Chemical Research in Toxicology最新文献

{"title":"Data Exploration for Target Predictions Using Proprietary and Publicly Available Data Sets.","authors":"Aljoša Smajić, Thomas Steger-Hartmann, Gerhard F Ecker, Anke Hackl","doi":"10.1021/acs.chemrestox.4c00347","DOIUrl":"10.1021/acs.chemrestox.4c00347","url":null,"abstract":"<p><p>When applying machine learning (ML) approaches for the prediction of bioactivity, it is common to collect data from different assays or sources and combine them into single data sets. However, depending on the data domains and sources from which these data are retrieved, bioactivity data for the same macromolecular target may show a high variance of values (looking at a single compound) and cover very different parts of the chemical space as well as the bioactivity range (looking at the whole data set). The effectiveness and applicability domain of the resulting prediction models may be strongly influenced by the sources from which their training data were retrieved. Therefore, we investigated the chemical space and active/inactive distribution of proprietary pharmaceutical data from Bayer AG and the publicly available ChEMBL database, and their impact when applied as training data for classification models. For this end, we applied two different sets of descriptors in combination with different ML algorithms. The results show substantial differences in chemical space between the two different data sources, leading to suboptimal prediction performance when models are applied to domains other than their training data. MCC values between -0.34 and 0.37 among all targets were retrieved, indicating suboptimal model performance when models trained on Bayer AG data were tested on ChEMBL data and vice versa. The mean Tanimoto similarity of the nearest neighbors between these two data sources indicated similarities for 31 targets equal to or less than 0.3. Interestingly, all applied methods to assess overlap of chemical space of the two data sources to predict the applicability of models beyond their training data sets did not correlate with observed performances. Finally, we applied different strategies for creating mixed training data sets based on both public and proprietary sources, using assay format (cell-based and cell-free) information and Tanimoto similarities.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"820-833"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143954633","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":"Imidazole-Based ALK5 Inhibitor Attenuates TGF-β/Smad-Mediated Hepatic Stellate Cell Activation and Hepatic Fibrogenesis.","authors":"Si-Qi Wang, Yu-Qing Meng, Yan-Ling Wu, Ji-Xing Nan, Cheng-Hua Jin, Li-Hua Lian","doi":"10.1021/acs.chemrestox.5c00036","DOIUrl":"10.1021/acs.chemrestox.5c00036","url":null,"abstract":"<p><p>Liver fibrosis resulting from severe liver damage is a major clinical problem for which effective pharmacological drugs and treatment strategies are lacking. TGF-β, a hallmark of liver fibrosis, has been shown to promote ALK5 phosphorylation in an activated state. Hence, the suppression of ALK5 signal transduction has emerged as a promising therapeutic strategy for the treatment of liver fibrosis. In this study, the imidazole derivative J-1149, which exhibited inhibitory activity against ALK5, was synthesized to exert antifibrotic effects, and the inhibition mechanisms were uncovered. Our findings suggested that J-1149 significantly attenuated HSC activation and liver fibrogenesis by acting on the TGF-β/Smad signaling pathway. Concurrently, the potential of J-1149 to impede the P2X7R/NLRP3 axis, curtail the infiltration of macrophages and neutrophils, and reduce liver fibrogenesis was also highlighted. These results demonstrated that J-1149 is a promising candidate for the treatment of liver fibrosis.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"930-941"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143952368","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":"Tale of Three <i>N</i>-Nitrosamines and the Variables Needed to Assess Their Carcinogenicity In Silico Incorporated into a Single Workflow.","authors":"Jakub Kostal, Adelina Voutchkova-Kostal","doi":"10.1021/acs.chemrestox.4c00482","DOIUrl":"10.1021/acs.chemrestox.4c00482","url":null,"abstract":"<p><p><i>N</i>-Nitrosamine impurities in pharmaceuticals present a considerable challenge for regulators and industry alike, where the absence of carcinogenic-potency studies has left a gap that must be adequately filled to protect public health. In the interim, this means balancing risk assessment with the necessity to continue research, development, and supply of pharmaceuticals. In the long term, we