Chemical Research in Toxicology最新文献

{"title":"Prediction of Cytochrome P450 Substrates Using the Explainable Multitask Deep Learning Models","authors":"Jiaojiao Fang,&nbsp;Yan Tang,&nbsp;Changda Gong,&nbsp;Zejun Huang,&nbsp;Yanjun Feng,&nbsp;Guixia Liu,&nbsp;Yun Tang and Weihua Li*,&nbsp;","doi":"10.1021/acs.chemrestox.4c0019910.1021/acs.chemrestox.4c00199","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.4c00199https://doi.org/10.1021/acs.chemrestox.4c00199","url":null,"abstract":"<p >Cytochromes P450 (P450s or CYPs) are the most important phase I metabolic enzymes in the human body and are responsible for metabolizing ∼75% of the clinically used drugs. P450-mediated metabolism is also closely associated with the formation of toxic metabolites and drug–drug interactions. Therefore, it is of high importance to predict if a compound is the substrate of a given P450 in the early stage of drug development. In this study, we built the multitask learning models to simultaneously predict the substrates of five major drug-metabolizing P450 enzymes, namely, CYP3A4, 2C9, 2C19, 2D6, and 1A2, based on the collected substrate data sets. Compared to the single-task model and conventional machine learning models, the multitask fingerprints and graph neural networks model achieved superior performance with the average AUC values of 90.8% on the test set. Notably, the multitask model demonstrated its good performance on the small amount of substrate data sets such as CYP1A2, 2C9, and 2C19. In addition, the Shapley additive explanation and the attention mechanism were used to reveal specific substructures associated with P450 substrates, which were further confirmed and complemented by the substructure mining tool and the literature.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234617","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":"Toward an Explainable Large Language Model for the Automatic Identification of the Drug-Induced Liver Injury Literature","authors":"Chunwei Ma*,&nbsp; and ,&nbsp;Russell D. Wolfinger,&nbsp;","doi":"10.1021/acs.chemrestox.4c0013410.1021/acs.chemrestox.4c00134","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.4c00134https://doi.org/10.1021/acs.chemrestox.4c00134","url":null,"abstract":"<p >Drug-induced liver injury (DILI) stands as a significant concern in drug safety, representing the primary cause of acute liver failure. Identifying the scientific literature related to DILI is crucial for monitoring, investigating, and conducting meta-analyses of drug safety issues. Given the intricate and often obscure nature of drug interactions, simple keyword searching can be insufficient for the exhaustive retrieval of the DILI-relevant literature. Manual curation of DILI-related publications demands pharmaceutical expertise and is susceptible to errors, severely limiting throughput. Despite numerous efforts utilizing cutting-edge natural language processing and deep learning techniques to automatically identify the DILI-related literature, their performance remains suboptimal for real-world applications in clinical research and regulatory contexts. In the past year, large language models (LLMs) such as ChatGPT and its open-source counterpart LLaMA have achieved groundbreaking progress in natural language understanding and question answering, paving the way for the automated, high-throughput identification of the DILI-related literature and subsequent analysis. Leveraging a large-scale public dataset comprising 14 203 training publications from the CAMDA 2022 literature AI challenge, we have developed what we believe to be the first LLM specialized in DILI analysis based on LLaMA-2. In comparison with other smaller language models such as BERT, GPT, and their variants, LLaMA-2 exhibits an enhanced out-of-fold accuracy of 97.19% and area under the ROC curve of 0.9947 using 3-fold cross-validation on the training set. Despite LLMs’ initial design for dialogue systems, our study illustrates their successful adaptation into accurate classifiers for automated identification of the DILI-related literature from vast collections of documents. This work is a step toward unleashing the potential of LLMs in the context of regulatory science and facilitating the regulatory review process.