Computational Toxicology最新文献

{"title":"Building a compendium of expert driven read-across cases to facilitate an analysis of the contribution that different similarity contexts play in read-across performance","authors":"Grace Patlewicz ,&nbsp;Nathaniel Charest ,&nbsp;Amanda Ross ,&nbsp;HC Bledsoe ,&nbsp;Janielle Vidal ,&nbsp;Sadegh Faramarzi ,&nbsp;Brett Hagan ,&nbsp;Imran Shah","doi":"10.1016/j.comtox.2025.100366","DOIUrl":"10.1016/j.comtox.2025.100366","url":null,"abstract":"<div><div>Read-across is a data-gap filling technique used to predict the toxicity of a target chemical based on data from similar analogues. It is predominantly performed through expert-driven assessments which can limit reproducibility and broader acceptance. Data-driven approaches such as Generalised Read-Across (GenRA) offer the potential to generate more reproducible read-across predictions with quantified uncertainties and performance metrics. A key challenge is reconciling expert- and data-driven approaches particularly in how analogues are identified, evaluated and used to derive predictions. A critical aspect of analogue selection lies in understanding the relative contribution of different similarity contexts e.g. whether structural similarity plays a larger role than metabolism similarity. This study explored these considerations by compiling a compendium of expert-driven read-across assessments for repeated dose toxicity endpoints from peer reviewed and grey literature. Pairwise similarity was quantified across structural, physicochemical, metabolic and reactivity features within each case and a prediction model was developed to evaluate the contribution of each similarity context in analogue selection. Although the dataset comprised 157 read-across cases and 695 unique substances, it was limited in size, heterogeneous in origin and variable in analogue selection criteria and use contexts. These factors constrain generalisability of the findings and indicate that conclusions should be interpreted with caution. Nonetheless, the qualitative insight that structure and metabolism were influential led to a followup investigation using graph-based deep learning to explore whether embeddings derived from structure and/or metabolism information could improve read-across predictions, using repeated dose toxicity as a case study, relative to structural similarity baselines.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100366"},"PeriodicalIF":3.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631327","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":"Machine learning-based structural analysis of OATP1B1 interactors/non-interactors: Discriminating toxic and non-toxic alerts for transporter-mediated toxicity","authors":"Shovanlal Gayen ,&nbsp;Indrasis Dasgupta ,&nbsp;Balaram Ghosh ,&nbsp;Insaf Ahmed Qureshi ,&nbsp;Partha Pratim Roy","doi":"10.1016/j.comtox.2025.100373","DOIUrl":"10.1016/j.comtox.2025.100373","url":null,"abstract":"<div><div>This hepatic transporter, OATP1B1, plays a critical role in transporter-related toxic responses and drug-drug interactions (DDIs). Several drug-drug interactions associated with OATP1B1 are clinically reported during combination therapies of lipid-lowering statins with antihypertensive, antiviral, and antibiotic drugs.</div><div>In the present study, different molecular properties of OATP1B1-interactors and non-interactors were initially compared, and the results revealed a distinct pattern in molecular weight, hydrophobicity, and number of rotatable bonds between them. Further chemical space, scaffold content, and diversity analyses indicated that OATP1B1-interactors/non-interactors are structurally diverse. Recursive partitioning and Bayesian classification analyses, involving ECFP and FCFP fingerprints, highlighted critical structural features that may serve as alerts for toxic or non-toxic effects on OATP1B1-mediated toxicity. Other machine learning-based classification models were also constructed, where Support Vector Classifier (SVC) shows higher statistical significance and predictive ability (accuracy: 0.797; precision: 0.833, and recall: 0.758). Moreover, local and global SHAP analyses were also performed to explain the distinctive structural features of OATP1B1-interactors and non-interactors.</div><div>Overall, the study offers insights into structural determinants of OATP1B1 interactions and provides predictive models to distinguish interactors from non-interactors, which may aid in reducing transporter-related toxicity risks in drug development. The outcomes may assist in advancing the safety and performance of medicinal compounds.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100373"},"PeriodicalIF":2.