need a cost-effective solution that optimizes both. As if beholden to Newton's Third Law, every crisis breeds an opportunity of equal magnitude. Consequently, cross-industry consortia have been racing to find a solution by advancing our current science. Recent spotlight has been on in silico tools, as a fast and increasingly reliable alternative to in vivo and in vitro testing. Because <i>N</i>-nitrosamine bioactivation lends itself uniquely to quantum mechanics (QM) approaches, the integration of electronic-structure considerations has emerged as the dominant in silico approach. This signifies a considerable leap in predictive toxicology, which has, for much of its existence, relied on atomistic (quantitative) structure-activity relationships, i.e., (Q)SARs. Here we present a validation of an integrated docking-QM approach within the CADRE program and demonstrate its utility on three different impurities, <i>N</i>-nitroso-7-monomethylamino-6-deoxytetracycline, <i>N</i>-nitroso-dabigatran etexilate, and 1-methyl-4-nitrosopiperazine. We show that a combined in silico strategy, which considers bioavailability, transport, cytochrome P450 binding, and reactivity, can be leveraged to supplement the overly conservative Carcinogenic Potency Categorization Approach (CPCA) in setting the daily acceptable intake (AI) using defensible, highly mechanistic, and quantitative drivers of <i>N</i>-nitrosamine metabolism. To that end, we argue that while <i>N</i>-nitroso-7-monomethylamino-6-deoxytetracycline and 1-methyl-4-nitrosopiperazine are cohort-of-concern impurities, <i>N</i>-nitroso-dabigatran etexilate is not a potent carcinogen (TD₅₀ > 1.5 mg/kg/day), contrasting the CPCA-derived AI. Lastly, we discuss how the CADRE tool can be integrated with the broader landscape of QM methods and the CPCA into a single harmonized in silico strategy for carcinogenicity assessment of <i>N</i>-nitrosamine impurities.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"834-848"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951560","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":"Obituary for Robert P. Hanzlik (1943–2025)","authors":"John R. Cashman*,&nbsp; and ,&nbsp;Matthew A. Cerny,&nbsp;","doi":"10.1021/acs.chemrestox.5c0016810.1021/acs.chemrestox.5c00168","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.5c00168https://doi.org/10.1021/acs.chemrestox.5c00168","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 5","pages":"745–746 745–746"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083564","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":"Migration of Soluble Polymers in Human Saliva during Swabbing Characterized by Direct Electrospray Ionization Mass Spectrometry.","authors":"Qiaoyun Huang, Jianfeng Zhang, Songbin Dong, Bin Hu","doi":"10.1021/acs.chemrestox.5c00004","DOIUrl":"10.1021/acs.chemrestox.5c00004","url":null,"abstract":"<p><p>Medical swabs are commonly used in routine medical sampling and testing of human body fluids, such as saliva and sputum. Many medical swabs are made of plastic polymers. Exposure to plastic medical swabs containing many free soluble polymers and residual monomers could increase the potential health risk. Conventional analytical methods for assessing personal exposure to polymers usually require complex sample preparation and time-consuming analytical procedures. In this study, we established a direct electrospray ionization mass spectrometry method to investigate the occurrence and species of soluble polymers in different medical swabs. The migration of typical soluble polymers, i.e., PA6 and PEG, was found in medical swabs and human saliva after swabbing within seconds. The amounts of PA6 and PEG were found to be nanograms per swab. Trace polymer could rapidly reside in saliva within seconds (e.g., 3 s). The exposure level of polymer residual was evaluated during different swabbing times and saliva volumes, showing the concentration of soluble polymers in saliva at the ng/mL level. Despite the low concentration and low toxicity of soluble polymers in saliva, it is anticipated that this method could offer a convenient and simple way to evaluate polymer exposures rapidly. We also hope our findings will attract more attention to the health risks of ubiquitous plastic materials in daily life and propose an efficient strategy to eliminate saliva polymers.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"909-914"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143952261","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":"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":"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":"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}