Chemical Research in Toxicology最新文献

{"title":"Uncertainties in the Extrapolation of In Vitro Data in Human Risk Assessment: A Case Study of qIVIVE for Imazalil Using the Monte Carlo Risk Assessment Platform.","authors":"Anne Zwartsen, Marco Zeilmaker, Waldo J de Boer, Emiel Rorije, Hilko van der Voet","doi":"10.1021/acs.chemrestox.4c00287","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.4c00287","url":null,"abstract":"<p><p>New approach methodologies (NAMs) are promising for refining, reducing, and replacing animal experiments for hazard characterization. Quantitative in vitro-in vivo extrapolation (qIVIVE) is essential to extrapolate an in vitro-based point of departure to an in vitro-based human equivalent dose and subsequently to an in vitro-based health-based guidance or threshold value. The use of NAMs for hazard characterization leads to the need for various new extrapolations and linked uncertainties that preferably are quantified. Currently, qIVIVE is often performed without addressing these uncertainties. A clear description and, if possible, quantification of extrapolations and uncertainties when using NAMs for risk assessment will aid the regulatory implementation of NAMs for risk assessment. A case study of a qIVIVE-based assessment on the risk of liver steatosis from dietary exposure to imazalil is reported, using a human cell line in vitro test method as an example of a NAM to replace animal experiments. We consider the uncertainties related to the extrapolations from in vitro to in vivo effects, from in vitro nominal concentrations to in vitro intracellular concentrations, from in vitro concentrations to external doses (reverse dosimetry), from in vitro exposure durations to in vivo exposure situations, and from the average human to a sensitive individual. The case study addresses these uncertainties in a mainly quantitative approach, using available data and the Monte Carlo Risk Assessment platform.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074919","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":"The Role of Endoplasmic Reticulum Stress in Fine Particulate Matter-Induced Phenotype Switching of Vascular Smooth Muscle Cells.","authors":"Linyuan Huang, Ruiyang Ding, Kanglin Yan, Junchao Duan, Zhiwei Sun","doi":"10.1021/acs.chemrestox.5c00056","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.5c00056","url":null,"abstract":"<p><p>As a major component of air pollution, fine particulate matter (PM_2.5) was the second global leading cause of death in 2021. Evidence from humans suggested that PM_2.5 was associated with an enhanced coronary calcium score (CAC), and animal studies indicated that PM_2.5 induced vascular calcification, while mechanisms remained largely unknown. In this study, PM_2.5 enhanced the proliferative potential and migration capacity of human aortic vascular smooth muscle cells (VSMCs), as well as disturbing intracellular Ca²⁺ homeostasis. Subsequent transcriptomic analysis implicated that PM_2.5 could influence genes involved in the IRE1α-mediated unfolded protein responses and reduce the expression of DNAJB9, a co-chaperone that formed a complex with BiP/IRE1α to inhibit the activation of endoplasmic reticulum (ER) stress. Further mechanistic investigations indicated that PM_2.5 activated the IRE1α/XBP1 signaling pathway and enhanced the expression of osteogenic phenotype-related hallmarks. In contrast, pretreatment with an ER stress antagonist (4-PBA) could suppress PM_2.5-associated calcium dysregulation and osteogenic transformation via alleviation of ER stress. Taken together, this study revealed the role of ER stress in the phenotype switching of VSMCs induced by PM_2.5, highlighted the regulation of DNAJB9, provided insights into the mechanisms of air pollution-related vascular calcification, and pointed out molecules for future investigations.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074918","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":"Detection of Trace Skin Sensitizers Using Novel Nucleophiles with Quaternary Ammonium by Liquid Chromatography–Mass Spectrometry","authors":"Masataka Kitadani*,&nbsp;Yohei Shimizu,&nbsp;Akihiro Moriuchi and Ryo Koike,&nbsp;","doi":"10.1021/acs.chemrestox.4c0049710.1021/acs.chemrestox.4c00497","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.4c00497https://doi.org/10.1021/acs.chemrestox.4c00497","url":null,"abstract":"<p >Various methods for assessing the risk of skin sensitization have been developed to reduce the number of animal tests. Although sensitization can be induced by trace amounts of impurities in the raw materials, current test methods have limitations in detecting sensitization from impurities and in not identifying the cause. In this study, novel nucleophiles, <i>N</i>-(4-trimethylammoniobenzoyl)-2-sulfanylethylamine (TMAS) and <i>N</i>-(4-trimethylammoniobenzoyl)-6-aminohexylamine (TMAA), were synthesized for the highly sensitive detection of trace skin sensitizers using mass spectrometry. The reactivities of TMAS and TMAA were verified using several sensitizers. TMAS and TMAA were selectively reactive with sensitizers, and adducts were detected with a sensitivity of 10 μg/L in solution. Because a specific product ion (<i>m</i>/<i>z</i> 148.08) was detected in all adducts, selective detection by various tandem mass spectrometric analyses, such as selective reaction monitoring, is feasible. This method is effective for the detection and structural analysis of skin sensitizers present in raw materials.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 5","pages":"849–857 849–857"},"PeriodicalIF":3.7,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083531","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,&nbsp;Jianfeng Zhang,&nbsp;Songbin Dong and Bin Hu*,&nbsp;","doi":"10.1021/acs.chemrestox.5c0000410.1021/acs.chemrestox.5c00004","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.5c00004https://doi.org/10.1021/acs.chemrestox.5c00004","url":null,"abstract":"<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":"38 5","pages":"909–914 909–914"},"PeriodicalIF":3.