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234588","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":"Targeting Glioblastoma: Efficacy of Ruthenium-Based Drugs","authors":"Puthiyavalappil Rasin,&nbsp;Sravan Sangeeth Surendran,&nbsp;Karthik K S,&nbsp;Jebiti Haribabu and Anandaram Sreekanth*,&nbsp;","doi":"10.1021/acs.chemrestox.4c0018810.1021/acs.chemrestox.4c00188","DOIUrl":"https://doi.org/10.1021/acs.chemrestox.4c00188https://doi.org/10.1021/acs.chemrestox.4c00188","url":null,"abstract":"<p >Ruthenium compounds offer improved selectivity and fewer side effects compared to platinum-based drugs in glioblastoma treatment. Insights into their interactions with transferrin suggest targeted drug delivery, while photoactivated chemotherapy is a novel cytotoxic approach in tumor tissues.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234546","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":"Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human-Induced Pluripotent Stem-Cell-Derived Cardiomyocytes.","authors":"Han-Hsuan D Tsai, Lucie C Ford, Sarah D Burnett, Allison N Dickey, Fred A Wright, Weihsueh A Chiu, Ivan Rusyn","doi":"10.1021/acs.chemrestox.4c00193","DOIUrl":"10.1021/acs.chemrestox.4c00193","url":null,"abstract":"<p><p>Environmental chemicals may contribute to the global burden of cardiovascular disease, but experimental data are lacking to determine which substances pose the greatest risk. Human-induced pluripotent stem cell (iPSC)-derived cardiomyocytes are a high-throughput cardiotoxicity model that is widely used to test drugs and chemicals; however, most studies focus on exploring electro-physiological readouts. Gene expression data may provide additional molecular insights to be used for both mechanistic interpretation and dose-response analyses. Therefore, we hypothesized that both transcriptomic and functional data in human iPSC-derived cardiomyocytes may be used as a comprehensive screening tool to identify potential cardiotoxicity hazards and risks of the chemicals. To test this hypothesis, we performed concentration-response analysis of 464 chemicals from 12 classes, including both pharmaceuticals and nonpharmaceutical substances. Functional effects (beat frequency, QT prolongation, and asystole), cytotoxicity, and whole transcriptome response were evaluated. Points of departure were derived from phenotypic and transcriptomic data, and risk characterization was performed. Overall, 244 (53%) substances were active in at least one phenotype; as expected, pharmaceuticals with known cardiac liabilities were the most active. Positive chronotropy was the functional phenotype activated by the largest number of tested chemicals. No chemical class was particularly prone to pose a potential hazard to cardiomyocytes; a varying proportion (10-44%) of substances in each class had effects on cardiomyocytes. Transcriptomic data showed that 69 (15%) substances elicited significant gene expression changes; most perturbed pathways were highly relevant to known key characteristics of human cardiotoxicants. The bioactivity-to-exposure ratios showed that phenotypic- and transcriptomic-based POD led to similar results for risk characterization. Overall, our findings demonstrate how the integrative use of in vitro transcriptomic and phenotypic data from iPSC-derived cardiomyocytes not only offers a complementary approach for hazard and risk prioritization, but also enables mechanistic interpretation of the in vitro test results to increase confidence in decision-making.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141755642","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":"Women in Toxicology Special Issue.","authors":"Prof Sayuri Miyamoto, Prof Dean Naisbitt, Dr Annette Kraegeloh","doi":"10.1021/acs.chemrestox.4c00288","DOIUrl":"10.1021/acs.chemrestox.4c00288","url":null,"abstract":"","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998882","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":"Improved Detection of Drug-Induced Liver Injury by Integrating Predicted <i>In Vivo</i> and <i>In Vitro</i> Data.","authors":"Srijit Seal, Dominic Williams, Layla Hosseini-Gerami, Manas Mahale, Anne E Carpenter, Ola Spjuth, Andreas Bender","doi":"10.1021/acs.chemrestox.4c00015","DOIUrl":"10.1021/acs.chemrestox.4c00015","url":null,"abstract":"<p><p>Drug-induced liver injury (DILI) has been a significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. Over the last decade, the existing suite of <i>in vitro</i> proxy-DILI assays has generally improved