Chemical Research in Toxicology最新文献

{"title":"","authors":"Xiaolin Pan*, Yaowen Gu, Weijun Zhou and Yingkai Zhang*, ","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 5","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.4c00560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144450263","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":"Aljoša Smajić, Thomas Steger-Hartmann, Gerhard. F. Ecker and Anke Hackl*, ","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 5","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.4c00347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144450264","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":"Chemoproteomic Approach for Identifying Nuclear Arsenite-Binding Proteins.","authors":"Shiyuan Guo, Pengcheng Wang, Songbo Wei, Yinsheng Wang","doi":"10.1021/acs.chemrestox.5c00107","DOIUrl":"10.1021/acs.chemrestox.5c00107","url":null,"abstract":"<p><p>Trivalent arsenic, i.e., As(III), is the main form of arsenic species in the environment. Prolonged exposure to arsenicals through ingesting contaminated food and water has been implicated in the development of cancer and diabetes as well as cardiovascular and neurodegenerative diseases. A number of studies have been conducted to examine the mechanisms underlying the toxic effects of arsenite exposure, where As(III) was shown to displace Zn(II) and impair the functions of zinc-binding proteins. Considering that many zinc-binding proteins can bind to nucleic acids, we reason that systematic identification of arsenite-binding proteins in the nucleus may provide additional insights into the molecular targets of arsenite, thereby improving our understanding of the mechanisms of arsenic toxicity. Here, we conducted a quantitative proteomics experiment relying on affinity pull-down from nuclear protein lysate with a biotin-As(III) probe to identify nuclear arsenite-binding proteins. We uncovered a number of candidate As(III)-binding proteins that are involved in mRNA splicing, DNA repair, and replication. We also found that As(III) could bind to splicing factor 1 (SF1) and that this binding perturbs mRNA splicing in human cells. Together, our work provided insights into the mechanisms of As(III) toxicity by revealing new nuclear protein targets of As(III).</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"954-961"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143952365","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 5","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/txv038i005_1936303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144450254","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":"Garrit Clabaugh,&nbsp; and ,&nbsp;Yinsheng Wang*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 5","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.4c00547","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144450261","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":"Enhancing Transthyretin Binding Affinity Prediction with a Consensus Model: Insights from the Tox24 Challenge.","authors":"Xiaolin Pan, Yaowen Gu, Weijun Zhou, Yingkai Zhang","doi":"10.1021/acs.chemrestox.4c00560","DOIUrl":"10.1021/acs.chemrestox.4c00560","url":null,"abstract":"<p><p>Transthyretin (TTR) plays a vital role in thyroid hormone transport and homeostasis in both the blood and target tissues. Interactions between exogenous compounds and TTR can disrupt the function of the endocrine system, potentially causing toxicity. In the Tox24 challenge, we leveraged the data set provided by the organizers to develop a deep learning-based consensus model, integrating sPhysNet, KANO, and GGAP-CPI for predicting TTR binding affinity. Each model utilized distinct levels of molecular information, including 2D topology, 3D geometry, and protein-ligand interactions. Our consensus model achieved favorable performance on the blind test set, yielding an RMSE of 20.8 and ranking fifth among all submissions. Following the release of the blind test set, we incorporated the leaderboard test set into our training data, further reducing the RMSE to 20.6 in an offlineretrospective study. These results demonstrate that combining three regression models across different modalities significantly enhances the predictive accuracy. Furthermore, we employ the standard deviation of the consensus model's ensemble outputs as an uncertainty estimate. Our analysis reveals that both the RMSE and interval error of predictions increase with rising uncertainty, indicating that the uncertainty can serve as a useful measure of prediction confidence. We believe that this consensus model can be a valuable resource for identifying potential TTR binders and predicting their binding affinity in silico. The source code for data preparation, model training, and prediction can be accessed at https://github.com/xiaolinpan/tox24_challenge_submission_yingkai_lab.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"900-908"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951350","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":"Identification and Structural Elucidation of a Novel Pyrrolidinophenone-Based Designer Drug on the Illicit Market: α-BPVP.","authors":"Sara Casati, Roberta F Bergamaschi, Riccardo Primavera, Alessandro Ravelli, Ivana Lavota, Alessio Battistini, Gabriella Roda, Chiara Ciccarelli, Claudio Guidotti, Paola Rota","doi":"10.1021/acs.chemrestox.5c00068","DOIUrl":"10.1021/acs.chemrestox.5c00068","url":null,"abstract":"<p><p>The identification of a new psychoactive substances (NPS) with a cathinone structure and a biphenyl substituent, found in seized powder from the black market, is here reported. By combining analytical techniques, including 1D and 2D NMR and HRMS, the compound was identified as 1-([1,1'-biphenyl]-4-yl)-2-(pyrrolidin-1-yl)pentan-1-one (α-BPVP), an α-pyrrolidinopentiophenone (α-PVP) analogue featuring a biphenyl group instead of the phenyl ring. This previously unreported molecule raises urgent legal and public health concerns, which warrants further toxicological investigation.