Chemical Research in Toxicology最新文献

{"title":"","authors":"Gui-zhi Han, Shuang Li, Yuan-yuan Cui, Bo Shao, Ye Song, Shun-li Jiang* and Zhao-qiang Zhang*, ","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, Rong Tan, Hui Yang, Bowen Gong, Xu Wang, Shiyu Zhang, Dandan Yang, Tingmin Ye, Zixia Hu, Ying Peng*, Weiwei Li* and Jiang Zheng*, ","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":"","authors":"Medjda Bellamri, Scott J. Walmsley, Lihua Yao, Thomas A. Rosenquist, Christopher J. Weight, Peter W. Villalta and Robert J. Turesky*, ","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 In vitro Mutagenicity Using Multi-Task Deep Learning and REACH Data","authors":"Panagiotis G. Karamertzanis*,&nbsp;Mike Rasenberg,&nbsp;Imran Shah and Grace Patlewicz,&nbsp;","doi":"10.1021/acs.chemrestox.5c00152","DOIUrl":"10.1021/acs.chemrestox.5c00152","url":null,"abstract":"<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":"38 8","pages":"1382–1407"},"PeriodicalIF":3.8,"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}

{"title":"Circular RNAs: A Potential Regulator in Environmental Chemical Carcinogenesis","authors":"Huijun Huang,&nbsp;Yiqi Zhou,&nbsp;Yiyan Huang,&nbsp;Jiaxin Wang,&nbsp;Shiyi Ouyang,&nbsp;Meiqi Lan,&nbsp;Lieyang Fan* and Yun Zhou*,&nbsp;","doi":"10.1021/acs.chemrestox.5c00146","DOIUrl":"10.1021/acs.chemrestox.5c00146","url":null,"abstract":"<p >Circular RNA (circRNA), a class of evolutionarily conserved, structurally stable, and tissue-specifically expressed noncoding RNA, is increasingly recognized as a key regulator of diverse biological processes and disease pathogenesis, including cancer. While the roles of circRNAs in tumorigenesis are well-documented, their involvement in the tumorigenesis induced by environmental chemical carcinogens (ECCs) remains relatively unexplored. Recent studies have identified aberrant expressions of specific circRNAs during ECC exposure-related carcinogenesis, suggesting their critical regulatory functions. Given their unique structure and broad regulatory roles, circRNAs exhibit great potential as diagnostic, therapeutic, and prognostic biomarkers for ECC exposure-associated cancers. This review summarizes the characteristics and functions of circRNAs, as well as the potential regulatory mechanisms in ECC exposure-induced cancer and the dysregulations of circRNAs caused by ECCs. We highlight the complexity and heterogeneity of circRNA regulatory networks, emphasizing the need for integrated and dynamic investigations to fully elucidate the underlying mechanisms. Future research efforts should prioritize biomarker studies to facilitate the prevention, early detection, and effective treatment of ECC exposure-associated cancers.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 8","pages":"1309–1324"},"PeriodicalIF":3.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657853","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":"ROS-Dependent Endoplasmic Reticulum Stress Is Involved in Silica-Induced Pulmonary Fibrosis through the GRP78/CHOP/TXNIP/NLRP3 Signaling Pathway in Rats","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":"10.1021/acs.chemrestox.5c00135","DOIUrl":"10.1021/acs.chemrestox.5c00135","url":null,"abstract":"<p >Several studies have suggested that silica-induced reactive oxygen species (ROS) stimulate the endoplasmic reticulum to undergo endoplasmic reticulum stress (ERS), which eventually leads to pulmonary fibrosis. However, the mechanisms by which ROS-dependent ERS leads to silicosis and fibrosis remain unclear. In this study, male rats were intratracheally instilled with a single dose of crystalline silica (SiO2) suspension (100 mg/mL, 1 mL) to establish silicosis and then were injected intravenously with 1 mL of N-Acetylcysteine (NAC) (at the dose of 20, 40, or 80 mg/kg, respectively) daily to inhibit ROS-dependent ERS. Rats given a single intratracheal dose of SiO₂ suspension and subsequently receiving daily intravenous injections of phosphate buffer solution (PBS) served as models, while those given a single intratracheal dose of PBS and subsequently receiving daily intravenous injections of PBS served as controls. After 40 days, lung samples were taken for pathological observation, and