Computational Toxicology最新文献

{"title":"A novel deep learning framework for predicting protein-ligand interaction fingerprints from sequence data: integrating graph inductive bias transformer with Kolmogorov-Arnold networks","authors":"Lixin Lei,&nbsp;Qianjin Guo,&nbsp;Wu Liu,&nbsp;Zijun Wang,&nbsp;Kaitai Han,&nbsp;Chaojing Shi,&nbsp;Zhenxing Li,&nbsp;Sichao Lu,&nbsp;Mengqiu Wang,&nbsp;Zhiwei Zhang,&nbsp;Ruoyan Dai,&nbsp;Zhenghui Wang,&nbsp;Xingyu Liu","doi":"10.1016/j.comtox.2025.100386","DOIUrl":"10.1016/j.comtox.2025.100386","url":null,"abstract":"<div><div>Accurately modeling protein–ligand interactions is a central challenge in computational protein design and drug discovery. Traditional interaction fingerprint (IFP) approaches, while valuable, struggle to capture subtle binding features and adapt to diverse structural contexts. To address these limitations, we propose <strong>GITK</strong>, a deep learning framework that integrates a modified graph inductive bias transformer (GRIT) with Kolmogorov–Arnold networks (KANs) for interpretable interaction fingerprint prediction. GRIT introduces inductive bias to effectively represent both local and global graph structures of proteins and ligands, while KAN provides a principled functional decomposition that enhances nonlinear feature learning and interpretability. Benchmarking across multiple datasets demonstrates that GITK outperforms state-of-the-art models in binding affinity prediction, functional effect classification, and virtual screening. Moreover, GITK enables reliable selectivity analysis, successfully highlighting conformational differences and key residues in adenosine receptor subtypes, consistent with experimental findings such as the selectivity of the A1 antagonist DPCPX.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100386"},"PeriodicalIF":2.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling molecular level mechanisms of oxidative stress generation induced by agrochemicals in CKDu initiation","authors":"Samarawikcrama Wanni Arachchige Madushani Upamalika ,&nbsp;Champi Thusangi Wannige ,&nbsp;Sugandhima Mihirani Vidanagamachchi ,&nbsp;Don Kulasiri ,&nbsp;Mahesan Niranjan","doi":"10.1016/j.comtox.2025.100385","DOIUrl":"10.1016/j.comtox.2025.100385","url":null,"abstract":"<div><div>Oxidative stress is identified as a primary factor contributing to the failure of renal function. The excessive generation of oxidative stress is observed in CKDu patients in many experiments. Agrochemicals are identified as a major inducer of oxidative stress. Oxidative stress is induced mainly by direct generation of ROS through enzyme activation and by depleting antioxidant enzymes. To study how toxic exposure to agrochemicals alters the oxidative stress level in CKDu, a mathematical model of the body’s Redox system was developed and simulated how toxic exposure to agrochemicals, particularly arsenic toxicity, increases oxidative stress in cells. This model was employed to study how the molecular mechanisms of ROS generation are affected in CKDu. The study explores how arsenic concentration levels alter the oxidative stress levels and molecular interactions involved. The model indicates that the mitochondrial electron transport chain complex III is the primary contributor to ROS production, which needs to be validated through wet lab experiments. Sensitivity analyses on the model revealed that parameters associated with superoxide production are susceptible to perturbations. Further analysis shows that enzyme-driven reactions, especially those involving superoxide generation, catalase, and glutathione peroxidase, are crucial in governing oxidative stress generation in CKDu. According to the sensitivity analysis results, both NOX (NADPH oxidase) and SOD2 (superoxide dismutase 2) appear to be promising drug targets.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100385"},"PeriodicalIF":2.