Pub Date : 2025-11-11DOI: 10.1016/j.comtox.2025.100391
Jake Muldoon , Holger Moustakas , Terry W. Schultz , Trevor M. Penning , Amanda Bryant-Friedrich , Danielle J. Botelho , Anne Marie Api
The Research Institute for Fragrance Materials, Inc. (RIFM) has developed a robust, reliable, reproducible method for clustering chemicals based on their structural signatures and deriving structure–activity groups. This method facilitates the institutionalization of knowledge gained from manually assessing thousands of chemical pairings of fragrance ingredients. The technique improves accuracy, consistency, transparency, and explainability for evaluating chemical safety while reducing reliance on expert judgment and any associated bias. A material’s signature-based structure–activity group is created via a top-down approach using standardized signature trees based on Indicator Phrases (IPs) representing seminal sub-structural features. We have applied the approach to over 6,000 discrete fragrances and fragrance-like organic chemicals (e.g. organic compounds of the chemical classes described in the inventory such as aldehyde, ketone, esters, etc.), and it has been shown to perform well for various properties and parameters observed in this chemical space. The signature trees are adaptable and can be expanded for IPs not found in fragrance materials. The structure–activity groups readily allow for transparent and repeatable separation of an inventory of thousands of chemicals into clusters of chemicals that share the same IPs. Adjacent groups that share all but one or two of the same IPs can be identified, thereby effortlessly expanding the range of potential read-across source substances. With its ease of interpretation, the system facilitates discussions among scientists with different levels of chemical knowledge. In addition to clustering for data-gap filling through read-across, other applications include prioritization for testing and predictive toxicology by encoding IPs using various machine-learning techniques.
{"title":"Advancing chemical grouping: development and application of signature-based structure-activity groups for non-animal safety assessments","authors":"Jake Muldoon , Holger Moustakas , Terry W. Schultz , Trevor M. Penning , Amanda Bryant-Friedrich , Danielle J. Botelho , Anne Marie Api","doi":"10.1016/j.comtox.2025.100391","DOIUrl":"10.1016/j.comtox.2025.100391","url":null,"abstract":"<div><div>The Research Institute for Fragrance Materials, Inc. (RIFM) has developed a robust, reliable, reproducible method for clustering chemicals based on their structural signatures and deriving structure–activity groups. This method facilitates the institutionalization of knowledge gained from manually assessing thousands of chemical pairings of fragrance ingredients. The technique improves accuracy, consistency, transparency, and explainability for evaluating chemical safety while reducing reliance on expert judgment and any associated bias. A material’s signature-based structure–activity group is created via a top-down approach using standardized signature trees based on Indicator Phrases (IPs) representing seminal sub-structural features. We have applied the approach to over 6,000 discrete fragrances and fragrance-like organic chemicals (e.g. organic compounds of the chemical classes described in the inventory such as aldehyde, ketone, esters, etc.), and it has been shown to perform well for various properties and parameters observed in this chemical space. The signature trees are adaptable and can be expanded for IPs not found in fragrance materials. The structure–activity groups readily allow for transparent and repeatable separation of an inventory of thousands of chemicals into clusters of chemicals that share the same IPs. Adjacent groups that share all but one or two of the same IPs can be identified, thereby effortlessly expanding the range of potential read-across source substances. With its ease of interpretation, the system facilitates discussions among scientists with different levels of chemical knowledge. In addition to clustering for data-gap filling through read-across, other applications include prioritization for testing and predictive toxicology by encoding IPs using various machine-learning techniques.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100391"},"PeriodicalIF":2.9,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568328","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}
Accurate prediction of protein toxicity is paramount in various fields, ranging from pharmaceutical drug development to environmental risk assessment, as it allows for early identification and mitigation of potentially harmful effects associated with protein exposure. Cardiotoxicity, enterotoxicity, and neurotoxicity are critical concerns that demand rigorous assessment during the early stages of drug development. This study addresses the need for accurate prediction models to identify proteins and peptides with potential cardiotoxic, enterotoxic, or neurotoxic effects. By leveraging machine learning (ML) techniques (support vector machine (SVM), random forest (RF), k-nearest neighbour (kNN)), and comprehensive datasets encompassing a wide range of molecular features, robust prediction models were developed to reliably classify proteins and peptides based on their potential toxicity profiles. The models integrate diverse features, including amino acid composition (Compo), conjoint-triads (CTriad), composition-transition-distribution (CTD), and physicochemical n-gram properties (PnGT) derived from protein primary sequences, enabling holistic analysis of the toxicity potential of the molecules. Various models were developed using isolated feature sets and combinations of four feature sets. The RF model consistently outperforms the other models in toxicity prediction, with the Compo + CTriad + CTD feature set being recommended because of its ability to capture intricate molecular interactions and structural details. The proposed model, Proteintox, balances detailed structural insights with practicalities, enhancing its ability to assess impacts involving molecular interactions. It delivers high accuracy, sensitivity, and specificity across all testing scenarios while remaining computationally efficient and interpretable. The study also highlights the significance of selecting appropriate feature sets to enhance model performance without increasing complexity, demonstrating that adding more features does not always translate to improved predictive ability. The significance of this work lies in its potential to streamline the drug discovery process by providing early toxicity predictions, thus reducing the reliance on costly and time-consuming experimental assays. The data and source code are available at https://github.com/PGlab-NIPER/Proteintox.
