Pub Date : 2025-07-25DOI: 10.1016/j.comtox.2025.100371
Donghyeon Kim , Siyeol Ahn , Jiyong Jeong, Jinhee Choi
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.
{"title":"Part I. Systematic development of machine learning models for predicting mechanism-based toxicity from in vitro ToxCast bioassay data","authors":"Donghyeon Kim , Siyeol Ahn , Jiyong Jeong, 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":"2025-07-25","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}
Pub Date : 2025-07-24DOI: 10.1016/j.comtox.2025.100370
Jakob Menz, Bernd Schäfer
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 in vitro genotoxicity assays: the bacterial reverse mutation assay (Ames test), the in vitro chromosomal aberration test (CAvit) and the in vitro 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 in silico 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 in vitro assay, only 491 could be mapped to an experimental data point. As a strategy to progress from in silico screening to risk assessment, we propose a tiered approach that integrates in silico modelling, exposure assessment and experimental testing. While it has to be acknowledged that current food composition databases and in silico models still have limitations, this work illustrates that data-driven approaches hold great promise for enhancing the identification of genotoxicants in foods.
{"title":"Screening for genotoxicants in food: A data-driven approach using food composition data and machine learning based in silico models","authors":"Jakob Menz, 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":"2025-07-24","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}
Pub Date : 2025-07-21DOI: 10.1016/j.comtox.2025.100368
Holly M. Mortensen , Maciej Gromelski , Ginnie Hench , Marvin Martens , Clemens Wittwehr , Saurav Kumar , Vikas Kumar , Karine Audouze , Vassilis Virvilis , Penny Nymark , Michelle Angrish , Iseult Lynch , Stephen Edwards , Barbara Magagna , Marcin W. Wojewodzic , The FAIR AOP Cluster Working Group
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.
{"title":"The FAIR AOP roadmap for 2025: Advancing findability, accessibility, interoperability, and re-usability of adverse outcome pathways","authors":"Holly M. Mortensen , Maciej Gromelski , Ginnie Hench , Marvin Martens , Clemens Wittwehr , Saurav Kumar , Vikas Kumar , Karine Audouze , Vassilis Virvilis , Penny Nymark , Michelle Angrish , Iseult Lynch , Stephen Edwards , Barbara Magagna , Marcin W. Wojewodzic , 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":"2025-07-21","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}
Pub Date : 2025-07-17DOI: 10.1016/j.comtox.2025.100369
Donghyeon Kim , Jiyong Jeong , Siyeol Ahn, Jinhee Choi
Artificial intelligence (AI)-based toxicity prediction models have emerged as promising new approach methodologies (NAMs) to reduce reliance on traditional in vivo testing in chemical risk assessment. In this study, we systematically developed machine learning models using toxicity data generated in accordance with OECD Test Guidelines (TG), available in the eChemPortal database. The models targeted endpoints regulated under major chemical frameworks, including Korea’s Act on the Registration and Evaluation of Chemical Substances (K-REACH) and the Consumer Chemical Products and Biocides Safety Control Act (K-BPR), as well as the European Union’s Registration, Evaluation, Authorization and Restriction of Chemicals (EU REACH) and Biocidal Products Regulation (EU BPR). A comprehensive training dataset was curated by harmonizing dose descriptors, effect levels, and exposure routes. Model features were generated using four types of molecular fingerprints (MACCS, Morgan, RDKit, and Layered), and five machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Tree, and XGBoost—were trained. Model performance was evaluated using standard metrics, including F1 score, precision, recall, accuracy, AUC-ROC. In total, 680 models were developed for 17 TG-based endpoints. The best-performing model for each endpoint was selected based on its F1 score. Machine learning models predicting acute toxicity (TG 420, 402, 403), developmental toxicity (TG 414), carcinogenicity (TG 453), and ecotoxicity (TG 201, 202, 203, 210, 211) demonstrated acceptable performance (F1 score ≥ 0.5), whereas models for other endpoints require further improvement. Based on these findings, we suggest key challenges and considerations for applying machine learning models trained on OECD TG data to support next generation chemical risk assessment (NGRA).