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational modeling of the hepatocytes reveals new insights into alterations in drug metabolism, oxidative stress response, and glutathione detoxification in acetaminophen-induced hepatotoxicity associated with MASLD","authors":"Yuki Miura ,&nbsp;Yasuyuki Sakai ,&nbsp;Masaki Nishikawa ,&nbsp;Eric Leclerc","doi":"10.1016/j.comtox.2025.100384","DOIUrl":"10.1016/j.comtox.2025.100384","url":null,"abstract":"<div><div>Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most common liver diseases worldwide, originating from abnormal fat accumulation in the liver. Acetaminophen (APAP) is a common antipyretic, but its overdose is a leading cause of acute liver failure. Clinical studies suggest that APAP-induced hepatotoxicity can be more frequent and severe in obese patients with MASLD. To investigate this process, we have developed a new mathematical model that comprehensively incorporates lipid metabolism, APAP metabolism, and glutathione (GSH) detoxification. In MASLD patients, we found that CYP and GST activities have higher sensitivity to ROS production than UGT and SULT, which are highly effective in detoxifying APAP. We also highlighted that the upregulation of GPx poses an unanticipated risk during steatosis by inducing an increase in H₂O₂. This occurs due to a vicious circle in which increasing NAPQI adducts further elevate H₂O₂ levels. According to clinical reports, the toxicity of APAP varies depending on the progression of MASLD. We simulated that the pool of enzymatic alterations observed in steatotic patients exacerbates APAP-induced toxicity, which is thought to be due to a significant upregulation of CYP2E1. In contrast, the enzyme changes in MASH patients alleviate APAP-induced toxicity, likely due to decreased activity of CYPs and increased activity of UGT and GST. We believe that our strategy, which couples lipid and drug metabolism, offers valuable pharmacological insights for identifying enzymes that play a significant role in liver injury and for devising future therapeutic strategies in the context of MASLD.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100384"},"PeriodicalIF":2.9,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of network toxicology and bioinformatics reveals novel neurodevelopmental toxicity mechanisms of 2,2′,4,4′-tetrabromodiphenyl ether","authors":"Yingying Feng,&nbsp;Tingting Huang","doi":"10.1016/j.comtox.2025.100383","DOIUrl":"10.1016/j.comtox.2025.100383","url":null,"abstract":"<div><div>Polybrominated diphenyl ethers, particularly 2,2′,4,4′-tetrabromodiphenyl ether (PBDE-47), are persistent environmental pollutants with suspected neurodevelopmental toxicity. This study systematically elucidated the mechanisms underlying PBDE-47-induced neurodevelopmental toxicity by integrating network toxicology and bioinformatic approaches. From 4070 potential targets, we identified 902 genes associated with neurodevelopmental disorders (ND), among which TP53, AKT1, and MAPK1 were identified as core regulatory factors via topological analysis. KEGG pathway enrichment analysis revealed significant enrichment in the HIF-1 signaling pathway and thyroid hormone signaling pathway. Molecular docking simulations confirmed that PBDE-47 stably binds to these key targets. Expression analysis validated the biological basis of PBDE-47 neurotoxicity. Single-cell RNA sequencing demonstrated the expression of target genes in neural cells. Immunohistochemistry further revealed the expression of AKT1 and MAPK1 in cortical neurons and glial cells. Ultimately, our study clarifies the multi-target and multi-pathway-mediated mechanisms of PBDE-47-induced neurodevelopmental toxicity, leading to an increased risk of ND. Although this computational approach provides mechanistic insights into environmentally induced ND, further experimental validation, epidemiological studies, and advanced spatial transcriptomic models are warranted to support these findings and facilitate the development of precise prevention strategies.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100383"},"PeriodicalIF":2.