{"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}
Sara Casati, Roberta F Bergamaschi, Riccardo Primavera, Alessandro Ravelli, Ivana Lavota, Alessio Battistini, Gabriella Roda, Chiara Ciccarelli, Claudio Guidotti, Paola Rota
{"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}
Nadia I. Georgieva, Eric R. Siegel, Radim J. Šrám, Pamela M. Vacek, Vernon E. Walker, Richard J. Albertini, James A. Swenberg and Gunnar Boysen*,
{"title":"","authors":"Nadia I. Georgieva, Eric R. Siegel, Radim J. Šrám, Pamela M. Vacek, Vernon E. Walker, Richard J. Albertini, James A. Swenberg and Gunnar Boysen*, ","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}
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
{"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}
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
{"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}
{"title":"","authors":"Hillul Chutia, Gori Sankar Borah, Hridoy Jyoti Mahanta and Selvaraman Nagamani*, ","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.4c00532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144450250","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":"Quantification of Flavors, Volatile Organic Compounds, Tobacco Markers, and Tobacco-Specific Nitrosamines in Heated Tobacco Products and Their Mainstream Aerosol.","authors":"Saria Hoshino, Kazushi Noro, Takashi Amagai","doi":"10.1021/acs.chemrestox.5c00005","DOIUrl":"10.1021/acs.chemrestox.5c00005","url":null,"abstract":"<p><p>As an alternative to cigarettes, the sales of heated tobacco products (HTPs) have increased in the Japanese market. This may contribute to improving a smoker's health because the levels of most toxic compounds─such as tobacco-specific nitrosamines (TSNAs) and volatile organic compounds (VOCs)─in the mainstream of HTPs are lower than those in cigarettes. However, the risks associated with the flavors that provide attractive tastes to HTPs remain unknown. We demonstrated that compared with cigarettes, HTPs reduce the health risks associated with VOCs and TSNAs while achieving comparable nicotine and flavor levels. The VOC and TSNA concentrations in the mainstream aerosol of HTPs were 0.0039 (benzene)-0.53 (acetaldehyde) times lower than those in cigarettes. Using HTPs may still pose adverse noncarcinogenic and carcinogenic effects on human health, as indicated by hazard quotients >1 for acrolein and acetaldehyde, margins of exposure <100 for (<i>R</i>)-(+)-limonene, and cancer risks >1.0 × 10<sup>-6</sup> for acetaldehyde. Additionally, the exhalation of mainstream aerosol may increase the indoor acrolein concentration to 0.069 μg m<sup>-3</sup>, exceeding the reference concentration for acrolein (0.02 μg m<sup>-3</sup>). Therefore, reducing acrolein concentrations is an effective measure for improving the safety of HTP use.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"915-922"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668581","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":"Nuclear SUMOylation and Proteotoxic Stress Responses to Metals with Different Ligand Preferences.","authors":"Giorgiana Madalina Ursu, Casey Krawic, Anatoly Zhitkovich","doi":"10.1021/acs.chemrestox.5c00040","DOIUrl":"10.1021/acs.chemrestox.5c00040","url":null,"abstract":"<p><p>Proteins are vulnerable to damage by a broad range of electrophiles, and cells contain several proteotoxic stress-monitoring systems. Main transcriptional responses to protein damage are driven by cytosolic HSF1 and NRF2 using soft nucleophile Cys-SH as sensors of electrophiles. It is unclear what stress responses are activated by poorly SH-reactive hard electrophiles. We examined protein damage responses in normal human lung cells with equitoxic doses of three carcinogenic metals with different electrophilic softness: soft, cadmium(II), intermediate, cobalt(II), and hard, chromium(III) delivered into cells using chromium(VI)/chromate. Cd(II) strongly activated cytosolic NRF2 and HSF1, produced soluble and insoluble polyubiquitinated proteins in the cytosol, and moderately elevated ER and mitochondrial unfolded protein responses and nuclear polySUMOylation. Cr(III) primarily induced nuclear protein damage and polySUMOylation and was negative for the activation of all cytoplasmic stress responses. Co(II) triggered HSF1, NRF2, and other responses seen with both Cr(III) and Cd(II) except for cytosolic polyubiquitin aggregates. Physiological levels of the antioxidant ascorbate inhibited but did not eliminate NRF2 activation by Co(II) and enhanced polySUMOylation by Cr(VI/III). For all three metals, SUMOylated proteins accumulated in nuclear PML bodies, and their formation was suppressed by PML knockdown. Inhibition of SUMOylation decreased transcription and, even more severely, protein expression of NRF2 and HSF1 targets by Cd(II) and Co(II), revealing the importance of this nuclear response in the functionality of cytosolic stress-activated pathways. Our findings demonstrate that soft and hard metal electrophiles elicit distinct proteotoxic stress responses, with the notable inability of the hard electrophile Cr(III) to trigger cytosolic damage-monitoring systems.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"942-953"},"PeriodicalIF":3.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12308312/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951557","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":"HDAC6-Mediated NLRP3 Inflammasome Activation Is Involved in Nickel Nanoparticle-Induced Pulmonary Inflammation and Fibrosis.","authors":"Yiqun Mo, Jisheng Nie, Yue Zhang, Yuanbao Zhang, Jiali Yuan, Qunwei Zhang","doi":"10.1021/acs.chemrestox.4c00551","DOIUrl":"10.1021/acs.chemrestox.4c00551","url":null,"abstract":"<p><p>Nickel nanoparticles (Nano-Ni) are increasingly utilized in industrial and biomedical applications, drawing growing attention to their potential adverse health effects. Our previous studies have demonstrated that Nano-Ni exposure induces severe, widespread, and persistent pulmonary inflammation and fibrosis. However, the underlying mechanisms are still unclear. The NLRP3 inflammasome is a vital component of the innate immune system and inflammatory signaling. In this study, we investigated whether Nano-Ni exposure activated the NLRP3 inflammasome and also examined its role in Nano-Ni-induced pulmonary inflammation and fibrosis. Our findings demonstrated that intratracheal instillation of wild-type mice (C57BL/6J) with 50 μg Nano-Ni per mouse resulted in NLRP3 inflammasome activation, IL-1β production, and extensive pulmonary inflammation and fibrosis. In contrast, Nano-Ni exposure induced only mild pulmonary inflammation and fibrosis in <i>Nlrp3</i><sup>-/-</sup> mice (lacking functional NLRP3 inflammasome) or <i>Il-1r1</i><sup>-/-</sup> mice (unresponsive to IL-1), highlighting the critical role of NLRP3 inflammasome activation in Nano-Ni-induced pulmonary damage. Further investigations using mouse alveolar macrophages (MH-S) revealed that Nano-Ni acts as a secondary activation signal for the NLRP3 inflammasome, triggering its activation in LPS-primed but not unprimed cells. Moreover, siRNA-mediated knockdown experiments demonstrated that this activation depended on Nano-Ni-induced upregulation of HDAC6. These findings suggest that Nano-Ni activates the NLRP3 inflammasome via HDAC6 as a second activation signal, leading to IL-1β production and subsequent pulmonary inflammation and fibrosis.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":"877-891"},"PeriodicalIF":3.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143955888","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}