at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing the <i>in silico</i> prediction of DILI because it allows for evaluating large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predict nine proxy-DILI labels and then use them as features in addition to chemical structural features to predict DILI. The features include <i>in vitro</i> (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, <i>in vivo</i> (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILI data set (composed of DILIst and DILIrank) and tested them on a held-out external test set of 223 compounds from the DILI data set. The best model, DILIPredictor, attained an AUC-PR of 0.79. This model enabled the detection of the top 25 toxic compounds (2.68 LR+, positive likelihood ratio) compared to models using only structural features (1.65 LR+ score). Using feature interpretation from DILIPredictor, we identified the chemical substructures causing DILI and differentiated cases of DILI caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as nontoxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity and the potential for mechanism evaluation. DILIPredictor required only chemical structures as input for prediction and is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11337212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561884","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":"Use of Structural Alerts for Reactive Metabolites in the Application SpotRM.","authors":"Alf Claesson","doi":"10.1021/acs.chemrestox.4c00205","DOIUrl":"10.1021/acs.chemrestox.4c00205","url":null,"abstract":"<p><p>Reactive metabolite (RM) formation is widely accepted as playing a crucial role in causing idiosyncratic adverse drug reactions (IADRs), where the liver is most affected. An important goal of drug design is to avoid selection of drug candidates giving rise to RMs and therefore risk causing problems later on involving IADRs. The simplest, initial approach is to avoid test structures that have substructures known or strongly suspected to be associated with IADRs. However, as is evident from the many case reports of IADRs, in most cases a clear association with any (bio)chemical mechanism is lacking, which makes it hard to establish any structure-toxicity relationship. Separate studies of RM formation, in vitro and in vivo, have led to likely evidence and to establishing many structural alerts (SAs) that can be used for fast selection/deselection of planned test compounds. As a background to a discussion of the concept, 25 kinase inhibitor drugs with known problems of hepatotoxicity were probed against a set of SAs contained in the application SpotRM. A clear majority of the probed drugs show liabilities as evident by being flagged by more than one of the fairly established types of SAs. At the same time, no clear SAs were found in three drugs, which is discussed in the broader context of usefulness and selection tactics of SAs in drug design.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141873539","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":"8-OxodGuo and Fapy•dG Mutagenicity in <i>Escherichia coli</i> Increases Significantly when They Are Part of a Tandem Lesion with 5-Formyl-2'-deoxyuridine.","authors":"Srijana Dasgupta, Shijun Gao, Haozhe Yang, Marc M Greenberg, Ashis K Basu","doi":"10.1021/acs.chemrestox.4c00231","DOIUrl":"10.1021/acs.chemrestox.4c00231","url":null,"abstract":"<p><p>Tandem lesions, which are defined by two or more contiguously damaged nucleotides, are a hallmark of ionizing radiation. Recently, tandem lesions containing 5-formyl-2'-deoxyuridine (5-fdU) flanked by a 5'-8-OxodGuo or Fapy•dG were discovered, and they are more mutagenic in human cells than the isolated lesions. In the current study, we examined replication of these tandem lesions in <i>Escherichia coli</i>. Bypass efficiency of both tandem lesions was reduced by 30-40% compared to the isolated lesions. Mutation frequencies (MFs) of isolated 8-OxodGuo and Fapy•dG were low, and no mutants were isolated from replication of a 5-fdU construct. The types of mutations from 8-OxodGuo were targeted G → T transversion, whereas Fapy•dG predominantly gave G → T and G deletion. 