Chemical Research in Toxicology最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 7","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/txv038i007_1961694","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665211","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":"Acetic Acid Inhalation-Induced Lung Injury: A Common Chemical with Underestimated Risks.","authors":"Puthiyavalappil Rasin, Praveena Prabhakaran","doi":"10.1021/acs.chemrestox.5c00226","DOIUrl":"10.1021/acs.chemrestox.5c00226","url":null,"abstract":"<p><p>Acetic acid is widely used; however, its inhalation can cause significant respiratory harm. This paper examines its toxicological mechanisms, overlooked health risks, and the need for targeted safety measures to prevent lung injury in both domestic and occupational places.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"1147-1149"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144551433","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":"Shaowei Wang,&nbsp;Xianghong Fu,&nbsp;Xiya Ren,&nbsp;Peng Yi,&nbsp;Zhigang Wu*,&nbsp;Ren-shan Ge* and Bo Peng*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 7","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.5c00156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665213","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":"Gui-zhi Han,&nbsp;Shuang Li,&nbsp;Yuan-yuan Cui,&nbsp;Bo Shao,&nbsp;Ye Song,&nbsp;Shun-li Jiang* and Zhao-qiang Zhang*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 7","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.5c00135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665216","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":"Yunfang Jiang,&nbsp;Rong Tan,&nbsp;Hui Yang,&nbsp;Bowen Gong,&nbsp;Xu Wang,&nbsp;Shiyu Zhang,&nbsp;Dandan Yang,&nbsp;Tingmin Ye,&nbsp;Zixia Hu,&nbsp;Ying Peng*,&nbsp;Weiwei Li* and Jiang Zheng*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 7","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.5c00025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665218","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":"Toxicokinetics and Perfluorooctanesulfonic Acid-Induced Liver Protein Expression Are Markedly Altered in Mice Lacking Albumin.","authors":"Emily M Kaye, Jitka Becanova, Simon Vojta, Rainer Lohmann, Fabian Christoph Fischer, Angela Slitt","doi":"10.1021/acs.chemrestox.4c00508","DOIUrl":"10.1021/acs.chemrestox.4c00508","url":null,"abstract":"<p><p>Perfluorooctanesulfonic acid (PFOS) is a ubiquitous perfluoroalkyl substance (PFAS) linked to liver disease and obesity in humans. Binding studies suggest that albumin is a crucial blood protein influencing PFOS toxicokinetics and hepatotoxicity; however, its role has not been mechanistically tested in vivo. This study used an albumin-deficient mouse model to investigate the relevance of albumin in PFOS tissue distribution and liver disease end points. Adult male C57BL/6J wild-type (Alb^+/+) and albumin-deficient (Alb^-/-) mice were orally gavaged daily for 7 days with either vehicle or PFOS at 0.5 or 10 mg/kg body weight. The measured PFOS concentrations in plasma were significantly lower in Alb^-/- mice compared to those in Alb^+/+ mice, while liver concentrations were significantly higher in Alb^-/- mice. Binding experiments confirmed these findings, indicating that PFOS toxicokinetics are driven by plasma and tissue binding. Significant changes in liver protein expression did not translate into differences in liver disease end points between genotypes, suggesting the need for chronic exposure studies. Our data imply that disease-related albumin deficiency in humans can influence PFAS toxicokinetics and susceptibility to hepatotoxicity. Our framework using knockout mice can be adapted in future studies to assess the relevance of protein binding and membrane transporters in PFAS distribution and elimination.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"1183-1191"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256637","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":"AmesFormer: State-of-the-Art Mutagenicity Prediction with Graph Transformers.","authors":"Luke A Thompson, Josiah G Evans, Slade T Matthews","doi":"10.1021/acs.chemrestox.4c00466","DOIUrl":"10.1021/acs.chemrestox.4c00466","url":null,"abstract":"<p><p>The Ames mutagenicity test is a gold standard assay for the safety assessment of new chemicals. However, many in silico models rely on challenging-to-interpret ensemble strategies and molecular fingerprint data, which neglects gestalt molecular structure. To improve upon these models, we propose AmesFormer, a graph transformer neural network that shows state-of-the-art performance when paired with our new Ames data set. We briefly review the current state of Ames modeling with a focus on graph neural networks. We then benchmark AmesFormer on a standardized test data set against 22 other Ames models, achieving state of the art (SOTA) performance. We uniquely report the calibration performance of our model and attempt to improve it using temperature scaling. We support our findings with reference to other models from the literature and with developments in machine learning (ML) and graph theory. Overall, we present a high-performance, accessible, and open-source computational model for Ames mutagenicity, with significant potential for regulatory and drug development applications.