{"title":"Part II. Systematic development of machine learning models for predicting human and ecotoxicity from in vivo OECD test guideline data","authors":"Donghyeon Kim , Jiyong Jeong , Siyeol Ahn, Jinhee Choi","doi":"10.1016/j.comtox.2025.100369","DOIUrl":"10.1016/j.comtox.2025.100369","url":null,"abstract":"<div><div>Artificial intelligence (AI)-based toxicity prediction models have emerged as promising new approach methodologies (NAMs) to reduce reliance on traditional in vivo testing in chemical risk assessment. In this study, we systematically developed machine learning models using toxicity data generated in accordance with OECD Test Guidelines (TG), available in the eChemPortal database. The models targeted endpoints regulated under major chemical frameworks, including Korea’s Act on the Registration and Evaluation of Chemical Substances (K-REACH) and the Consumer Chemical Products and Biocides Safety Control Act (K-BPR), as well as the European Union’s Registration, Evaluation, Authorization and Restriction of Chemicals (EU REACH) and Biocidal Products Regulation (EU BPR). A comprehensive training dataset was curated by harmonizing dose descriptors, effect levels, and exposure routes. Model features were generated using four types of molecular fingerprints (MACCS, Morgan, RDKit, and Layered), and five machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Tree, and XGBoost—were trained. Model performance was evaluated using standard metrics, including F1 score, precision, recall, accuracy, AUC-ROC. In total, 680 models were developed for 17 TG-based endpoints. The best-performing model for each endpoint was selected based on its F1 score. Machine learning models predicting acute toxicity (TG 420, 402, 403), developmental toxicity (TG 414), carcinogenicity (TG 453), and ecotoxicity (TG 201, 202, 203, 210, 211) demonstrated acceptable performance (F1 score ≥ 0.5), whereas models for other endpoints require further improvement. Based on these findings, we suggest key challenges and considerations for applying machine learning models trained on OECD TG data to support next generation chemical risk assessment (NGRA).</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100369"},"PeriodicalIF":3.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714500","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}
Pub Date : 2025-07-10DOI: 10.1016/j.comtox.2025.100366
Grace Patlewicz , Nathaniel Charest , Amanda Ross , HC Bledsoe , Janielle Vidal , Sadegh Faramarzi , Brett Hagan , Imran Shah
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.
{"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 , Nathaniel Charest , Amanda Ross , HC Bledsoe , Janielle Vidal , Sadegh Faramarzi , Brett Hagan , 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":"2025-07-10","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}
Pub Date : 2025-07-01DOI: 10.1016/j.comtox.2025.100365
Jonathan T. Wall , Risa R. Sayre , Doris Smith , Samuel Winter , Maxwell Groover , Jasmine Hope , Adriana Webb , Katie Paul Friedman , Madison Feshuk , Antony J. Williams , Charles Lowe , Nisha S. Sipes , Jason Lambert , Jennifer H. Olker , Russell S. Thomas , Colleen Elonen , Richard S. Judson , Chelsea A. Weitekamp
The Toxicity Values Database, ToxValDB, was developed by the U.S. EPA Center for Computational Toxicology and Exposure as a resource to curate, store, standardize, and make accessible a wide range of human health-relevant toxicity information. The database originated in response to the need for harmonized and computationally accessible toxicology data. The scope and design of the database have evolved over time since its first release in 2016. Herein, the newly redesigned structure and development of ToxValDB v9.6.1 is described. The database is a compilation of three classes of summary-level values for chemical substances: in vivo toxicity study results (e.g., lowest- and no-observed adverse effect level), derived toxicity values (e.g., maximum acceptable oral dose), and media exposure guidelines (e.g., maximum contaminant level for drinking water). The current version of the database (9.6.1) contains 242,149 records covering 41,769 unique chemicals from 36 sources (55 source tables). With all records in a consistent structure normalized to a standardized vocabulary, the chemical and data landscape of ToxValDB v9.6.1 can be evaluated. To illustrate chemical coverage, the available data were mapped to chemical lists of regulatory importance. Further, the distribution of oral administered doses within in vivo toxicity studies was assessed by annotated chemical class. The harmonized in vivo data within ToxValDB have many applications including use in chemical screening and prioritization for human health assessment, modeling predictions, and benchmarking for New Approach Methods (NAMs), as well as to address a diverse range of novel research questions.
