Chemical Research in Toxicology最新文献

{"title":"A Review of the Most Frequent Compounds, Metals, and Compound and Metal Mixtures Found at U.S. Superfund Sites and Their Carcinogenic Potential.","authors":"June K Dunnick, Charles P Schmitt, Darlene Dixon","doi":"10.1021/acs.chemrestox.4c00506","DOIUrl":"10.1021/acs.chemrestox.4c00506","url":null,"abstract":"<p><p>The United States Environmental Protection Agency's (U.S. EPA) National Priorities List (NPL) is a list of sites in the U.S. and its territories of national priority that are sources of known hazardous contaminants, pollutants, or substances that pose a significant risk to human health and the environment. These sites are commonly termed U.S. Superfund sites and contain many harmful compounds and metals. This paper reviews the carcinogenic potential of the most frequent compounds, metals, and mixtures at U.S. Superfund sites. Of the most frequent compounds and metals identified at U.S. Superfund sites, some are classified as human carcinogens and some as probable/possible human carcinogens. The most frequent mixtures of three individual carcinogenic compound or metals at U.S. Superfund sites include: nickel, arsenic, and cadmium (496 sites); benzene, arsenic, trichloroethene (451 sites); benzene, vinyl chloride, trichloroethene (420 sites); and arsenic, vinyl chloride, trichloroethene (386 sites). Many compounds or metals that are frequently found at U.S. Superfund Sites have not been evaluated for carcinogenic activity because of limited data including copper, xylene, mercury, barium, and iron. Factors in human cancer development include both environmental factors and genetic disease susceptibility backgrounds. Thus, future mixture toxicology studies should be conducted with a design that looks at mixture toxicology in a variety of models with varied genetic backgrounds.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"963-974"},"PeriodicalIF":3.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12199832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Metabolic Similarity in Read-Across: A Case Study Using Graph Convolutional Networks to Predict Genotoxicity Outcomes from Simulated Metabolic Networks.","authors":"Brett Hagan, Louis Groff, Grace Patlewicz, Imran Shah","doi":"10.1021/acs.chemrestox.5c00120","DOIUrl":"10.1021/acs.chemrestox.5c00120","url":null,"abstract":"<p><p>Metabolic similarity is a key consideration in evaluating candidate source analogues for read-across (RAx), but approaches to systematically characterize metabolism for read-across prediction are still evolving. Metabolic similarity is multifaceted, considering the similarity of the metabolic tree, the metabolites simulated, and the transformation pathways. The structure of metabolic trees lends itself naturally to graph representations, for which several methods, including graph convolutional networks (GCNs), can be applied to quantify the pairwise similarity between the target and source analogue(s) within an analogue or category approach. In this study, we compared metabolic graph representations of metabolites with structural similarities in predicting genotoxicity outcomes using a data set comprising 5403 chemicals. Xenobiotic metabolism pathways were predicted using the rat liver models within the commercial expert system, TIssue MEtabolism Simulator (TIMES), and the phase I and II xenobiotic metabolism modules within the freely available system BioTransformer. Metabolic pathways were converted to graphs and used to train GCNs, generating embeddings for each chemical. The classification performance of generalized read-across (GenRA), random forest (RF), logistic regression (LR), and multilayer perceptron (MLP) was compared using GCN-derived embeddings versus both Morgan and MACCS chemical fingerprints to identify genotoxic chemicals. GCN embeddings with LR, based on in vivo TIMES metabolism predictions using MACCS fingerprints as node features, achieved the highest area under the curve of the receiver operating characteristic of 0.807, outperforming GenRA and LR with MACCS fingerprints by 14.47% and 5.49%, respectively. Our findings suggest that GCN embeddings of predicted metabolism pathways perform substantially better than structural features of the parent chemicals in predicting genotoxicity outcomes. Such GCN embeddings offer new avenues of systematically encoding end point metabolic information to facilitate analogue identification for read-across.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"1122-1133"},"PeriodicalIF":3.