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Factors that increase estrogen or progesterone (P4) action are well-established as increasing breast cancer risk, and many first-line treatments to prevent breast cancer recurrence work by blocking estrogen synthesis or action. In previous work, using data from an in vitro steroidogenesis assay developed for the U.S. Environmental Protection Agency (EPA) ToxCast program, we identified 182 chemicals that increased estradiol (E2up) and 185 that increased progesterone (P4up) in human H295R adrenocortical carcinoma cells, an OECD validated assay for steroidogenesis. Chemicals known to induce mammary effects in vivo were very likely to increase E2 or P4 synthesis, further supporting the importance of these pathways for breast cancer. To identify additional chemical exposures that may increase breast cancer risk through E2 or P4 steroidogenesis, we developed a cheminformatics approach to identify structural features associated with these activities and to predict other E2 or P4 steroidogens from their chemical structures. First, we used molecular descriptors and physicochemical properties to cluster the 2,012 chemicals screened in the steroidogenesis assay using a self-organizing map (SOM). Structural features such as triazine, phenol, or more broadly benzene ramified with halide, amine or alcohol, are enriched for E2 or P4up chemicals. Among E2up chemicals, phenol and benzenone are found as significant substructures, along with nitrogen-containing biphenyls. For P4up chemicals, phenol and complex aromatic systems ramified with oxygen-based groups such as flavone or phenolphthalein are significant substructures. Chemicals that are active for both E2up and P4up are enriched with substructures such as dihydroxy phosphanedithione or are small chemicals that contain one benzene ramified with chlorine, alcohol, methyl or primary amine. These results are confirmed with a chemotype ToxPrint analysis. Then, we used machine learning and artificial intelligence algorithms to develop and validate predictive classification QSAR models for E2up and P4up chemicals. These models gave reasonable external prediction performances (balanced accuracy ~ 0.8 and Matthews Coefficient Correlation ~ 0.5) on an external validation. The QSAR models were enriched by adding a confidence score that considers the chemical applicability domain and a ToxPrint assessment of the chemical. This profiling and these models may be useful to direct future testing and risk assessments for chemicals related to breast cancer and other hormonally-mediated outcomes. [ABSTRACT FROM AUTHOR]

Plant-derived products are frequently found as ingredients in cosmetics. However, the current data show non-neglectable skin sensitizing potential of these preparations suggesting an urgent need for data regarding their health safety profile. The aim of this study was to assess the skin sensitization potential of commercial essential oils by selected Lamiaceae species (Lavandula angustifolia, Melissa officinalis, Mentha longifolia, Thymus vulgaris, Salvia officinalis, and Rosmarinus officinalis) using a chemistry-based Direct Peptide Reactivity Assay (DPRA) in order to predict their potential allergic properties. In the DPRA assay, nucleophile-containing synthetic peptides (cysteine peptide and lysine peptide) were incubated with the test substance for 24 h. Depletion of the peptide in the reaction mixture was measured by high-pressure liquid chromatography (HPLC) using UV detection and the average peptide depletion data for cysteine and lysine was then calculated. Menthae longifoliae aetheroleum showed no or minimal reactivity with 4.48% cysteine depletion, Rosmarini aetheroleum and Salviae aetheroleum showed low reactivity with the 12.79% and 15.34% of cysteine depletion, respectively, while the other analyzed essential oils showed moderate reactivity with the cysteine depletion between 23.21 and 48.43%. According to DPRA predictive analysis, only Menthae longifoliae aetheroleum can be classified as negative, while all other essential oils may be classified as positive, thus having the potential to cause skin sensitization. [ABSTRACT FROM AUTHOR]

