Computational Toxicology最新文献_第2页

Nanoinformatics: Emerging technology for prediction and controlling of biological performance of nanomedicines 纳米信息学：预测和控制纳米药物生物性能的新兴技术

IF 2.9

Computational Toxicology Pub Date : 2025-09-01 DOI: 10.1016/j.comtox.2025.100378

Anjana Sharma , Zubina Anjum , Khalid Raza , Nitin Sharma , Balak Das Kurmi

{"title":"Nanoinformatics: Emerging technology for prediction and controlling of biological performance of nanomedicines","authors":"Anjana Sharma , Zubina Anjum , Khalid Raza , Nitin Sharma , Balak Das Kurmi","doi":"10.1016/j.comtox.2025.100378","DOIUrl":"10.1016/j.comtox.2025.100378","url":null,"abstract":"<div><div>The nanoinformatics provides a platform to refine the nanotechnology approach by controlling the parameters based on the previous informations. Nanoinformatics helps the research community by leveraging sophisticated algorithms and complex computational modeling to predict the essential properties of nanomedicine and ensure their optimal biological interaction and performance. There are numerous potential roles of nanoinformatics in enhancing therapeutic value and preventing unpredictable toxicological pathways of nanomedicine. This review article delves into the pivotal applications of various computational tools to optimize the biological behavior of nanomedicine by controlling their physicochemical characteristics. This review thus offers an insight into adequately comprehending the <em>in silico</em> models such as nano-QSAR, MD simulations, CGMD and Brownian simulations to optimize nanomedicine. These tools help in product development by reducing the cost and time by controlling several biological responses of nanomedicines, including their protein interaction, mitigation, extravasation, receptor interaction and toxicological responses.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100378"},"PeriodicalIF":2.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010073","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}

引用次数: 0

Development of mathematical new approach methods to assess chemical mixtures 发展新的数学方法来评估化学混合物

IF 2.9

Computational Toxicology Pub Date : 2025-08-21 DOI: 10.1016/j.comtox.2025.100376

R. Broughton , M. Feshuk , Z. Stanfield , K.K. Isaacs , K. Paul Friedman

{"title":"Development of mathematical new approach methods to assess chemical mixtures","authors":"R. Broughton , M. Feshuk , Z. Stanfield , K.K. Isaacs , 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 log<sub>10</sub>-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":"2025-08-21","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}

引用次数: 0

Conservative consensus QSAR approach for the prediction of rat acute oral toxicity 保守共识QSAR方法预测大鼠急性口服毒性

IF 2.9

Computational Toxicology Pub Date : 2025-08-19 DOI: 10.1016/j.comtox.2025.100374

Jerry Achar , James W. Firman , Mark T.D. Cronin

{"title":"Conservative consensus QSAR approach for the prediction of rat acute oral toxicity","authors":"Jerry Achar , James W. Firman , Mark T.D. Cronin","doi":"10.1016/j.comtox.2025.100374","DOIUrl":"10.1016/j.comtox.2025.100374","url":null,"abstract":"<div><div>Consensus approaches are applied in different quantitative structure–activity relationship (QSAR) modeling contexts based on the assumption that combining individual model predictions will improve prediction reliability. This study evaluated the performance of TEST, CATMoS and VEGA models for prediction of oral rat LD<sub>50</sub>, both individually and in consensus, across a dataset of 6,229 organic compounds. Predicted LD<sub>50</sub> values from the models were compared for each compound, and the lowest value was assigned as the output of the conservative consensus model (CCM). Predictive accuracy was then evaluated based on the agreement of predicted LD<sub>50</sub>-based GHS category assignments with those derived experimentally. The aim was to allow for the most conservative value to be identified. Results showed that CCM had the highest over-prediction rate at 37 %, compared to TEST (24 %), CATMoS (25 %) and VEGA (8 %). Meanwhile, its under-prediction rate was lowest at 2 %, relative to TEST (20 %), CATMoS (10 %) and VEGA (5 %). Due to the method applied, CCM was the most conservative across all GHS categories. Further, structural analysis demonstrated that no specific chemical classes or functional groups were consistently underpredicted or overpredicted. The utility of CCM lies in its ability to establish a foundation for contextualizing the general use of consensus modeling, in order to derive health-protective oral rat LD<sub>50</sub> estimates under conditions of uncertainty, especially where experimental data are limited or absent.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100374"},"PeriodicalIF":2.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879907","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}

