Computational Toxicology最新文献_第6页

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 : 2025-09-01 Epub 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":"2025-09-01","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

Hybrid QSAR modeling of LD50 in organophosphorus nerve agents: a mechanistic approach using DFT and molecular docking 有机磷神经毒剂中LD50的混合QSAR建模：一种使用DFT和分子对接的机制方法

IF 3.1

Computational Toxicology Pub Date : 2025-09-01 Epub Date: 2025-06-14 DOI: 10.1016/j.comtox.2025.100363

Youngchan Jang , Jeongyun Kim , Doo-Hee Lee , Jin Yoo , Jeongwan Park , Ku Kang

{"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":"2025-09-01","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}

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 : 2025-09-01 Epub 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":"2025-09-01","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

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 Epub Date: 2025-09-02 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

Revisiting the Role of Liver X Receptors (LXRs) in Disease: In-silico Discovery of Novel Modulators Through Molecular Docking and Chemico-Pharmacokinetic Profiling 重新审视肝脏X受体（LXRs）在疾病中的作用：通过分子对接和化学药代动力学分析的新型调节剂的硅发现

IF 3.1

Computational Toxicology Pub Date : 2025-06-01 DOI: 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

{"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}

引用次数: 0

Prediction of progesterone receptor binding potency, agonism and antagonism using machine learning models 使用机器学习模型预测黄体酮受体结合效力、激动作用和拮抗作用

IF 3.1

Computational Toxicology Pub Date : 2025-06-01 Epub Date: 2025-05-11 DOI: 10.1016/j.comtox.2025.100351

Nemanja Milošević , Nataša Sukur Milošević , Svetlana Fa Nedeljkovic , Bojana Stanic , Nebojsa Andric

{"title":"Prediction of progesterone receptor binding potency, agonism and antagonism using machine learning models","authors":"Nemanja Milošević , Nataša Sukur Milošević , Svetlana Fa Nedeljkovic , Bojana Stanic , Nebojsa Andric","doi":"10.1016/j.comtox.2025.100351","DOIUrl":"10.1016/j.comtox.2025.100351","url":null,"abstract":"<div><div>The use of Machine Learning (ML) models to predict the binding potency of chemicals to estrogen and androgen receptors has become well-established, helping in the prioritization of chemicals for endocrine disruption testing. However, the potential of ML models for other endocrine targets, such as the progesterone receptor (PR), remains underexplored. In this study, we developed an ML model to predict PR binding affinity and assess the agonistic/antagonistic properties of chemicals. The model achieved a training accuracy of 99.72% and a validation accuracy of 74.46%. External validation was conducted on a dataset of approximately 10,000 chemicals, including 5720 compounds from the training set for which there is a known outcome. External predictions aligned closely with experimental <em>in vitro</em> data, achieving an accuracy of 96.85%. Additionally, the model successfully predicted PR binding affinity and agonistic/antagonistic properties for chemicals without available experimental data. In summary, this study highlights the potential of ML as an effective tool for prioritizing chemicals for future <em>in vitro</em> and <em>in vivo</em> testing of PR binding potency and agonistic/antagonistic properties of chemicals.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100351"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068772","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

Towards a quantitative adverse outcome pathway for liver carcinogenesis: From proliferation to prediction 肝癌发生不良后果的定量途径：从增殖到预测

IF 3.1

Computational Toxicology Pub Date : 2025-06-01 DOI: 10.1016/j.comtox.2025.100359

Christina H.J. Veltman , Hiba Khalidi , Elias Zgheib , Bob van de Water , Mirjam Luijten , Jeroen L.A. Pennings

{"title":"Towards a quantitative adverse outcome pathway for liver carcinogenesis: From proliferation to prediction","authors":"Christina H.J. Veltman , Hiba Khalidi , Elias Zgheib , Bob van de Water , Mirjam Luijten , Jeroen L.A. Pennings","doi":"10.1016/j.comtox.2025.100359","DOIUrl":"10.1016/j.comtox.2025.100359","url":null,"abstract":"<div><div>Hazard assessment of non-genotoxic carcinogens could greatly benefit from next generation risk assessment approaches, driven by the multitude of mechanisms through which non-genotoxic carcinogens operate. One method for structuring new approach methodology-derived data is the adverse outcome pathway (AOP) concept. Currently, mostly qualitative AOPs are described, limiting their application for regulatory decision making. In contrast, quantitative AOPs use mathematical terms to describe the relationships between key events (KEs), allowing for the derivation of a Point of Departure (PoD). Here, we report quantification of the key event relationship (KER) between sustained hepatocyte proliferation and liver tumour formation, two KEs of AOP#220 relating to CYP2E1 activation leading to liver cancer. We use incidence of histopathological lesions indicative of proliferation, as well as BrdU labelling obtained from existing sub-chronic toxicity studies in rats, to quantify proliferation. For liver cancer, incidences of hepatocellular adenoma and carcinoma from 2-year rodent carcinogenicity studies were collected. Data for both KEs were combined to calibrate a response-response model, and Bayesian logistic regression analysis was applied to obtain predictions and credible intervals for carcinogenicity. Proliferative lesion incidence was observed to be a highly specific, yet insensitive predictor, and combining this with BrdU labelling yields more accurate predictions of carcinogenicity. Importantly, we demonstrate that for most of the chemicals tested, inclusion of BrdU labelling returns more precise predicted benchmark dose intervals for PoD derivation. To further explore this quantitative KER and its regulatory application, we propose to include and standardize BrdU labelling for sub-chronic toxicity studies performed for regulatory purposes.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100359"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253717","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