5'-8-OxodGuo-5-fdU also gave exclusively G → T mutation, which was 3-fold and 11-fold greater, without and with SOS induction, respectively, compared to that of an isolated 8-OxodGuo. In <i>mutY/mutM</i> cells, the MF of 8-OxodGuo and 5'-8-OxodGuo-5-fdU increased 13-fold and 7-fold, respectively. The MF of 5'-8-OxodGuo-5-fdU increased 2-fold and 3-fold in Pol II- and Pol IV-deficient cells, respectively, suggesting that these polymerases carry out largely error-free bypass. The MF of 5'- Fapy•dG-5-fdU was similar without (13 ± 1%) and with (16 ± 2%) SOS induction. Unlike the complex mutation spectrum reported earlier in human cells for 5'- Fapy•dG-5-fdU, with G → T as the major type of errors, in <i>E. coli</i>, the mutations were predominantly from deletion of 5-fdU. We postulate that removal of adenine-incorporated opposite 8-OxodGuo by Fpg and MutY repair proteins is partially impaired in the tandem 5'-8-OxodGuo-5-fdU, resulting in an increase in the G → T mutations, whereas a slippage mechanism may be operating in the 5'- Fapy•dG-5-fdU mutagenesis. This study showed that not only are these tandem lesions more mutagenic than the isolated lesions but they may also exhibit different types of mutations in different organisms.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746746","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":"A Kinetic Model for Assessing Potential Nitrosamine Carcinogenicity.","authors":"Shu Yu, J Christopher McWilliams, Olivier Dirat, Krista L Dobo, Amit S Kalgutkar, Michelle O Kenyon, Matthew T Martin, Eric D Watt, Maik Schuler","doi":"10.1021/acs.chemrestox.4c00133","DOIUrl":"10.1021/acs.chemrestox.4c00133","url":null,"abstract":"<p><p>Understanding the potential carcinogenic potency of nitrosamines is necessary to setting acceptable intake limits. Nitrosamines and the components that can form them are commonly present in food, water, cosmetics, and tobacco. The recent observation of nitrosamines in pharmaceuticals highlighted the need for effective methods to determine acceptable intake limits. Herein, we describe two computational models that utilize properties based upon quantum mechanical calculations in conjunction with mechanistic insights and established data to determine the carcinogenic potency of a variety of common nitrosamines. These models can be applied to experimentally untested nitrosamines to aid in the establishment of acceptable intake limits.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141791283","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":"Cheminformatic Read-Across Approach Revealed Ultraviolet Filter Cinoxate as an Obesogenic Peroxisome Proliferator-Activated Receptor γ Agonist.","authors":"Seungchan An, In Guk Park, Seok Young Hwang, Junpyo Gong, Yeonjin Lee, Sungjin Ahn, Minsoo Noh","doi":"10.1021/acs.chemrestox.4c00091","DOIUrl":"10.1021/acs.chemrestox.4c00091","url":null,"abstract":"<p><p>This study introduces a novel cheminformatic read-across approach designed to identify potential environmental obesogens, substances capable of disrupting metabolism and inducing obesity by mainly influencing nuclear hormone receptors (NRs). Leveraging real-valued two-dimensional features derived from chemical fingerprints of 8435 Tox21 compounds, cluster analysis and subsequent statistical testing revealed 385 clusters enriched with compounds associated with specific NR targets. Notably, one cluster exhibited selective enrichment in peroxisome proliferator-activated receptor γ (PPARγ) agonist activity, prominently featuring methoxy cinnamate ultraviolet (UV) filters and obesogen-related compounds. Experimental validation confirmed that 2-ethoxyethyl 4-methoxycinnamate, an organic UV filter cinoxate, could selectively bind to PPARγ (<i>K</i>_i = 18.0 μM), eliciting an obesogenic phenotype in human bone marrow-derived mesenchymal stem cells during adipogenic differentiation. Molecular docking and further experiments identified cinoxate as a potent PPARγ full agonist, demonstrating a preference for coactivator SRC3 recruitment. Moreover, cinoxate upregulated transcription levels of genes encoding lipid metabolic enzymes in normal human epidermal keratinocytes as primary cells exposed during clinical usage. This study provides compelling evidence for the efficacy of cheminformatic read-across analysis in prioritizing potential obesogens, showcasing its utility in unveiling cinoxate as an obesogenic PPARγ agonist.</p>","PeriodicalId":31,"journal":{"name":"Chemical Research in Toxicology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141878016","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}