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"1167-1182"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144504049","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":"Prediction of Internal Exposures after Virtual Oral Doses of Disparate Chemicals in Rats and Humans Using Simplified Physiologically Based Pharmacokinetic Models with <i>In Silico-</i>Generated Input Parameters.","authors":"Hiroshi Yamazaki, Makiko Shimizu","doi":"10.1021/acs.chemrestox.5c00157","DOIUrl":"10.1021/acs.chemrestox.5c00157","url":null,"abstract":"<p><p>Toxicological evaluation of industrial chemicals with a broad range of chemical structures, for example, bioactive food components, toxic food-derived compounds, and drugs, usually involves the estimation of human clearance by allometric extrapolation of traditionally determined <i>in vivo</i> rat profiles. Three general methods are used to utilize and expand observed time-dependent plasma concentration data after single oral doses of chemicals: empirical standard noncompartmental analysis, compartmental modeling, and physiologically based pharmacokinetic (PBPK) modeling. Application of the PBPK model for forward dosimetry (from external to internal concentrations) following oral administrations has recently been simplified by using <i>in silico</i>-generated input parameters to evaluate internal exposures in humans without reference to any experimental data. Human PBPK model input parameters for a diverse range of compounds have been successfully estimated by using <i>in silico</i>-generated chemical descriptors and machine learning tools. Key values for the fraction absorbed × intestinal availability, the absorption constant, the volume of systemic circulation, and the hepatic intrinsic clearance can be generated <i>in silico</i> using mathematical equations to estimate values for sets of approximately 30 physicochemical properties or <i>in silico</i> descriptors. After virtual oral dosing of more than 350 compounds, the plasma and liver concentrations generated by PBPK models (1) using traditionally determined input parameters and (2) using input parameters estimated <i>in silico</i> were correlated in rat models and human models. This approach to pharmacokinetic modeling could potentially be applied in the clinical setting and during computational toxicological assessment of the potential risks of a wide range of general chemicals.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"1157-1166"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144590053","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":"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":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 7","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.5c00126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665223","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":"Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data.","authors":"Panagiotis G Karamertzanis, Mike Rasenberg, Imran Shah, Grace Patlewicz","doi":"10.1021/acs.chemrestox.5c00152","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.5c00152","url":null,"abstract":"<p><p>Under REACH, mutagenicity assessment relies on <i>in vitro</i> testing (gene mutation test in bacteria and/or mammalian cells, as well as chromosomal aberration or micronucleus assays in mammalian cells) followed by <i>in vivo</i> testing if necessary. This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. An extensive genotoxicity dataset with over 12,000 substances was compiled, including algorithmically curated REACH data and information from several public sources. Genotoxicity information was also retrieved from ToxValDB and literature sources to construct external (hold-out) test sets for a stringent assessment of the models' generalized performance. A range of single-task and multi-task models were investigated from classical machine learning techniques and chemical fingerprints to deep learning methods using graphs for molecular structure representation. The best deep learning single-task model achieved a cross-validation balanced accuracy of 73-84% for the four <i>in vitro</i> assays and exceeded classical machine learning by 2-8%. Gene mutation detection for specific bacterial strains and metabolic activation modes exhibited balanced accuracy 82-85%, with improvements ranging from 7% to 12%. Multi-task deep learning models for specific bacterial strains and metabolic activation modes had on average 8% higher cross-validation test balanced accuracy than single-task models but were comparable when assay outcomes were aggregated. The best deep learning models for specific bacterial strains and metabolic activation modes showed external balanced accuracy of 72-78 % when there were at least 200 positives and 200 negatives. The dimensionality-reduced molecular embeddings from graph neural network models were able to distinguish positives from negatives and cluster structures that trigger known genotoxicity structural alerts. The models were also used to identify structural moieties linked to predicted negative genotoxicity in bacteria and positive genotoxicity in mammalian cells.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663839","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}