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"808-811"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143954926","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":"Nadia I. Georgieva,&nbsp;Eric R. Siegel,&nbsp;Radim J. Šrám,&nbsp;Pamela M. Vacek,&nbsp;Vernon E. Walker,&nbsp;Richard J. Albertini,&nbsp;James A. Swenberg and Gunnar Boysen*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 5","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acs.chemrestox.4c00530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144450257","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":"Sugars in Tobacco Products: Toxicity Research and Implications for Tobacco Product Regulation.","authors":"Irina Stepanov, Micah Berman, Marielle C Brinkman, Alex Carll, Vernat Exil, Eleanore G Hansen, Ahmad El Hellani, Sairam V Jabba, Nada O F Kassem, Mary Rezk-Hanna, Reinskje Talhout, Andrea M Stroup","doi":"10.1021/acs.chemrestox.4c00550","DOIUrl":"10.1021/acs.chemrestox.4c00550","url":null,"abstract":"<p><p>Sugars are naturally present in tobacco plants and are introduced as additives during the manufacturing of various tobacco- and nicotine-containing products. Product palatability and appeal are the primary reasons for manufacturers' attention to the sugar content in tobacco and nicotine products. However, because of the complex chemistry of sugars and their thermal decomposition, these versatile constituents are also contributing to the toxicity profile of tobacco and nicotine products. Using published empirical data, this non-systematic review summarizes the state of knowledge on the toxicologically relevant chemical transformations of sugars and artificial sweeteners in tobacco and nicotine products, including waterpipe tobacco, combustible and electronic cigarettes, heated tobacco products, and smokeless tobacco, and available research on the associated health effects of sugar-derived toxicants. Implications of sugar and sweetener content for abuse liability of various tobacco products are also discussed. Based on the findings of this review, research gaps are identified and policy recommendations are made for regulating sugars and artificial sweeteners in tobacco and nicotine products, including adding sugars and artificial sweeteners to the list of harmful and potentially harmful constituents (HPHCs).</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"747-758"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951569","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":"Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.","authors":"Srijit Seal, Manas Mahale, Miguel García-Ortegón, Chaitanya K Joshi, Layla Hosseini-Gerami, Alex Beatson, Matthew Greenig, Mrinal Shekhar, Arijit Patra, Caroline Weis, Arash Mehrjou, Adrien Badré, Brianna Paisley, Rhiannon Lowe, Shantanu Singh, Falgun Shah, Bjarki Johannesson, Dominic Williams, David Rouquie, Djork-Arné Clevert, Patrick Schwab, Nicola Richmond, Christos A Nicolaou, Raymond J Gonzalez, Russell Naven, Carolin Schramm, Lewis R Vidler, Kamel Mansouri, W Patrick Walters, Deidre Dalmas Wilk, Ola Spjuth, Anne E Carpenter, Andreas Bender","doi":"10.1021/acs.chemrestox.5c00033","DOIUrl":"10.1021/acs.chemrestox.5c00033","url":null,"abstract":"<p><p>Machine learning (ML) is increasingly valuable for predicting molecular properties and toxicity in drug discovery. However, toxicity-related end points have always been challenging to evaluate experimentally with respect to <i>in vivo</i> translation due to the required resources for human and animal studies; this has impacted data availability in the field. ML can augment or even potentially replace traditional experimental processes depending on the project phase and specific goals of the prediction. For instance, models can be used to select promising compounds for on-target effects or to deselect those with undesirable characteristics (e.g., off-target or ineffective due to unfavorable pharmacokinetics). However, reliance on ML is not without risks, due to biases stemming from nonrepresentative training data, incompatible choice of algorithm to represent the underlying data, or poor model building and validation approaches. This might lead to inaccurate predictions, misinterpretation of the confidence in ML predictions, and ultimately suboptimal decision-making. Hence, understanding the predictive validity of ML models is of utmost importance to enable faster drug development timelines while improving the quality of decisions. This perspective emphasizes the need to enhance the understanding and application of machine learning models in drug discovery, focusing on well-defined data sets for toxicity prediction based on small molecule structures. We focus on five crucial pillars for success with ML-driven molecular property and toxicity prediction: (1) data set selection, (2) structural representations, (3) model algorithm, (4) model validation, and (5) translation of predictions to decision-making. Understanding these key pillars will foster collaboration and coordination between ML researchers and toxicologists, which will help to advance drug discovery and development.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"759-807"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093382/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143955686","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}