{"title":"Development of the toxicity values database, ToxValDB: A curated resource for experimental and derived human health-relevant toxicity data","authors":"Jonathan T. Wall , Risa R. Sayre , Doris Smith , Samuel Winter , Maxwell Groover , Jasmine Hope , Adriana Webb , Katie Paul Friedman , Madison Feshuk , Antony J. Williams , Charles Lowe , Nisha S. Sipes , Jason Lambert , Jennifer H. Olker , Russell S. Thomas , Colleen Elonen , Richard S. Judson , Chelsea A. Weitekamp","doi":"10.1016/j.comtox.2025.100365","DOIUrl":"10.1016/j.comtox.2025.100365","url":null,"abstract":"<div><div>The Toxicity Values Database, ToxValDB, was developed by the U.S. EPA Center for Computational Toxicology and Exposure as a resource to curate, store, standardize, and make accessible a wide range of human health-relevant toxicity information. The database originated in response to the need for harmonized and computationally accessible toxicology data. The scope and design of the database have evolved over time since its first release in 2016. Herein, the newly redesigned structure and development of ToxValDB v9.6.1 is described. The database is a compilation of three classes of summary-level values for chemical substances: <em>in vivo</em> toxicity study results (e.g., lowest- and no-observed adverse effect level), derived toxicity values (e.g., maximum acceptable oral dose), and media exposure guidelines (e.g., maximum contaminant level for drinking water). The current version of the database (9.6.1) contains 242,149 records covering 41,769 unique chemicals from 36 sources (55 source tables). With all records in a consistent structure normalized to a standardized vocabulary, the chemical and data landscape of ToxValDB v9.6.1 can be evaluated. To illustrate chemical coverage, the available data were mapped to chemical lists of regulatory importance. Further, the distribution of oral administered doses within <em>in vivo</em> toxicity studies was assessed by annotated chemical class. The harmonized <em>in vivo</em> data within ToxValDB have many applications including use in chemical screening and prioritization for human health assessment, modeling predictions, and benchmarking for New Approach Methods (NAMs), as well as to address a diverse range of novel research questions.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100365"},"PeriodicalIF":3.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614246","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}
Pub Date : 2025-06-25DOI: 10.1016/j.comtox.2025.100364
Candice Johnson , Sue Marty , Marlene Kim , Kevin Crofton , Alessandra Roncaglioni , Arianna Bassan , Tara Barton-Maclaren , Ana Domingues , Markus Frericks , Agnes Karmaus , Sunil Kulkarni , Elena Lo Piparo , Stephanie Melching-Kollmuss , Ray Tice , David Woolley , Kevin Cross
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 in silico 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.