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AOP-informed qIVIVE modelling for liver steatosis using triazoles","authors":"A.M. Steinbach ,&nbsp;C.T. Willenbockel ,&nbsp;P. Marx-Stoelting ,&nbsp;M.T.D. Cronin ,&nbsp;V. Städele","doi":"10.1016/j.comtox.2025.100382","DOIUrl":"10.1016/j.comtox.2025.100382","url":null,"abstract":"<div><div>Due to increasing scientific, societal and regulatory demands as well as ethical considerations there is an urgent need for improved animal-free strategies for chemical testing. A promising development in this context is the increased application of <em>in vitro</em> testing and <em>in silico</em> tools. This study aimed at integrating quantitative <em>in vitro</em> to <em>in vivo</em> extrapolation (qIVIVE) with the adverse-outcome pathway (AOP) for liver steatosis. Liver steatosis is an important (toxicological) endpoint which constitutes the first step of metabolic-dysfunction associated steatotic liver disease (MASLD), a growing challenge in the public health sector. Focus was set on the late key event of triglyceride accumulation measured <em>in vitro</em> after exposure of cells to the fungicides propiconazole and tebuconzole, and the corresponding key event of liver fat vacuolation observed <em>in vivo</em>. The qIVIVE approach was facilitated by physiologically based kinetic (PBK) and <em>in vitro</em> distribution models. Concentrations predicted by PBK modelling corresponded well with experimentally determined <em>in vivo</em> plasma and liver concentrations of the fungicides. The <em>in vitro</em> concentration–response data for triglyceride accumulation, when translated to equivalent oral doses, showed good correlation to rodent <em>in vivo</em> data on liver fat vacuolation after oral exposure to propi- and tebuconazole. qIVIVE-derived benchmark dose values were similar to values obtained from the <em>in vivo</em> experiments. This case study confirms the usefulness of integrating AOPs and qIVIVE for adversity prediction particularly with regard to the “replacement” aspect of the 3R principle.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100382"},"PeriodicalIF":2.9,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of PBK models using the OECD assessment framework taking PFAS as case study","authors":"Deepika Deepika ,&nbsp;Kanchan Bharti ,&nbsp;Shubh Sharma ,&nbsp;Saurav Kumar ,&nbsp;Trine Husøy ,&nbsp;Marcin W. Wojewodzic ,&nbsp;Klára Komprdová ,&nbsp;Aude Ratier ,&nbsp;Joost Westerhout ,&nbsp;Thomas Gastellu ,&nbsp;Meg-Anne Moriceau ,&nbsp;Sanah Majid ,&nbsp;Renske Hoondert ,&nbsp;Johannes Kruisselbrink ,&nbsp;Jasper Engel ,&nbsp;Annelies Noorlander ,&nbsp;Carolina Vogs ,&nbsp;Vikas Kumar","doi":"10.1016/j.comtox.2025.100381","DOIUrl":"10.1016/j.comtox.2025.100381","url":null,"abstract":"<div><div>Physiologically based kinetic (PBK) models are becoming increasingly important in chemical risk assessment, helping in linking external and internal exposure concentrations, thereby supporting the development of regulatory health-based limits for chemicals with exposure from environmental, occupational, and consumer sources. To increase confidence in PBK models for regulatory purposes, the OECD published a guidance document in 2021 outlining the characterization, validation and reporting of PBK models. However, its use remains limited in chemical toxicology as reflected by the few publications that have applied it during model development. The aim of this study was to evaluate several published PBK models for Per- and polyfluoroalkyl substances (PFASs) as proof of concept to assess their validity and credibility for regulatory purposes, based on the OECD guidance. Out of 28 published PFASs human PBK models considered, 11 were selected for evaluation. The assessment used the OECD guidance document, encompassing two main areas: i) documentation (context/implementation, documentation, software implementation, verification, and peer engagement) and ii) assessment of model validity (biological basis, theoretical basis of model equations, input parameter’s reliability, uncertainty and sensitivity analysis, goodness-of-fit and predictivity). To standardize this process, an online evaluation system based on the OECD guidance was developed and used for this model evaluation exercise. The collected data were analysed to assess the overall quality of published models and identify limitations in the current PFAS model landscape. Our analysis revealed opportunities for improvement in the biological representation within current PFAS models, particularly regarding the inclusion of diverse population groups. Currently, PFAS models primarily focus on only four compounds, highlighting an opportunity to extend coverage to other PFASs using read-across approaches for data-poor chemicals. Furthermore, our findings show that a harmonized approach for PBK model reporting is needed. To facilitate broader adoption of the OECD guidance, we developed and hosted an R Shiny template on our group’s web server (<span><span>https://app.shiny.insilicohub.org/Evaluation_PBPK/</span><svg><path></path></svg></span>). This template can act as valuable tool for researchers evaluating PBK models according to the OECD guidance.