Mutagenicity evaluation to UV filters of benzophenone-6, benzophenone-8, and 4-methylbenzylidene camphor by Ames test.
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SUBMITTER: Zhang J
PROVIDER: S-EPMC8412341 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In this study, the morphology and sorption behavior of polyethylene terephthalate (PET) microplastics during the aging process are investigated. To clarify the sorption mechanism of aged PET microplastics, the common sunblock 4-methylbenzylidene camphor (4-MBC) was chosen as the target contaminant, and UV irradiation was used for the laboratory aging simulation. The results show that oxygen-containing functional groups (carboxylic, carbonyl, ketone and hydroxyl groups) increase on the surface of aged PET microplastics. Based on density functional theory (DFT) simulations, the camphor part of 4-MBC acts as a hydrogen bond acceptor, whereas the carboxylic group on aged PET microplastics acts as a hydrogen bond donor. The formation of hydrogen bonding causes increased sorption of 4-MBC on aged PET microplastics. The sorption capacity increased from 5 to 11 μg g-1 for 50 ppb 4-MBC with 100 mg PET microplastics after a five-day aging process. Other environmental factors that affect sorption were also identified; a higher pH value and the presence of salinity reduced the amount of sorption. The sorption of virgin PET ranged from 8.0 to 3.4 μg g-1 and the sorption of aged PET ranged from 22 to 5 μg g-1 at pH 4 to 10. In the presence of salinity (10% seawater), the virgin PET sorption dropped to 2.1 μg g-1 while the aged PET sorption dropped to 4 μg g-1. A similar phenomenon was also observed in the sorption behavior under natural sunlight (the sorption of PET increased from 0.4 to 0.8 μg g-1 after 6 months of aging). The potential risk to ecosystems of aged PET microplastics under prolonged sunlight exposure in the natural environment could be greater than that predicted for virgin microplastics.
| S-EPMC9042197 | biostudies-literature
Project description:IntroductionSeveral alkenylbenzenes, including methyleugenol (ME), are present in a wide range of botanicals and exhibit carcinogenic and mutagenic properties. Negative results are generally obtained for alkenylbenzenes in standard in vitro genotoxicity tests, including the Ames test. A lack of mutagenicity observed in such tests is thought to result from impaired metabolic activation of alkenylbenzenes via hydroxylation, with subsequent sulfoconjugation to its ultimate mutagenic or carcinogenic form. Although recent studies have reported the mutagenicity of hydroxylated ME metabolites in the Ames test using modified TA100 strains expressing human sulfotransferases (SULTs), to our knowledge, the detection of ME mutagenicity has not yet been reported.FindingsUsing strain TA100-hSULT1C2, which expresses human SULT1C2, we optimized the protein content of S9 Mix and the pre-incubation time required to promote metabolic activation in the Ames test. This procedure enabled us to obtain a positive response with ME.ConclusionsWe established Ames-test conditions enabling the detection of ME-induced mutagenicity, using a strain expressing human SULT1C2 in the presence of induced-rat S9 Mix. This simple approach will help assess the mutagenicity of other alkenylbenzenes and related chemicals.
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Project description:There is a growing concern about the occurrence of bisphenols and benzophenone UV filters in natural ecosystems, while data are limited regarding their actual occurrence in wildlife species, especially in raptors. In this study, concentrations of bisphenol and benzophenone UV filter analogues were determined in liver tissue samples (n = 38) from white-tailed eagles (Haliaeetus albicilla) that were found dead in Smøla (2006-2018), which is a Norwegian municipality that holds one of the densest breeding populations of white-tailed eagles in Europe. Bisphenol AF (BPAF; a fluorinated analogue) was the most ubiquitous contaminant since it was detected in 32 liver samples at concentrations ranging from 1.08 to 6.68 ng/g wet weight (w.w.), followed by bisphenol A (BPA, mean 10.4 ng/g w.w.), benzophenone-1 (BzP-1, mean 3.24 ng/g w.w.), and 4-hydroxybenzophenone (4-OH-BzP, mean 0.62 ng/g w.w.). The concentrations found in livers suggested that white-tailed eagles potentially accumulate bisphenols and benzophenone UV filters, which raises concern, as these plastic and personal care product-related emerging contaminants can show endocrine-disrupting properties. The high detection frequency of the fluorinated BPAF warrants further attention as other fluorinated compounds have proven to be extremely persistent and potentially harmful to wildlife.