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144155227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Untargeted DNA Adductomics Identifies Aristolochic Acid III as a Potent DNA-Damaging Agent among 11 Substituted Aromatic Genotoxicants in the Rat Urinary System","authors":"Medjda Bellamri,&nbsp;Scott J. Walmsley,&nbsp;Lihua Yao,&nbsp;Thomas A. Rosenquist,&nbsp;Christopher J. Weight,&nbsp;Peter W. Villalta and Robert J. Turesky*,&nbsp;","doi":"10.1021/acs.chemrestox.5c00126","DOIUrl":"10.1021/acs.chemrestox.5c00126","url":null,"abstract":"<p >An untargeted, data-independent acquisition high-resolution accurate tandem mass spectrometry method using an Orbitrap mass spectrometer was employed to screen for DNA adducts formed from 11 environmental and dietary aromatic or substituted aromatic carcinogens in the kidney, urinary bladder, prostate, pancreas, liver, and the lung of male rats 24 h after treatment. Among the carcinogens investigated, DNA adducts of the structurally related nitrophenanthrenes 3-nitrobenzanthrone (3-NBA), an atmospheric pollutant, and 8-methoxy-6-nitrophenanthro[3,4-<i>d</i>]-1,3-dioxole-5-carboxylic acid (AA-I), a naturally occurring genotoxicant from Aristolochiaceae plants, were the most abundant across most organs, forming both 2′-deoxyguanosine (dG) and 2′-deoxyadenosine (dA) adducts. In contrast, significantly lower DNA adduct levels were formed with the aromatic amine 4-aminobiphenyl and 2-nitrofluorene, an oxidized derivative of 2-aminofluorene; the heterocyclic aromatic amines 2-amino-3,8-dimethylimidazo[4,5-<i>f</i>]quinoxaline, 2-amino-1-methyl-6-phenylimidazo[4,5-<i>b</i>]pyridine, 2-amino-α-carboline, and 2-amino-3-methyl-α-carboline; and the polycyclic aromatic hydrocarbon benzo[<i>a</i>]pyrene. DNA adducts of <i>o</i>-toluidine and 2-naphthylamine were not detected. Most notably, 10-methoxy-6-nitrophenanthro[3,4-<i>d</i>]-1,3-dioxole-5-carboxylic acid (AA-III), an isomer of AA-I, which was later identified as a minor contaminant (5.3%) in the purified herbal extract assayed, exhibited strong organotropism for DNA damage within the urinary system. Dose-adjusted levels of dA and dG adducts of AA-III were 30 to 80 times higher than those of AA-I in the kidney and urinary bladder. This strikingly high organ-specific DNA adduct formation in the urinary system was unique to AA-III and was not observed for the structurally related 3-NBA and AA-I, or the other carcinogens tested. Given that AA-III frequently occurs with AA-I in <i>Aristolochia</i> herbs, these findings underscore the need for further research into the carcinogenic potential of AA-III and its role in renal and urinary bladder cancer associated with traditional herbal medicines.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 7","pages":"1239–1256"},"PeriodicalIF":3.8,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144300690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data.","authors":"Thalita Cirino, Luis Pinto, Mateusz Iwan, Alexis Dougha, Bono Lučić, Antonija Kraljević, Zaven Navoyan, Ani Tevosyan, Hrach Yeghiazaryan, Lusine Khondkaryan, Narek Abelyan, Vahe Atoyan, Nelly Babayan, Yuma Iwashita, Kyosuke Kimura, Tomoya Komasaka, Koki Shishido, Taichi Nakamura, Mizuho Asada, Sankalp Jain, Alexey V Zakharov, Haobo Wang, Wenjia Liu, Vladimir Chupakhin, Yoshihiro Uesawa","doi":"10.1021/acs.chemrestox.5c00018","DOIUrl":"10.1021/acs.chemrestox.5c00018","url":null,"abstract":"<p><p>Transthyretin (TTR) is a key transporter of the thyroid hormone thyroxine, and chemicals that bind to TTR, displacing the hormone, can disrupt the endocrine system, even at low concentrations. This study evaluates computational modeling strategies developed during the Tox24 Challenge, using a data set of 1512 compounds tested for TTR binding affinity. Individual models from nine top-performing teams were analyzed for performance and uncertainty using regression metrics and applicability domains (AD). Consensus models were developed by averaging predictions across these models, with and without consideration of their ADs. While applying AD constraints in individual models generally improved external prediction accuracy (at the expense of reduced chemical space coverage), it had limited additional benefit for consensus models. Results showed that consensus models outperformed individual models, achieving a root-mean-square error (RMSE) of 19.8% on the test set, compared to an average RMSE of 20.9% for the nine individual models. Outliers consistently identified in several of these models indicate potential experimental artifacts and/or activity cliffs, requiring further investigation. Substructure importance analysis revealed that models prioritized different chemical features, and consensus averaging harmonized these divergent perspectives. These findings highlight the value of consensus modeling in improving predictive performance and addressing model limitations. Future work should focus on expanding chemical space coverage and refining experimental data sets to support public health protection.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"1061-1071"},"PeriodicalIF":3.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12175157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refining the Amino Reactivity-Based Identification of Respiratory Sensitizers.","authors":"Martin Simoneit, Helene Langer, Nadin Ulrich, Alexander Böhme","doi":"10.1021/acs.chemrestox.4c00545","DOIUrl":"10.1021/acs.chemrestox.4c00545","url":null,"abstract":"<p><p>The sensitization of the respiratory tract may lead to various pulmonary diseases such as asthma. It can be triggered by the chemical reaction of organic electrophiles with nucleophiles of lung proteins with amino groups being of particular interest in this case. For assessing the dermal sensitization potential of chemicals, the direct peptide reactivity assay (DPRA) has become an OECD-accepted nonanimal test system. However, issues with the identification