uterus of the immature rat after a single dose of 17 alpha-ethinyl estradiol (EE) by microarray analysis, evaluating the expression of 15,923 genes. Immature 20-day-old rats were exposed to a single dose of EE (10 microg/kg), and the effects on uterine histology, weight, and gene expression were determined after 1, 2, 8, 24, 48, 72, and 96 h.

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Recently gene expression studies have been multiplied at an accelerated rate by the use of high-density microarrays. By assaying thousands of transcripts at a time, microarrays have led to the discovery of dozens of genes involved in particular biochemical processes, for example, the response of a tissue/organ to a given chemical with therapeutic or toxic properties. The next step in these studies is to focus on the response of a subset of relevant genes to verify or refine potential therapeutic or toxic properties.

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The purpose of this study was to determine (1) the transcriptional program elicited by exposure to three estrogen receptor (ER) agonists: 17 alpha-ethynyl estradiol (EE), genistein (Ges), and bisphenol A (BPA) during fetal development of the rat testis and epididymis; and (2) whether very low dosages of estrogens (evaluated over five orders of magnitude of dosage) produce unexpected changes in gene expression (i.e., a non-monotonic dose-response curve).

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Background: In a previous study, we determined the effects of 17-alpha-ethynyl estradiol (EE) on gene expression using microarrays that represented approximately 9,000 genes, which was the state of-the-art. Higher content arrays with almost double the number of genes have since become available. In order to better determine whether common sets of gene expression changes can be predictive of estrogenic activity, we have replicated the previous experiment using the more comprehensive microarray.

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Microarrays make it possible to evaluate the responses of a major fraction of the genome in response to physiological perturbation or exogenous insult. This represents a huge advance in our ability to detect changes in gene expression that may be responsible for physiological or toxicological responses. Our laboratory is interested in the effects of estrogens on female reproductive system development and function.

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