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831426","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":"Development of mathematical new approach methods to assess chemical mixtures","authors":"R. Broughton ,&nbsp;M. Feshuk ,&nbsp;Z. Stanfield ,&nbsp;K.K. Isaacs ,&nbsp;K. Paul Friedman","doi":"10.1016/j.comtox.2025.100376","DOIUrl":"10.1016/j.comtox.2025.100376","url":null,"abstract":"<div><div>The Toxicity Forecaster (ToxCast) program contains targeted bioactivity screening data for thousands of chemicals, but chemicals are often encountered as co-exposures. This work evaluated the feasibility of using single chemical ToxCast data to predict mixture bioactivity assuming chemical additivity. Twenty-one binary mixtures and their single components, inspired by consumer product chemical exposures, were screened in concentration–response using a multidimensional <em>in vitro</em> assay platform for transcription factor activity. Three models were applied to simulate mixtures’ concentration-responses: concentration addition (CA), independent action (IA), and a model that treats the mixture as the most potent single chemical component (MP). Uncertainty in the modeled and observed mixture points of departure and full concentration-responses was considered using bootstrap resampling and a Bayesian statistical framework. Approximately 80 % of the predicted mixture point of departure values were within ±0.5 on a log₁₀-micromolar scale of the observed concentrations; a majority of these predicted points of departure were protective (90–96 %), whether using CA, IA, or MP derived with the screened single components, when compared to the observed mixture. For most mixtures, ≥80 % of the observed mixture concentration–response data points fell within the modeled 95 % prediction interval, suggesting it would be difficult to observe deviations from additivity when accounting for experimental and mixtures modeling uncertainties. As it is resource-prohibitive to screen all mixtures, a case study to estimate bioactivity:exposure ratios for mixtures of per- and polyfluoroalkyl chemicals demonstrated the utility of operationalizing existing ToxCast data with mixtures modeling that includes uncertainty to predict potential risk from co-exposures.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100376"},"PeriodicalIF":2.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891954","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":"On the comparability between studies in predictive ecotoxicology","authors":"Christoph Schür ,&nbsp;Kristin Schirmer ,&nbsp;Marco Baity-Jesi","doi":"10.1016/j.comtox.2025.100367","DOIUrl":"10.1016/j.comtox.2025.100367","url":null,"abstract":"<div><div>Comparability across <em>in silico</em> predictive ecotoxicology studies remains a significant challenge, particularly when assessing model performance. In this work, we identify key criteria necessary for meaningful comparison between independent studies: (i) the use of identical datasets that represent the same chemical and/or taxonomic space; (ii) consistent data cleaning procedures; (iii) identical train/test splits; (iv) clearly defined evaluation metrics, as subtle differences — such as alternative formulations of <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> — can lead to misleading discrepancies; and (v) transparent reporting through code and dataset sharing. Our review of recent literature on fish acute toxicity prediction reveals a critical gap: no two studies fully meet these criteria, rendering cross-study comparisons unreliable. This lack of comparability hampers scientific progress in the field. To address this, we advocate for the adoption of benchmark datasets with standardized cleaning protocols, version control, and defined data splits. We further emphasize the importance of precise metric definitions and transparent reporting practices, including code availability and the use of structured reporting or data sheets, to foster reproducibility and advance the discipline.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100367"},"PeriodicalIF":2.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866339","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 analyses as a tool for regulatory assessment of protein digestibility: Where are we?","authors":"Fernando Rivero-Pino,&nbsp;Caroline Idowu,&nbsp;Hannes Malfroy,&nbsp;Diana Rueda,&nbsp;Hannah Lester","doi":"10.1016/j.comtox.2025.100372","DOIUrl":"10.1016/j.comtox.2025.100372","url":null,"abstract":"<div><div><em>In silico</em> tools are emerging as a valuable resource for predicting the behaviour of proteins, not only for the assessment of toxicity and allergenicity, but also for modelling digestion to study protein digestibility. These methods offer cost-effective, high-throughput alternatives to traditional <em>in vitro</em> and <em>in vivo</em> methods. Computational models simulate enzymatic digestion, allowing the analysis of protein cleavage and peptide release. Complementary tools such as molecular docking have also been proposed as part of the <em>in silico</em> battery of tests. Given their efficiency, <em>in silico</em> approaches could ultimately be proposed to support regulated product applications, particularly in