Moso bamboo (Phyllostachys pubescens, Gramineae) is a well-known medicinal and edible plant found in China with various bioactivities, but few systematic studies address the utilization of its anti-fungal activity. The extract of moso bamboo leaf showed good anti-fungal activity to Phytophthora capsici, Fusarium graminearum, Valsa mali Miyabe et Yamada, Botryosphaeria dothidea, Venturia nashicola, and Botrytis cinerea Pers, with inhibitory rate of 100.00%, 75.12%, 60.66%, 57.24%, 44.62%, and 30.16%, respectively. Anti-fungal activity was different by the difference of samples picking time and location. The extract showed good synergistic effects with carbendazim at the ratios of 9:1 and 15:1 (extract : carbendazim), and the co-toxicity coefficients were 124.4 and 139.95. Compound 2 was isolated and identified as the main active component, with the EC50 value of 11.02 mg L−1. Then, the extract was formulated as a 10% emulsion in water, which was stable and had no acute toxic effects. Moreover, a field trial about this formulation was assayed to control pepper phytophthora blight, with the control effect of 85.60%. These data provided a better understanding of the anti-fungal activity and relevant active component of moso bamboo leaf extract. Taken together, our findings illustrated that bamboo leaf extract could be developed and utilized as a botanical fungicide or fungicide adjuvant. [ABSTRACT FROM AUTHOR]

Metabolomics is an analytical technique that investigates the small biochemical molecules present within a biological sample isolated from a plant, animal, or cultured cells. It can be an extremely powerful tool in elucidating the specific metabolic changes within a biological system in response to an environmental challenge such as disease, infection, drugs, or toxins. A historically difficult step in the metabolomics pipeline is in data interpretation to a meaningful biological context, for such high-variability biological samples and in untargeted metabolomics studies that are hypothesis-generating by design. One way to achieve stronger biological context of metabolomic data is via the use of cultured cell models, particularly for mammalian biological systems. The benefits of in vitro metabolomics include a much greater control of external variables and no ethical concerns. The current concerns are with inconsistencies in experimental procedures and level of reporting standards between different studies. This review discusses some of these discrepancies between recent studies, such as metabolite extraction and data normalisation. The aim of this review is to highlight the importance of a standardised experimental approach to any cultured cell metabolomics study and suggests an example procedure fully inclusive of information that should be disclosed in regard to the cell type/s used and their culture conditions. Metabolomics of cultured cells has the potential to uncover previously unknown information about cell biology, functions and response mechanisms, and so the accurate biological interpretation of the data produced and its ability to be compared to other studies should be considered vitally important. [ABSTRACT FROM AUTHOR]

Background: Because of expanding presence of nanomaterials, there has been an increase in the exposure of humans to nanoparticles that is why nanotoxicology studies are important. A number of studies on the effects of nanomatrials in in vitro and in vivo systems have been published. Currently cytotoxicity of different nanoparticles is assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay on different cell lines to determine cell viability, a tedious and expensive method. The aim of this study was to evaluate the Artemia salina test in comparison with the MTT assay in the assessment of cytotoxicity of nanostructures because the former method is more rapid and convenient and less expensive. Methods: At the first stage, toxicity of different nanoparticles with different concentrations (1.56-400 µg/mL) was measured by means of the brine shrimp lethality test. At the second stage, the effect of nanoparticles on the viability of the L929 cell line was assessed using the MTT assay. Experiments were conducted with each concentration in triplicate. Results: The results obtained from both tests (A salina test and MTT assay) did not have statistically significant differences (P> 0.05). Conclusions: These findings suggest that the A. salina test may expedite toxicity experiments and decrease costs, and therefore, may be considered an alternative to the in vitro cell culture assay. [ABSTRACT FROM AUTHOR]

The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The exclusive use of in vivo testing is limited by the time and costs required for performing experiments and by the need to sacrifice a number of animals. (Quantitative) structure–activity relationships [(Q)SAR] proved a valid alternative to reduce and assist in vivo assays for assessing acute toxicological hazard. In the framework of a new international collaborative project, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods and the U.S. Environmental Protection Agency's National Center for Computational Toxicology compiled a large database of rat acute oral LD50 data, with the aim of supporting the development of new computational models for predicting five regulatory relevant acute toxicity endpoints. In this article, a series of regression and classification computational models were developed by employing different statistical and knowledge-based methodologies. External validation was performed to demonstrate the real-life predictability of models. Integrated modeling was then applied to improve performance of single models. Statistical results confirmed the relevance of developed models in regulatory frameworks, and confirmed the effectiveness of integrated modeling. The best integrated strategies reached RMSEs lower than 0.50 and the best classification models reached balanced accuracies over 0.70 for multi-class and over 0.80 for binary endpoints. Computed predictions will be hosted on the EPA's Chemistry Dashboard and made freely available to the scientific community. [ABSTRACT FROM AUTHOR]