the levels of glucose-regulated protein 78(GRP78), CCAAT-enhancer-binding protein homologous protein (CHOP), thioredoxin-interacting protein (TXNIP), and nucleotide-binding oligomerization domain (NOD)-like receptor family pyrin domain containing 3 inflammasome (NLRP3 inflammasome) were assessed. The results showed that compared with the control group, the lung tissues of the model rats exhibited obvious fibrosis and ERS, accompanied by the elevated levels of GRP78, CHOP, TXNIP, and NLRP3 inflammasome. After ROS were inhibited with NAC, the degree of lung fibrosis and ERS was significantly alleviated, and the levels of the aforementioned cytokines were also reduced. Moreover, the higher the dose of NAC intervention, the more pronounced the effects. The results demonstrated that ROS-dependent ERS is deeply involved in silica-induced pulmonary fibrosis through the GRP78/CHOP/TXNIP/NLRP3 signaling pathway in rats.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 7","pages":"1257–1265"},"PeriodicalIF":3.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598907","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":"Role of Human Aldo-Keto Reductases and Nuclear Factor Erythroid 2-Related Factor 2 in the Metabolic Activation of 1,8-Dinitropyrene and Its Metabolite 1-Amino-8-nitropyrene via Nitroreduction in Human Lung Cells","authors":"Anthony L. Su,&nbsp;Cátia F. Marques,&nbsp;Jacek Krzeminski,&nbsp;Karam El-Bayoumy and Trevor M. Penning*,&nbsp;","doi":"10.1021/acs.chemrestox.5c00101","DOIUrl":"10.1021/acs.chemrestox.5c00101","url":null,"abstract":"<p >1,8-Dinitropyrene (1,8-DNP) is a diesel exhaust constituent classified as a possible human carcinogen (Group 2B) by the International Agency for Research on Cancer. Its mutagenic properties can be attributed in part through the formation of covalent DNA adducts that result from mononitroreduction (e.g., <i>N</i>-(deoxyguanosin-8-yl)-1-amino-8-nitropyrene). Recombinant aldo-keto reductases (AKRs) 1C1−1C3 catalyze the nitroreduction of 1,8-DNP, 1-nitropyrene, and 3-nitrobenzanthrone. Although <i>AKR1C1−1C3</i> are induced by nuclear factor erythroid 2-related factor 2 (NRF2), the contribution of NRF2 toward the nitroreduction of 1,8-DNP in human lung cells is currently unknown. We used highly sensitive and specific in-cell fluorescence assays to examine the ability of human lung A549 and HBEC3-KT cells to metabolize 1,8-DNP to yield 1-amino-8-nitropyrene (1,8-ANP) and 1,8-DNP to yield 1,8-diaminopyrene (1,8-DAP) via mono- and bis-nitroreduction, respectively. A549 cells generated both 1,8-ANP and 1,8-DAP from 1,8-DNP. By contrast, HBEC3-KT cells formed 1,8-ANP, but essentially no 1,8-DAP, from 1,8-DNP. We used genetic and pharmacological approaches to investigate the dependence of 1,8-DNP nitroreduction on AKR1C1−1C3 and NRF2. A549 cells with homozygous <i>NFE2L2</i>/NRF2 knockout did not exhibit decreased 1,8-ANP formation but showed decreased 1,8-DAP formation, indicating that the second but not the first nitroreduction step was NRF2-dependent. Treatment of HBEC3-KT cells with NRF2 activators (<i>R</i>-sulforaphane (SFN) or 1-(2-cyano-3,12,28-trioxooleana-1,9(11)-dien-28-yl)-1<i>H</i>-imidazole (CDDO-Im) did not increase the mononitroreduction of 1,8-DNP to 1,8-ANP but increased the conversion of 1,8-ANP to 1,8-DAP consistent with the second step requiring inducible NRF2. AKR1C isoform specific inhibitors showed that these enzymes accounted for the majority of 1,8-ANP and 1,8-DAP formation in both cell lines. The ability of A549 <i>NFE2L2/</i>NRF2 knockout cells to still form 1,8-ANP coupled with their lack of AKR1C isoform expression indicated that a new nitroreductase was expressed as an adaptive response to NRF2 loss. We find that this nitroreductase is not NQO1, thioredoxin reductase, xanthine oxidase, or NADPH-P450 oxidoreductase.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 7","pages":"1227–1238"},"PeriodicalIF":3.