An R-based predictive model for skin-sensitizing potential of substances with known structures 一种基于r的已知结构物质致敏电位预测模型

IF 3.1

Computational Toxicology Pub Date : 2025-06-01 Epub Date: 2025-05-11 DOI: 10.1016/j.comtox.2025.100350

Yuri Hatakeyama , Kosuke Imai , Hayato Nishida , Shiho Oeda , Tomomi Atobe , Morihiko Hirota

{"title":"An R-based predictive model for skin-sensitizing potential of substances with known structures","authors":"Yuri Hatakeyama , Kosuke Imai , Hayato Nishida , Shiho Oeda , Tomomi Atobe , Morihiko Hirota","doi":"10.1016/j.comtox.2025.100350","DOIUrl":"10.1016/j.comtox.2025.100350","url":null,"abstract":"<div><div>Evaluation of skin-sensitizing potential is important to confirm the safety of cosmetics. As animal testing is no longer permitted, several alternative methods based on the adverse outcome pathway (AOP) approach have been reported. In addition, integrated approaches to testing and assessment (IATA), which combine the results of multiple alternative methods to assess skin sensitization potential, have been developed. We have reported an artificial neural network (ANN) model for sensitization risk assessment using commercial software, QwikNet. In the present study, we constructed a new sensitization prediction model for substances with known structures using the free and open-source software R for statistical analysis, and compared the results with those of the QwikNet model. The R model was confirmed to show similar predictive performance for estimated concentration three (EC3) which is the concentration of a test substance needed to produce a stimulation index of 3 to the QwikNet model on the same training set of 134 compounds. The accuracy, overpredicted rate, and underpredicted rate of the R model were 81.3%, 10.4%, and 8.2%, respectively, versus 79.9%, 10.4%, and 9.7% for the QwikNet model. In case studies of compounds not included in the training set, the R model showed generally good predictive ability. For less-well-predicted substances, additional <em>in silico</em> and read-across evaluations complemented the ANN model and improved the predictive accuracy. This study demonstrates that the ANN model is portable to the R software system. Furthermore, the combination of ANN prediction with <em>in silico</em> predictions and read-across taking account of substructures improves the prediction of skin-sensitizing potential in a weight-of-evidence approach.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100350"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089788","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 Alarming Consequences of Workforce Reductions at the FDA, EPA, NIH and CDC in the United States 美国FDA、EPA、NIH和CDC裁员的惊人后果

IF 3.1

Computational Toxicology Pub Date : 2025-06-01 Epub Date: 2025-05-02 DOI: 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)

引用次数: 0

A multicomponent similarity approach to identify potential substances of very high concern 一种多组分相似度方法，用于识别高度关注的潜在物质

IF 3.1

Computational Toxicology Pub Date : 2025-06-01 Epub Date: 2025-04-02 DOI: 10.1016/j.comtox.2025.100343

Yordan Yordanov , Emiel Rorije , Jordi Minnema , Thimo Schotman , Willie J.G.M. Peijnenburg , Pim N.H. Wassenaar

{"title":"A multicomponent similarity approach to identify potential substances of very high concern","authors":"Yordan Yordanov , Emiel Rorije , Jordi Minnema , Thimo Schotman , Willie J.G.M. Peijnenburg , Pim N.H. Wassenaar","doi":"10.1016/j.comtox.2025.100343","DOIUrl":"10.1016/j.comtox.2025.100343","url":null,"abstract":"<div><div>The number of chemicals being placed on the market is increasing. As such, there is an increased need for screening and evaluation of chemical hazards and risks. Particularly, chemicals with intrinsic properties that are considered of very high concern are ideally identified and regulated before wide-spread use and exposure. The use of <em>in silico</em> tools can help to identify potential substances of very high concern (SVHCs).</div><div>Earlier, predictive models have been developed that identify potential SVHCs based on global structural similarity to known SVHCs. Here in this study, these read-across similarity models have been extended with other similarity modules, including toxicophore, biological and physicochemical similarity.</div><div>The newly developed SVHC similarity profiles do individually not outperform the existing global similarity model. However, combining these new modules in an extended similarity approach results in more comprehensive predictions and allows for improved interpretability and applicability to the broader chemical universe. As such, this new approach is thought to support model users in interpretation of the model-prediction, and can thereby contribute to better screening and prioritization of potential SVHCs.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100343"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792157","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