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083599","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":"Identification and Structural Elucidation of a Novel Pyrrolidinophenone-Based Designer Drug on the Illicit Market: α-BPVP","authors":"Sara Casati,&nbsp;Roberta F. Bergamaschi,&nbsp;Riccardo Primavera,&nbsp;Alessandro Ravelli,&nbsp;Ivana Lavota,&nbsp;Alessio Battistini,&nbsp;Gabriella Roda,&nbsp;Chiara Ciccarelli,&nbsp;Claudio Guidotti and Paola Rota*,&nbsp;","doi":"10.1021/acs.chemrestox.5c0006810.1021/acs.chemrestox.5c00068","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.5c00068https://doi.org/10.1021/acs.chemrestox.5c00068","url":null,"abstract":"<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":"38 5","pages":"808–811 808–811"},"PeriodicalIF":3.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.chemrestox.5c00068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083600","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":"Bioactivation of the β-Amyloid Precursor Protein-Cleaving Enzyme 1 Inhibitor Atabecestat Leads to Protein Adduct Formation on Glutathione S-Transferase Pi","authors":"Megan Ford,&nbsp;Paul J. Thomson,&nbsp;Adam Lister,&nbsp;Jan Snoeys,&nbsp;Laurent Leclercq,&nbsp;Filip Cuyckens,&nbsp;Dean J. Naisbitt and Xiaoli Meng*,&nbsp;","doi":"10.1021/acs.chemrestox.5c0007010.1021/acs.chemrestox.5c00070","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.5c00070https://doi.org/10.1021/acs.chemrestox.5c00070","url":null,"abstract":"<p >Exposure to atabecestat is associated with liver injury, which subsequently led to its withdrawal from development. Previous studies of patients with atabecestat induced liver injury identified T cells responsive to atabecestat and its metabolites, indicating that immune-mediated mechanisms are involved. As irreversible protein modification is suspected to drive immunogenicity, this study aimed to characterize potential atabecestat protein adducts using HSA, GSTA1, and GSTP as model proteins. We have shown that atabecestat only formed a cysteine adduct on GSTP in the presence of metabolic systems, highlighting the important role of bioactivation in adduct formation and selectivity for the binding interaction.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":"38 5","pages":"812–815 812–815"},"PeriodicalIF":3.7,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.chemrestox.5c00070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083575","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*,&nbsp;Manas Mahale,&nbsp;Miguel García-Ortegón,&nbsp;Chaitanya K. Joshi,&nbsp;Layla Hosseini-Gerami,&nbsp;Alex Beatson,&nbsp;Matthew Greenig,&nbsp;Mrinal Shekhar,&nbsp;Arijit Patra,&nbsp;Caroline Weis,&nbsp;Arash Mehrjou,&nbsp;Adrien Badré,&nbsp;Brianna Paisley,&nbsp;Rhiannon Lowe,&nbsp;Shantanu Singh,&nbsp;Falgun Shah,&nbsp;Bjarki Johannesson,&nbsp;Dominic Williams,&nbsp;David Rouquie,&nbsp;Djork-Arné Clevert,&nbsp;Patrick Schwab,&nbsp;Nicola Richmond,&nbsp;Christos A. Nicolaou,&nbsp;Raymond J. Gonzalez,&nbsp;Russell Naven,&nbsp;Carolin Schramm,&nbsp;Lewis R Vidler,&nbsp;Kamel Mansouri,&nbsp;W. Patrick Walters,&nbsp;Deidre Dalmas Wilk,&nbsp;Ola Spjuth*,&nbsp;Anne E. Carpenter* and Andreas Bender*,&nbsp;","doi":"10.1021/acs.chemrestox.5c0003310.1021/acs.chemrestox.5c00033","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.5c00033https://doi.org/10.1021/acs.chemrestox.5c00033","url":null,"abstract":"<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":"38 5","pages":"759–807 759–807"},"PeriodicalIF":3.7,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.chemrestox.5c00033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083623","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,&nbsp;Jisheng Nie,&nbsp;Yue Zhang,&nbsp;Yuanbao Zhang,&nbsp;Jiali Yuan and Qunwei Zhang*,&nbsp;","doi":"10.1021/acs.chemrestox.4c0055110.1021/acs.chemrestox.4c00551","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.4c00551https://doi.org/10.1021/acs.chemrestox.4c00551","url":null,"abstract":"<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>^–/– mice (lacking functional NLRP3 inflammasome) or <i>Il-1r1</i>^–/– 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":"38 5","pages":"877–891 877–891"},"PeriodicalIF":3.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083413","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,&nbsp;Pengcheng Wang,&nbsp;Songbo Wei and Yinsheng Wang*,&nbsp;","doi":"10.1021/acs.chemrestox.5c0010710.1021/acs.chemrestox.5c00107","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.5c00107https://doi.org/10.1021/acs.chemrestox.5c00107","url":null,"abstract":"<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":"38 5","pages":"954–961 954–961"},"PeriodicalIF":3.7,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083535","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":"Enhancing Transthyretin Binding Affinity Prediction with a Consensus Model: Insights from the Tox24 Challenge","authors":"Xiaolin Pan*,&nbsp;Yaowen Gu,&nbsp;Weijun Zhou and Yingkai Zhang*,&nbsp;","doi":"10.1021/acs.chemrestox.4c0056010.1021/acs.chemrestox.4c00560","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.4c00560https://doi.org/10.1021/acs.chemrestox.4c00560","url":null,"abstract":"<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":"38 5","pages":"900–908 900–908"},"PeriodicalIF":3.7,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.chemrestox.4c00560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083516","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}