</div><div>GitHub: PBPK-OECD-EVALUATION.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100381"},"PeriodicalIF":2.9,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In silico approaches using CASE Ultra and QSAR Toolbox for predicting genotoxicity and carcinogenicity on diverse groups of chemicals","authors":"Gowrav Adiga Perdur ,&nbsp;Zabiullah AJ ,&nbsp;Mohan Krishnappa ,&nbsp;Kamil Jurowski ,&nbsp;Varun Ahuja","doi":"10.1016/j.comtox.2025.100380","DOIUrl":"10.1016/j.comtox.2025.100380","url":null,"abstract":"<div><div>Humans are daily exposed to a wide range of chemicals in their environment, many of which may exert harmful effects on health. Hence, knowledge of these chemicals for their genotoxicity and carcinogenicity potential is crucial for protecting human health. Genotoxicity, in particular, serves as an early indicator of carcinogenic risk. The assessment of both genotoxicity and carcinogenicity is vital for regulatory bodies and has led to the development of alternative non-animal testing methods. One such method is <em>in silico</em> approach, which relies on predictive software tools for faster, more cost-effective screening.</div><div>This paper examines two <em>in silico</em> tools, CASE Ultra 1.9.0.8 (MultiCASE, USA) and QSAR Toolbox 4.5 (OECD), to evaluate their ability to predict the genotoxicity and carcinogenicity of various chemicals. The <em>in silico</em> tools CASE Ultra, QSAR Toolbox, and its profilers demonstrated remarkable performance, with balanced accuracy rates of 80%, 85%, and 62%, for genotoxicity and 79%, 86% and 66% for carcinogenicity, respectively. These promising results underscore the potential of computational approaches in risk assessment, offering a valuable complement to traditional testing methods for evaluating the genotoxicity and carcinogenicity of chemicals. Such tools can play a crucial role in regulatory decision-making and public health protection.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100380"},"PeriodicalIF":2.9,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT integrated quantile principal component analysis based framework for toxic pesticides recognition and classification","authors":"Kanak Kumar ,&nbsp;Anshul Verma ,&nbsp;Pradeepika Verma","doi":"10.1016/j.comtox.2025.100375","DOIUrl":"10.1016/j.comtox.2025.100375","url":null,"abstract":"<div><div>Pesticides present significant concerns regarding environmental sustainability and global stability. This study investigates the types, benefits, and environmental challenges associated with pesticide use. To address these concerns, we developed an innovative Internet of Things (IoT) integrated quantile principal component analysis (QPCA) framework for the recognition of toxic pesticides in smart farming, termed IoT-TPR. The proposed IoT-TPR system is an intelligent electronic nose based on a tin-oxide sensor array, consisting of eight commercial metal–oxide–semiconductor gas sensors, which detect toxic pesticides and transmit the data to the Amazon Web Services cloud for further analysis. A two-stage QPCA preprocessing technique is employed to analyze sensor responses. Subsequently, four classifiers such as radial basis function (RBF), extreme learning machine (ELM), decision tree (DT), and k-nearest neighbor (KNN) are used for comparative performance evaluation. The results indicate that QPCA-KNN achieves the highest accuracy at 99.05%, outperforming other methods across all performance metrics and demonstrating superior classification capability. RBF (96.24%) and ELM (95.81%) also exhibit strong performance, though slightly lower than QPCA-KNN, while DT (92.35%) shows the lowest accuracy but still maintains reasonable performance. Overall, QPCA-KNN emerges as the most effective and robust classification model in this study.