of known respiratory sensitizers such as isocyanates and anhydrides based on their amino reactivity in the DPRA have been reported. Hence, in this study the chemoassay employing glycine-<i>para</i>-nitroanilide (Gly-pNA) as model nucleophile is applied to eight iso(thio)cyanates, seven anhydrides, four dinitrobenzenes, one triazine, five acrylates, glutaraldehyde, and chloramine T to quantify their amino reactivity in terms of the second order rate constant <i>k</i>_Gly and the DPRA-like 24 h percent depletion <i>D</i>_Gly. A comparison of <i>D</i>_Gly with respective DPRA amino reactivity data (<i>D</i>_DPRA) showed that in particular iso(thio)cyanates and anhydrides are substantially more reactive toward Gly-pNA. This can be rationalized by the unintentional and so far not considered reaction of the test compounds with the ammonium acetate buffer used for DPRA testing. A detailed analysis of this reaction includes half-lives and analytically determined adduct patterns and indicates that it can hamper the envisaged depletion of the DPRA amino nucleophile. Finally, the obtained log <i>k</i>_Gly values range from -3.73 to ≥ 4.52 and allow for an improved identification of respiratory sensitizers. Hence, the Gly-pNA chemoassay may serve as a nonanimal screening method as one part of a mechanism-informed integrated testing and assessment strategy for respiratory sensitizers.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"1046-1060"},"PeriodicalIF":3.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12175168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144172110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of the Protective Effects of Selenium against T-2 Toxin-Induced Toxicity.","authors":"Alexey A Tinkov, Anatoly V Skalny, Xiong Guo, Tatiana V Korobeinikova, Yujie Ning, Joao B T Rocha, Feng Zhang, Michael Aschner","doi":"10.1021/acs.chemrestox.5c00095","DOIUrl":"10.1021/acs.chemrestox.5c00095","url":null,"abstract":"<p><p>The objective of the present study was to review the potential protective effects of Se against T-2 toxin-induced adverse effects in cartilage and other tissues as well as to discuss the potential molecular mechanisms by which Se counteracts T-2 toxicity. Laboratory studies demonstrate that Se attenuates T-2 toxin-induced chondrocyte death by inhibition of the mitochondrial pathway of apoptosis. Protective effects of Se against T-2 toxin-induced oxidative stress in chondrocytes are mediated by improvement of antioxidant selenoprotein expression, which is altered upon mycotoxin exposure. In addition to T-2 toxin-induced oxidative stress, Se treatment is associated with the inhibition of mycotoxin-induced chondrocyte ferroptosis. Along with prevention of chondrocyte damage, Se improves extracellular matrix (ECM) metabolism by the up-regulation of type II collagen and proteoglycans expression and inhibition of T-2 toxin-induced ECM degradation by matrix metalloproteinases. It is also noteworthy that part of the interactive effects between Se treatment and T-2 toxin exposure is mediated by epigenetic mechanisms, especially modulation of noncoding RNA expression. Recent evidence also shows that Se mitigates the toxic effects of the T-2 toxin in the liver, kidney, immune system, and other organs. Notably, a number of studies demonstrated that a Se deficiency aggravates the adverse effects of T-2 toxin exposure, supporting the notion of the protective effects of Se. However, the existing data were obtained in laboratory in vivo and in vitro models, and the potential therapeutic effects of Se supplementation in T-2 toxin-exposed human subjects have yet to be fully characterized.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"975-996"},"PeriodicalIF":3.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144109096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"Lijuan You,&nbsp;Xiaohong Wang,&nbsp;Yuan Zhi,&nbsp;Huiling Wang,&nbsp;Zhisen Zhuang,&nbsp;Jing Yang,&nbsp;Qiannan Zhang,&nbsp;Hailin Shang,&nbsp;Yongning Li,&nbsp;Yi Wan,&nbsp;Xudong Jia and Hui Yang*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 6","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.5c00066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144422305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 6","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/txv038i006_1947265","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144422308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"Indu Sinha,&nbsp;Zachary Bitzer,&nbsp;Stephanie Barnett,&nbsp;Lisa Reinhart,&nbsp;Todd M. Umstead,&nbsp;Zissis C. Chroneos,&nbsp;Matthew Lanza,&nbsp;Dongxiao Sun,&nbsp;Junjia Zhu,&nbsp;John P. Richie Jr. and Raghu Sinha*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 6","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.4c00525","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144422311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"Martin Simoneit,&nbsp;Helene Langer,&nbsp;Nadin Ulrich and Alexander Böhme*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 6","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.4c00545","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144422743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}