根据法规（EC） No 1272/2008，内分泌干扰（ED）作为一个新的分类、标签和包装（CLP）危害类别被引入。此外，根据1999年加拿大环境保护法（CEPA），考虑内分泌干扰特性和内分泌相关影响仍然是化学品管理的一个重要方面，以确定潜在激素干扰物质的优先级和危害特征。为了支持化学物质优先级和危害评估，本研究提出了一种结构化的计算机程序，用于评估雌激素(E)、雄激素(a)、甲状腺(T)和类固醇生成(S)模式的内分泌活性。该方案使用基于危害评估框架（HAF）的结构化方法，将（定量）结构-活性关系（(Q)SAR）预测与实验数据相结合，并定义了评估预测可靠性和置信度的原则。为每个模态确定关键端点和模型开发机会。给出了两个案例研究来演示该协议的应用。在对4-氯-1-[2,2-二氯-1-（4-氯苯基）乙基]-2-（甲基磺酰基）苯的评估中，结构相似的类似物支持了对雌激素和雄激素活性的中等置信度评估。然而，在氯丁二烯的评估中，由于潜在代谢转化的不确定性限制了负面评估的可信度。这些案例研究说明了如何将模型输出、实验证据、类似物分析和专家评审结合起来，以产生透明和可重复的评估。该框架支持证据权重（WOE）非测试方法来识别内分泌活性物质。
{"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 , Sue Marty , Marlene Kim , Kevin Crofton , Alessandra Roncaglioni , Arianna Bassan , Tara Barton-Maclaren , Ana Domingues , Markus Frericks , Agnes Karmaus , Sunil Kulkarni , Elena Lo Piparo , Stephanie Melching-Kollmuss , Ray Tice , David Woolley , 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":"2025-06-25","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}
Pub Date : 2025-06-14DOI: 10.1016/j.comtox.2025.100363
Youngchan Jang , Jeongyun Kim , Doo-Hee Lee , Jin Yoo , Jeongwan Park , Ku Kang
Chemical warfare agents (CWAs), particularly organophosphorus (OP) nerve agents, are among the most toxic and persistent compounds known, posing significant threats to human health and security. Experimental determination of their median lethal dose (LD50) values is limited by ethical, biosafety, and accessibility constraints. While conventional QSAR models provide useful approximations, they often lack mechanistic interpretability, especially for novel agents.
In this study, we present a hybrid QSAR framework that integrates mechanistically relevant descriptors derived from density functional theory (DFT) and molecular docking simulations with conventional physicochemical features to predict LD50 of OP nerve agents. The key mechanistic descriptors include acetylcholinesterase (AChE) binding affinity and serine phosphorylation interaction energy, capturing distinct toxicodynamic phases of nerve agent action.
We evaluate both linear regression and random forest models to assess predictive performance and interpretability. Cross-validation confirms that incorporating mechanistic features modestly improves accuracy and generalizability. Feature importance analysis identifies interaction energy as the most influential predictor, aligning with the irreversible inhibition mechanism of AChE.
Importantly, the model is capable of predicting LD50 values for structurally untested agents, including GF and Novichok compounds, thereby extending its utility to substances lacking experimental data. This study highlights the potential of mechanistically grounded in silico methods as an ethically sound and scalable alternative to animal testing for acute toxicity assessment. By aligning with regulatory needs for interpretable and reproducible predictions, the proposed approach contributes to integrated testing strategies, and new approach methodologies in computational toxicology.
{"title":"Hybrid QSAR modeling of LD50 in organophosphorus nerve agents: a mechanistic approach using DFT and molecular docking","authors":"Youngchan Jang , Jeongyun Kim , Doo-Hee Lee , Jin Yoo , Jeongwan Park , Ku Kang","doi":"10.1016/j.comtox.2025.100363","DOIUrl":"10.1016/j.comtox.2025.100363","url":null,"abstract":"<div><div>Chemical warfare agents (CWAs), particularly organophosphorus (OP) nerve agents, are among the most toxic and persistent compounds known, posing significant threats to human health and security. Experimental determination of their median lethal dose (LD<sub>50</sub>) values is limited by ethical, biosafety, and accessibility constraints. While conventional QSAR models provide useful approximations, they often lack mechanistic interpretability, especially for novel agents.</div><div>In this study, we present a hybrid QSAR framework that integrates mechanistically relevant descriptors derived from density functional theory (DFT) and molecular docking simulations with conventional physicochemical features to predict LD<sub>50</sub> of OP nerve agents. The key mechanistic descriptors include acetylcholinesterase (AChE) binding affinity and serine phosphorylation interaction energy, capturing distinct toxicodynamic phases of nerve agent action.