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598906","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 In Silico-Generated Input Parameters","authors":"Hiroshi Yamazaki*,&nbsp; and ,&nbsp;Makiko Shimizu,&nbsp;","doi":"10.1021/acs.chemrestox.5c00157","DOIUrl":"10.1021/acs.chemrestox.5c00157","url":null,"abstract":"<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":"38 7","pages":"1157–1166"},"PeriodicalIF":3.8,"publicationDate":"2025-07-08","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":"Phthalate Exposure: Prevalence, Health Effects, Regulatory Frameworks, and Remediation","authors":"Abdulkhalik Mansuri,&nbsp;Charvi Trivedi,&nbsp;Shaivee Chokshi,&nbsp;Keya Jantrania and Ashutosh Kumar*,&nbsp;","doi":"10.1021/acs.chemrestox.4c00338","DOIUrl":"10.1021/acs.chemrestox.4c00338","url":null,"abstract":"<p >Phthalates, a ubiquitous class of plasticizers, are widely used to enhance the flexibility and durability of plastics. However, their noncovalent association with polymer matrices allows them to leach readily into the environment, raising significant global concerns. This review provides a comprehensive analysis of phthalates, including their chemical structures, properties, and applications, alongside their environmental prevalence and potential health risks. Particular emphasis is placed on the mechanisms of phthalate toxicity, including endocrine disruption, oxidative stress, and epigenetic modifications, with a critical discussion on how these mechanisms contribute to observed health outcomes. The bioaccumulation of phthalates in diverse environments is discussed, highlighting their presence in soil, water, and air. Advanced analytical techniques for phthalate detection are reviewed, with a focus on their strengths and limitations, and the need for more sensitive and accurate methods to monitor environmental contamination is underscored. Epidemiological and laboratory studies are critically examined to provide a detailed understanding of the developmental, reproductive, and systemic health effects associated with phthalate exposure. This review goes beyond summarizing existing knowledge by integrating discussions on regulatory frameworks, current challenges, and future directions for reducing environmental and health risks posed by phthalates. By addressing gaps in recent literature and consolidating diverse findings, this work aims to serve as a valuable resource for researchers and policymakers engaged in mitigating the impacts of phthalates on living organisms and ecosystems.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 8","pages":"1291–1308"},"PeriodicalIF":3.8,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574455","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":"Addressing Pitfalls of Metabolomics for Toxicology: A Call for Standardization, Reproducibility and Data Sharing","authors":"Min Nian,&nbsp;Xing Chen and Mingliang Fang*,&nbsp;","doi":"10.1021/acs.chemrestox.4c00555","DOIUrl":"10.1021/acs.chemrestox.4c00555","url":null,"abstract":"<p >Metabolomics has emerged as a pivotal tool in toxicology, providing unique insights into biochemical and molecular disruptions upon toxicant exposure. However, its application faces challenges such as metabolite misannotation, insufficient quality assurance and quality control (QA/QC), and limitations in dose–response and time-response studies. Pathway enrichment analysis is often hindered by incomplete databases and irrelevant background metabolites, leading to false positives or missed key pathways, while the lack of robust validation mechanisms can blur distinctions between general stress responses and toxicant-specific mechanisms. Addressing these pitfalls requires standardized protocols for sample preparation, analytical workflows, and data processing to ensure reproducibility. Rigorous QA/QC practices are essential to minimize batch effects, while cross-validation with transcriptomics and proteomics strengthens mechanistic insights. Comprehensive data sharing through public repositories enhances transparency and supports secondary analysis for novel discoveries. By adopting these strategies, metabolomics can achieve greater reliability and advance toxicological research by identifying early biomarkers, elucidating toxicant mechanisms, and improving environmental health assessments.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 7","pages":"1150–1156"},"PeriodicalIF":3.8,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574454","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}