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Project description:The widespread occurrence of benzophenone-type ultraviolet (UV) filter has raised the public concerns over the ecotoxicological effects of these chemicals. The present study assessed the joint toxicity of two representative benzophenones, benzophenone-1 (BP-1) and benzophenone-3 (BP-3), on the green alga Chlamydomonas reinhardtii using response surface methodologies (RSM). Specific growth rate and photosynthetic pigments were used as endpoints to evaluate the toxic effects. Generally, exposure to the combined BP-1 and BP-3 negatively affected cell growth and pigments production, with higher inhibitions at higher exposure concentrations. The simultaneous reduction in growth rate and pigments contents indicated that BP-1 and BP-3 regulated the growth of the tested alga by affecting the photosynthesis process. Results also showed that second order polynomial regression models fitted well with experimental results for all endpoints. The obtained regression models further indicated that the effects of the combination stemmed significantly from the linear concentration of BP-1 and BP-3. The overall results demonstrated that RSM could be a useful tool in ecotoxicological studies.
| S-EPMC5874781 | biostudies-literature
Project description:The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure-activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.
| S-EPMC6402315 | biostudies-literature
Project description:Background(Quantitative) Structure-Activity Relationship ((Q)SAR) is a promising approach to predict the potential adverse effects of chemicals based on their structure without performing toxicological studies. We evaluate the mutagenicity of food flavor chemicals by (Q) SAR tools, identify potentially mutagenic chemicals, and verify their mutagenicity by actual Ames test.ResultsThe Ames mutagenicity of 3942 food flavor chemicals was predicted using two (Q)SAR) tools, DEREK Nexus and CASE Ultra. Three thousand five hundred seventy-five chemicals (91%) were judged to be negative in both (Q) SAR tools, and 75 chemicals (2%) were predicted to be positive in both (Q) SAR tools. When the Ames test was conducted on ten of these positive chemicals, nine showed positive results.ConclusionThe (Q) SAR method can be used for screening the mutagenicity of food flavors.
| S-EPMC7706032 | biostudies-literature
Project description:BackgroundIn drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting the genotoxicity of reagent-sized aryl-amines (ArNH2), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of what molecular descriptors could improve these predictions and whether external data sets are appropriate for further training.ResultsIn trying to extend and improve this model beyond this quantum mechanical reaction energy, we faced considerable difficulty, which was surprising considering the long history and success of QSAR model development for this test. Other quantum mechanics descriptors were compared to this reaction energy including AM1 semi-empirical orbital energies, nitrenium formation with alternative leaving groups, nitrenium charge, and aryl-amine anion formation energy. Nitrenium formation energy, regardless of the starting species, was found to be the most useful single descriptor. External sets used in other QSAR investigations did not present the same difficulty using the same methods and descriptors. When considering all substructures rather than just aryl-amines, we also noted a significantly lower performance for the Novartis set. The performance gap between Novartis and external sets persists across different descriptors and learning methods. The profiles of the Novartis and external data are significantly different both in aryl-amines and considering all substructures. The Novartis and external data sets are easily separated in an unsupervised clustering using chemical fingerprints. The chemical differences are discussed and visualized using Kohonen Self-Organizing Maps trained on chemical fingerprints, mutagenic substructure prevalence, and molecular weight.ConclusionsDespite extensive work in the area of predicting this particular toxicity, work in designing and publishing more relevant test sets for compounds relevant to drug discovery is still necessary. This work also shows that great care must be taken in using QSAR models to replace experimental evidence. When considering all substructures, a random forest model, which can inherently cover distinct neighborhoods, built on Novartis data and previously reported external data provided a suitable model.