assessing protein digestibility for novel foods. However, their acceptance and use in risk assessment remains uncertain due to a lack of validation in part due to conflicting findings cited in the literature − while some studies report strong correlations between <em>in silico</em> and <em>in vitro</em> digestibility results, others indicate significant discrepancies. This review critically evaluates the potential regulatory application of <em>in silico</em> protein digestibility models for use in novel food risk assessment, highlighting key challenges such as model standardization, validation against experimental data, and the influence of protein structure and digestion conditions. Future research should focus on refining model accuracy and establishing clear validation frameworks to enhance regulatory confidence in <em>in silico</em> digestion tools.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100372"},"PeriodicalIF":2.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780871","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":"The FAIR AOP roadmap for 2025: Advancing findability, accessibility, interoperability, and re-usability of adverse outcome pathways","authors":"Holly M. Mortensen ,&nbsp;Maciej Gromelski ,&nbsp;Ginnie Hench ,&nbsp;Marvin Martens ,&nbsp;Clemens Wittwehr ,&nbsp;Saurav Kumar ,&nbsp;Vikas Kumar ,&nbsp;Karine Audouze ,&nbsp;Vassilis Virvilis ,&nbsp;Penny Nymark ,&nbsp;Michelle Angrish ,&nbsp;Iseult Lynch ,&nbsp;Stephen Edwards ,&nbsp;Barbara Magagna ,&nbsp;Marcin W. Wojewodzic ,&nbsp;The FAIR AOP Cluster Working Group","doi":"10.1016/j.comtox.2025.100368","DOIUrl":"10.1016/j.comtox.2025.100368","url":null,"abstract":"<div><div>Adverse Outcome Pathways (AOPs) describe the mechanistic interactions of biological entities with a stressor (chemical, nanomaterial, radiation, virus, etc.) that produce an adverse response. How these interactions and associations are catalogued contributes to our ability to understand mechanistic effects and apply this knowledge to New Approach Methods (NAMs) that have the potential to reduce animal testing in chemical, biological, and material safety assessments. Making AOP data align with FAIR (Findable, Accessible, Interoperable, and Reusable) metadata standards relies on technical tools that implement and process AOP data and related metadata, and the establishment of coordinated and consensus computational bioinformatic methods. Herein current efforts in addressing the FAIRification of AOP mechanistic data and metadata, as well as the international, collaborative efforts to document, and improve the (re)-use and reliability of AOP information will be described. These coordinated efforts contribute to the establishment of a directive for the processing and storing of standardized AOP mechanistic data in the AOP-Wiki repository, and application of these data to next generation risk assessment.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100368"},"PeriodicalIF":2.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748772","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":"Screening for genotoxicants in food: A data-driven approach using food composition data and machine learning based in silico models","authors":"Jakob Menz,&nbsp;Bernd Schäfer","doi":"10.1016/j.comtox.2025.100370","DOIUrl":"10.1016/j.comtox.2025.100370","url":null,"abstract":"<div><div>Foods represent complex mixtures of constituents and contaminants, some of which may pose risks to health through genotoxic effects. We investigated the current capabilities and limitations of a data-driven approach for the systematic identification of genotoxic substances in food. To this end, we used machine learning to develop quantitative structure–activity relationship (QSAR) models aimed at predicting outcomes for three <em>in vitro</em> genotoxicity assays: the bacterial reverse mutation assay (Ames test), the <em>in vitro</em> chromosomal aberration test (CAvit) and the <em>in vitro</em> micronucleus test (MNvit). These models were applied to screen for putative dietary genotoxicants using the FooDB compound dataset (n = 70,477) as a case study. Overall, 6.6 % of the FooDB compounds were predicted as positive by at least one <em>in silico</em> model, while 7.1 % were predicted as negative by all three models. Depending on the predicted endpoint, between 77 % and 82 % of the FooDB compounds fell outside the model’s applicability domain or gave an equivocal prediction. Interestingly, of the 4,683 FooDB compounds predicted to be positive in at least one <em>in vitro</em> assay, only 491 could be mapped to an experimental data point. As a strategy to progress from <em>in silico</em> screening to risk assessment, we propose a tiered approach that integrates <em>in silico</em> modelling, exposure assessment and experimental testing. While it has to be acknowledged that current food composition databases and <em>in silico</em> models still have limitations, this work illustrates that data-driven approaches hold great promise for enhancing the identification of genotoxicants in foods.