引用次数: 0

Machine learning-based structural analysis of OATP1B1 interactors/non-interactors: Discriminating toxic and non-toxic alerts for transporter-mediated toxicity 基于机器学习的OATP1B1相互作用物/非相互作用物的结构分析：对转运蛋白介导的毒性的毒性和无毒警报的区分

IF 2.9

Computational Toxicology Pub Date : 2025-08-08 DOI: 10.1016/j.comtox.2025.100373

Shovanlal Gayen , Indrasis Dasgupta , Balaram Ghosh , Insaf Ahmed Qureshi , Partha Pratim Roy

{"title":"Machine learning-based structural analysis of OATP1B1 interactors/non-interactors: Discriminating toxic and non-toxic alerts for transporter-mediated toxicity","authors":"Shovanlal Gayen , Indrasis Dasgupta , Balaram Ghosh , Insaf Ahmed Qureshi , 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":"2025-08-08","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}

引用次数: 0

On the comparability between studies in predictive ecotoxicology 预测生态毒理学研究的可比性

IF 2.9

Computational Toxicology Pub Date : 2025-08-07 DOI: 10.1016/j.comtox.2025.100367

Christoph Schür , Kristin Schirmer , Marco Baity-Jesi

{"title":"On the comparability between studies in predictive ecotoxicology","authors":"Christoph Schür , Kristin Schirmer , 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":"2025-08-07","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}

引用次数: 0

In silico analyses as a tool for regulatory assessment of protein digestibility: Where are we? 计算机分析作为蛋白质消化率调节评估的工具：我们在哪里？

IF 2.9

Computational Toxicology Pub Date : 2025-08-05 DOI: 10.1016/j.comtox.2025.100372

Fernando Rivero-Pino, Caroline Idowu, Hannes Malfroy, Diana Rueda, Hannah Lester

{"title":"In silico analyses as a tool for regulatory assessment of protein digestibility: Where are we?","authors":"Fernando Rivero-Pino, Caroline Idowu, Hannes Malfroy, Diana Rueda, 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":"2025-08-05","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}

引用次数: 0

Part I. Systematic development of machine learning models for predicting mechanism-based toxicity from in vitro ToxCast bioassay data 第一部分系统开发机器学习模型，用于从体外ToxCast生物测定数据预测基于机制的毒性

IF 2.9

Computational Toxicology Pub Date : 2025-07-25 DOI: 10.1016/j.comtox.2025.100371

Donghyeon Kim , Siyeol Ahn , Jiyong Jeong, Jinhee Choi

{"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}

引用次数: 0

Screening for genotoxicants in food: A data-driven approach using food composition data and machine learning based in silico models 筛选食品中的基因毒物：使用食品成分数据和基于计算机模型的机器学习的数据驱动方法

IF 2.9

Computational Toxicology Pub Date : 2025-07-24 DOI: 10.1016/j.comtox.2025.100370

Jakob Menz, Bernd Schäfer

{"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}

引用次数: 0

The FAIR AOP roadmap for 2025: Advancing findability, accessibility, interoperability, and re-usability of adverse outcome pathways 2025年的FAIR AOP路线图：提高不利结果路径的可查找性、可访问性、互操作性和可重用性

IF 2.9

Computational Toxicology Pub Date : 2025-07-21 DOI: 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

{"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}

引用次数: 0

Part II. Systematic development of machine learning models for predicting human and ecotoxicity from in vivo OECD test guideline data 第二部分。系统开发机器学习模型，用于从体内OECD测试指南数据预测人类和生态毒性

IF 3.1

Computational Toxicology Pub Date : 2025-07-17 DOI: 10.1016/j.comtox.2025.100369

Donghyeon Kim , Jiyong Jeong , Siyeol Ahn, Jinhee Choi

{"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}

引用次数: 0