| S-EPMC3277490 | biostudies-literature
Project description:The use of sunscreens is an established and recommended practice to protect skin from solar-induced damage. Around 30 UV filters can be used in sunscreen products in the European Union, which ought to follow the requirements of the regulation 1223/2009 to ensure their efficacy and safety for humans. Nevertheless, low photostability and putative toxicity for humans and environment have been reported for some UV filters. Particularly, the negative impact in marine organisms has recently raised concern on the scientific community. Therefore, it is important to develop new UV filters with improved safety profile and photostability. Over the last two decades, nearly 200 new compounds have revealed promising photoprotection properties. The explored compounds were obtained through different approaches, including exploration of natural sources, synthetic pathways, and nanotechnology. Almost 50 natural products and around 140 synthetic derivatives, such as benzimidazoles, benzotriazoles, hydroxycinnamic acids, xanthones, triazines, among others, have been studied aiming the discovery of novel, effective, and safer future photoprotective agents. Herein, we provide the reader with an overview about UV filters' challenges and prospects, offering a forward-looking to the next-generation of UV filters.
| S-EPMC8955451 | biostudies-literature
Project description:Concern has arisen about benzophenone (BP) ultraviolet (UV) radiation filters, given their use in sunscreen and personal-care products and their reported estrogenic and antiandrogenic activity. We recruited 501 couples who were discontinuing use of contraceptives in order to become pregnant for the Longitudinal Investigation of Fertility and the Environment (LIFE) Study (Michigan and Texas, 2005-2009). Couples provided urine specimens and completed daily journals until they either achieved pregnancy or had tried for 12 months. Women used fertility monitors to time sexual intercourse and digital pregnancy tests. Urinary concentrations of 5 UV filters (ng/mL) were determined using triple-quadrupole mass spectrometry: 2,4-dihydroxybenzophenone (also called BP-1); 2,2',4,4'-tetrahydroxybenzophenone (BP-2); 2-hydroxy-4-methoxybenzophenone (BP-3); 2,2'-dihydroxy-4-methoxybenzophenone (BP-8); and 4-hydroxybenzophenone. Fecundability odds ratios were estimated for each UV filter (dichotomized at the 75th percentile) and adjusted for age, creatinine concentration, body mass index (weight (kg)/height (m)(2)), cotinine concentration, season, and site, while accounting for time off contraception. Separate models were fitted for each UV filter and partner; final models included partners' concentrations. Male partners' concentrations of BP-2 and 4-hydroxybenzophenone were associated with reduced fecundity in adjusted models (fecundability odds ratio (FOR) = 0.69 (95% confidence interval (CI): 0.50, 0.95) and FOR = 0.74 (95% CI: 0.54, 1.00), respectively). In models adjusting for both partners' concentrations, male BP-2 concentration remained associated with reduced fecundity (FOR = 0.69, 95% CI: 0.49, 0.97). These data suggest that male exposure to select UV filters may diminish couples' fecundity, resulting in a longer time to pregnancy.
| S-EPMC4262441 | biostudies-literature
Project description:BackgroundA new algorithm has been developed to enable the interpretation of black box models. The developed algorithm is agnostic to learning algorithm and open to all structural based descriptors such as fragments, keys and hashed fingerprints. The algorithm has provided meaningful interpretation of Ames mutagenicity predictions from both random forest and support vector machine models built on a variety of structural fingerprints.A fragmentation algorithm is utilised to investigate the model's behaviour on specific substructures present in the query. An output is formulated summarising causes of activation and deactivation. The algorithm is able to identify multiple causes of activation or deactivation in addition to identifying localised deactivations where the prediction for the query is active overall. No loss in performance is seen as there is no change in the prediction; the interpretation is produced directly on the model's behaviour for the specific query.ResultsModels have been built using multiple learning algorithms including support vector machine and random forest. The models were built on public Ames mutagenicity data and a variety of fingerprint descriptors were used. These models produced a good performance in both internal and external validation with accuracies around 82%. The models were used to evaluate the interpretation algorithm. Interpretation was revealed that links closely with understood mechanisms for Ames mutagenicity.ConclusionThis methodology allows for a greater utilisation of the predictions made by black box models and can expedite further study based on the output for a (quantitative) structure activity model. Additionally the algorithm could be utilised for chemical dataset investigation and knowledge extraction/human SAR development.
| S-EPMC3997921 | biostudies-literature