</div><div>We evaluate both linear regression and random forest models to assess predictive performance and interpretability. Cross-validation confirms that incorporating mechanistic features modestly improves accuracy and generalizability. Feature importance analysis identifies interaction energy as the most influential predictor, aligning with the irreversible inhibition mechanism of AChE.</div><div>Importantly, the model is capable of predicting LD<sub>50</sub> values for structurally untested agents, including GF and Novichok compounds, thereby extending its utility to substances lacking experimental data. This study highlights the potential of mechanistically grounded in silico methods as an ethically sound and scalable alternative to animal testing for acute toxicity assessment. By aligning with regulatory needs for interpretable and reproducible predictions, the proposed approach contributes to integrated testing strategies, and new approach methodologies in computational toxicology.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100363"},"PeriodicalIF":3.1,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298564","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}
Pub Date : 2025-06-01DOI: 10.1016/j.comtox.2025.100361
Sarder Arifuzzaman MS , Md. Harun-Or-Rashid PhD , Farhina Rahman Laboni M. Pharm. , Mst Reshma Khatun MS , Nargis Sultana Chowdhury PhD
Liver X Receptors (LXRs) play a critical role in regulating lipid metabolism and inflammation, with their altered activity linked to several metabolic diseases. Although several LXR agonists have been identified, their clinical use has been limited due to adverse effects. In this study, we first leveraged multiple biological data repositories (including RNA-seq, Human Protein Atlas, DisGeNET, and WebGestalt) to examine the expression of LXRs at both the mRNA and protein levels across various tissues. We performed network and pathway analyses to redefine the physiological roles and disease associations of LXRs. Our findings emphasize the diverse functions of LXRs and highlight the potential for small molecules to pharmacologically modulate LXR activity for therapeutic purposes. In the second phase, we conducted an in-silico search for novel LXR modulators, beginning with molecular docking studies of eleven ligands that have been previously tested in preclinical or clinical settings. Based on docking scores and chemico-pharmacokinetic properties, we identified T0901317 and AZ876 as leading candidates, showing the highest binding affinity for LXR-α and LXR-β, respectively. In the final step, we extended our screening to discover new LXR ligands guided by the chemical structures of T0901317 and AZ876. Our docking and molecular dynamics (MD) simulations revealed that ZINC000095464663 and ZINC000021912925 exhibited the strongest binding affinities, alongside favorable pharmacokinetic profiles for both LXR subtypes. In conclusion, our in-silico approach, combining network analysis, virtual screening, molecular docking, MD simulations, and chemico-pharmacokinetic assessments, has uncovered two promising ligands for oral administration, offering potential for future therapeutic interventions targeting LXRs.
肝X受体（LXRs）在调节脂质代谢和炎症中起关键作用，其活性的改变与几种代谢性疾病有关。虽然已经确定了几种LXR激动剂，但由于其不良反应，其临床应用受到限制。在这项研究中，我们首先利用多个生物数据库（包括RNA-seq， Human Protein Atlas， DisGeNET和WebGestalt）来检查LXRs在mRNA和蛋白质水平上在不同组织中的表达。我们进行了网络和通路分析，以重新定义LXRs的生理作用和疾病关联。我们的研究结果强调了LXR的多种功能，并强调了小分子药物调节LXR活性以达到治疗目的的潜力。在第二阶段，我们进行了新型LXR调节剂的计算机搜索，从先前在临床前或临床环境中测试过的11种配体的分子对接研究开始。基于对接评分和化学药代动力学特性，我们确定T0901317和AZ876分别对LXR-α和LXR-β具有最高的结合亲和力。在最后一步，我们扩展了我们的筛选，以T0901317和AZ876的化学结构为导向，发现新的LXR配体。我们的对接和分子动力学（MD）模拟显示，ZINC000095464663和ZINC000021912925具有最强的结合亲和力，并且具有良好的药代动力学特征。总之，我们的计算机方法结合了网络分析、虚拟筛选、分子对接、MD模拟和化学药代动力学评估，发现了两种有前景的口服配体，为未来针对LXRs的治疗干预提供了潜力。