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100370"},"PeriodicalIF":2.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757488","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":"An in silico protocol for endocrine activity assessment: Integrating predictions, experimental evidence, and expert reviews across estrogen, androgen, thyroid, and steroidogenesis modalities","authors":"Candice Johnson ,&nbsp;Sue Marty ,&nbsp;Marlene Kim ,&nbsp;Kevin Crofton ,&nbsp;Alessandra Roncaglioni ,&nbsp;Arianna Bassan ,&nbsp;Tara Barton-Maclaren ,&nbsp;Ana Domingues ,&nbsp;Markus Frericks ,&nbsp;Agnes Karmaus ,&nbsp;Sunil Kulkarni ,&nbsp;Elena Lo Piparo ,&nbsp;Stephanie Melching-Kollmuss ,&nbsp;Ray Tice ,&nbsp;David Woolley ,&nbsp;Kevin Cross","doi":"10.1016/j.comtox.2025.100364","DOIUrl":"10.1016/j.comtox.2025.100364","url":null,"abstract":"<div><div>Endocrine disruption (ED) has been introduced as a new classification, labelling and packaging (CLP) hazard category under Regulation (EC) No 1272/2008. Additionally, consideration of endocrine-disrupting properties and endocrine-related effects continues to be an important aspect of chemicals management under the Canadian Environmental Protection Act (CEPA) 1999 for the prioritization and hazard characterization of potential hormone disrupting substances. To support chemical prioritization and hazard assessment, this study presents a structured <em>in silico</em> protocol for assessing endocrine activity across the estrogen (E), androgen (A), thyroid (T), and steroidogenesis (S) (EATS) modalities. The protocol integrates (Quantitative) Structure–Activity Relationship ((Q)SAR) predictions with experimental data using a structured approach grounded in a hazard assessment framework (HAF) and defines principles for evaluating the reliability and confidence of predictions. Key endpoints and model development opportunities are identified for each modality. Two case studies are presented to demonstrate the application of the protocol. In the assessment of 4-Chloro-1-[2,2-dichloro-1-(4-chlorophenyl)ethenyl]-2-(methylsulfonyl)benzene, structurally similar analogs supported a medium-confidence assessment of estrogen and androgen activity. Whereas, in the assessment of chloroprene, uncertainties due to potential metabolic transformation limited confidence in negative assessments. These case studies illustrate how model outputs, experimental evidence, an analysis of analogs, and expert review can be integrated to produce transparent and reproducible assessments. The framework supports a weight-of-evidence (WOE) non-testing approach for identifying endocrine-active substances.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100364"},"PeriodicalIF":3.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144569991","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":"Part I. Systematic development of machine learning models for predicting mechanism-based toxicity from in vitro ToxCast bioassay data","authors":"Donghyeon Kim ,&nbsp;Siyeol Ahn ,&nbsp;Jiyong Jeong,&nbsp;Jinhee Choi","doi":"10.1016/j.comtox.2025.100371","DOIUrl":"10.1016/j.comtox.2025.100371","url":null,"abstract":"<div><div>Artificial intelligence (AI) for toxicity prediction has gained significant attention as a potential new approach methodologies (NAMs) for next-generation risk assessment (NGRA). Among the various large toxicity data sources, the ToxCast database represents a valuable resource that is frequently used to develop AI models. To facilitate the regulatory adoption of such models, it is essential to identify those that offer both suitable predictive performance and clear relevance to regulatory endpoints. In this study, we systematically developed mechanism-based toxicity-prediction models using ToxCast bioassay data and sought to identify machine-learning models applicable to NGRA. We collected 1,485 bioassay datasets from InvitroDB v4.1 and pre-processed them for model training. Five types of molecular fingerprints (MACCS, Morgan, RDKit, Layered, and Pattern) and five machine-learning algorithms (logistic regression, decision tree, random forest, gradient boosting tree, and XGBoost) were applied to 980 bioassays, yielding 24,500 models. The best-performing model for each assay was selected according to the F1 score. Using annotations from the NTP ICE database, we ultimately selected 311 models trained on bioactivity data relevant to regulatory endpoints—including acute toxicity, developmental and reproductive toxicity, carcinogenicity, and endocrine disruption—that achieved acceptable performance (F1 score ≥ 0.5). Overall, this study provides a cornerstone for incorporating ToxCast-based AI models into NGRA.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100371"},"PeriodicalIF":2.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748773","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}