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100375"},"PeriodicalIF":2.9,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring in silico tools to predict estrogen receptor activity of chemicals for the assessment of endocrine disruption","authors":"Gyamfi Akyianu ,&nbsp;Carsten Kneuer ,&nbsp;Judy Choi","doi":"10.1016/j.comtox.2025.100379","DOIUrl":"10.1016/j.comtox.2025.100379","url":null,"abstract":"<div><div><em>In silico</em> software and tools are increasingly being employed as an alternative to <em>in vivo</em> animal testing to predict toxicity of chemicals. One particular application of the underlying <em>in silico</em> models for hazard assessment has been to predict the potential endocrine disrupting activity of chemicals, which is one of the three fundamental elements of an endocrine disrupting chemical (EDC). In this study, 11 <em>in silico</em> tools based on methods ranging from Quantitative Structure-Activity Relationship (QSAR) to docking were selected and tested for their predictivity of estrogen receptor (ER) activity using a set of 80 chemicals of known ER activity potential. The accuracy in prediction, as determined by Matthew’s correlation coefficient (MCC), among the 11 individual tools tested ranged from 0.16 to 0.54 (min–max). However, when combining various tools and applying rules set for a conservative approach in assessing the prediction outcomes, the MCC increased as high as 0.68, demonstrating the higher probability of generating a correct prediction when multiple <em>in silico</em> tools are employed. This study presents the strengths and weaknesses of the individual tools/models tested and provides insights on how <em>in silico</em> predictions could supplement the weight-of-evidence approach in determining endocrine activity potential of chemicals.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100379"},"PeriodicalIF":2.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“RapidTox”: A decision-support workflow to inform rapid toxicity and human health assessment","authors":"Jason C. Lambert ,&nbsp;Jason Brown ,&nbsp;Hui Gong ,&nbsp;Curtis Kilburn ,&nbsp;Jan Krysa ,&nbsp;Brad Kuntzelman ,&nbsp;Janet Lee ,&nbsp;April Luke ,&nbsp;Joshua Powell ,&nbsp;Asif Rashid ,&nbsp;James Renner ,&nbsp;Risa Sayre ,&nbsp;Jyothi Tumkur ,&nbsp;Carl F. Valone ,&nbsp;Chelsea Weitekamp ,&nbsp;Russell S. Thomas","doi":"10.1016/j.comtox.2025.100377","DOIUrl":"10.1016/j.comtox.2025.100377","url":null,"abstract":"<div><div>Regulatory bodies such as the U.S. Environmental Protection Agency are consistently faced with decisions pertaining to potential human health impacts of a diverse landscape of chemicals encountered in exposure matrices such as water, air, and soil. For legacy chemicals or those currently in commerce, decision contexts may range from emergency response to disasters where evaluation of potential threats to human health occurs on the order of hours to days, up to site- or media-specific assessment and remediation over the course of months to years. In addition, screening and prioritization of new chemicals or emerging contaminants represents an ever-present focus area for the regulatory community. A common theme across these overarching decision contexts is the need for assembling and integrating human health relevant data such as toxicity values and associated effects information. Various activities ranging from screening and prioritization to human health risk assessment of chemicals have historically been time and resource intensive, often requiring that practitioners consult and review a variety of disparate data streams to inform a given decision. In addition, many environmental chemicals are ‘data-poor’, lacking sufficient hazard data or toxicity values applicable to a given exposure scenario. In response, decision-based workflows have been developed and deployed in the RapidTox online platform wherein available toxicity values, hazard/effects data, physicochemical properties, and new approach methods-based data (e.g., read-across; cell-based bioactivity) have been assembled into data delivery modules. To date, the user interface design and expertly scoped content have been integrated in ‘screening human health assessment’ or ‘emergency response’ workflows to support decision-making.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100377"},"PeriodicalIF":2.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}