{"title":"Revisiting the Role of Liver X Receptors (LXRs) in Disease: In-silico Discovery of Novel Modulators Through Molecular Docking and Chemico-Pharmacokinetic Profiling","authors":"Sarder Arifuzzaman MS , Md. Harun-Or-Rashid PhD , Farhina Rahman Laboni M. Pharm. , Mst Reshma Khatun MS , Nargis Sultana Chowdhury PhD","doi":"10.1016/j.comtox.2025.100361","DOIUrl":"10.1016/j.comtox.2025.100361","url":null,"abstract":"<div><div>Liver X Receptors (LXRs) play a critical role in regulating lipid metabolism and inflammation, with their altered activity linked to several metabolic diseases. Although several LXR agonists have been identified, their clinical use has been limited due to adverse effects. In this study, we first leveraged multiple biological data repositories (including RNA-seq, Human Protein Atlas, DisGeNET, and WebGestalt) to examine the expression of LXRs at both the mRNA and protein levels across various tissues. We performed network and pathway analyses to redefine the physiological roles and disease associations of LXRs. Our findings emphasize the diverse functions of LXRs and highlight the potential for small molecules to pharmacologically modulate LXR activity for therapeutic purposes. In the second phase, we conducted an in-silico search for novel LXR modulators, beginning with molecular docking studies of eleven ligands that have been previously tested in preclinical or clinical settings. Based on docking scores and chemico-pharmacokinetic properties, we identified T0901317 and AZ876 as leading candidates, showing the highest binding affinity for LXR-α and LXR-β, respectively. In the final step, we extended our screening to discover new LXR ligands guided by the chemical structures of T0901317 and AZ876. Our docking and molecular dynamics (MD) simulations revealed that ZINC000095464663 and ZINC000021912925 exhibited the strongest binding affinities, alongside favorable pharmacokinetic profiles for both LXR subtypes. In conclusion, our in-silico approach, combining network analysis, virtual screening, molecular docking, MD simulations, and chemico-pharmacokinetic assessments, has uncovered two promising ligands for oral administration, offering potential for future therapeutic interventions targeting LXRs.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100361"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212197","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}
Pub Date : 2025-06-01DOI: 10.1016/j.comtox.2025.100352
Martin van den Berg PhD (Editor-in-Chief, Regulatory Toxicology Pharmacology Current Opinion in Toxicology) , Daniel R. Dietrich PhD (Editor-in-Chief, Chemico-Biological Interactions, Computational Toxicology, Journal of Toxicology and Regulatory Policy) , Sonja von Aulock PhD (Editor-in-Chief, ALTEX – Alternatives to Animal Experimentation) , Anna Bal-Price PhD (Editor-in-Chief, Reproductive Toxicology) , Michael D. Coleman PhD (Editor-in-Chief, Environmental Toxicology and Pharmacology) , Mark T.D. Cronin PhD (Editor-in-Chief, Computational Toxicology) , Paul Jennings PhD (Editor-in-Chief, Toxicology in Vitro) , Angela Mally PhD (Editor-in-Chief, Toxicology Letters) , Mathieu Vinken PhD (Editor-in-Chief, Toxicology NAM Journal) , Matthew C. Wright PhD (Editor-in-Chief, Food and Chemical Toxicology)
{"title":"The Alarming Consequences of Workforce Reductions at the FDA, EPA, NIH and CDC in the United States","authors":"Martin van den Berg PhD (Editor-in-Chief, Regulatory Toxicology Pharmacology Current Opinion in Toxicology) , Daniel R. Dietrich PhD (Editor-in-Chief, Chemico-Biological Interactions, Computational Toxicology, Journal of Toxicology and Regulatory Policy) , Sonja von Aulock PhD (Editor-in-Chief, ALTEX – Alternatives to Animal Experimentation) , Anna Bal-Price PhD (Editor-in-Chief, Reproductive Toxicology) , Michael D. Coleman PhD (Editor-in-Chief, Environmental Toxicology and Pharmacology) , Mark T.D. Cronin PhD (Editor-in-Chief, Computational Toxicology) , Paul Jennings PhD (Editor-in-Chief, Toxicology in Vitro) , Angela Mally PhD (Editor-in-Chief, Toxicology Letters) , Mathieu Vinken PhD (Editor-in-Chief, Toxicology NAM Journal) , Matthew C. Wright PhD (Editor-in-Chief, Food and Chemical Toxicology)","doi":"10.1016/j.comtox.2025.100352","DOIUrl":"10.1016/j.comtox.2025.100352","url":null,"abstract":"","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100352"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279466","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}
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