{"title":"Proteintox: A multifaceted machine learning strategy for identifying cardiotoxic, neurotoxic, and enterotoxic proteins","authors":"Pradnya Kamble , Anju Sharma , Aritra Banerjee , Shubham Pandey, Prabha Garg","doi":"10.1016/j.comtox.2025.100390","DOIUrl":"10.1016/j.comtox.2025.100390","url":null,"abstract":"<div><div>Accurate prediction of protein toxicity is paramount in various fields, ranging from pharmaceutical drug development to environmental risk assessment, as it allows for early identification and mitigation of potentially harmful effects associated with protein exposure. Cardiotoxicity, enterotoxicity, and neurotoxicity are critical concerns that demand rigorous assessment during the early stages of drug development. This study addresses the need for accurate prediction models to identify proteins and peptides with potential cardiotoxic, enterotoxic, or neurotoxic effects. By leveraging machine learning (ML) techniques (support vector machine (SVM), random forest (RF), k-nearest neighbour (kNN)), and comprehensive datasets encompassing a wide range of molecular features, robust prediction models were developed to reliably classify proteins and peptides based on their potential toxicity profiles. The models integrate diverse features, including amino acid composition (Compo), conjoint-triads (CTriad), composition-transition-distribution (CTD), and physicochemical n-gram properties (PnGT) derived from protein primary sequences, enabling holistic analysis of the toxicity potential of the molecules. Various models were developed using isolated feature sets and combinations of four feature sets. The RF model consistently outperforms the other models in toxicity prediction, with the Compo + CTriad + CTD feature set being recommended because of its ability to capture intricate molecular interactions and structural details. The proposed model, Proteintox, balances detailed structural insights with practicalities, enhancing its ability to assess impacts involving molecular interactions. It delivers high accuracy, sensitivity, and specificity across all testing scenarios while remaining computationally efficient and interpretable. The study also highlights the significance of selecting appropriate feature sets to enhance model performance without increasing complexity, demonstrating that adding more features does not always translate to improved predictive ability. The significance of this work lies in its potential to streamline the drug discovery process by providing early toxicity predictions, thus reducing the reliance on costly and time-consuming experimental assays. The data and source code are available at <span><span>https://github.com/PGlab-NIPER/Proteintox</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100390"},"PeriodicalIF":2.9,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467010","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 : 2025-10-30DOI: 10.1016/j.comtox.2025.100389
Céline Mare , Arnaud Tête , Sylvie Bortoli , Brigitte Vi-Fane , Sylvie Babajko , Ali Nassif
Background
Pesticide exposure during pregnancy has been proposed as a potential environmental risk factor for the development of cleft lip and palate (CLP). Several epidemiological studies have investigated this association, but results remain inconsistent.
Objective
This systematic review aimed to critically assess the evidence from human, animal, and in vitro studies regarding the potential link between pesticide exposure and CLP.
Methods
A comprehensive search was conducted in PubMed, Embase, and the Cochrane Library from January 1980 to June 2024, using standardized search terms combining descriptors related to pesticides and CLP. A total of 217 records were retrieved (189 from PubMed, 28 from Embase, and 0 from the Cochrane Library). After removing 61 duplicates, titles and abstracts were screened, and 87 studies were selected for full-text review. Finally, 47 articles were included in the review, including 20 epidemiological investigations in humans, 25 experimental studies in animal models (rodents and simians), and 3 in vitro investigations relevant to craniofacial development. The risk of bias for both observational and experimental studies was independently assessed using the JBI Critical Appraisal Tools developed by the Joanna Briggs Institute.
Results
Human epidemiological studies provided mixed results, whereas animal and in vitro studies supported a causal role for pesticide exposure in CLP. The quality assessment revealed methodological heterogeneity and varying levels of bias across studies.
Conclusions
The available evidence suggests that pesticide exposure may contribute to the risk of CLP, although results from human studies remain inconsistent. Further large-scale, well-designed studies are required to confirm these associations and to clarify dose–response relationships and underlying mechanisms.
{"title":"Pesticides and cleft lip/palate: A state-of-the-art review and analysis of epidemiologic evidence","authors":"Céline Mare , Arnaud Tête , Sylvie Bortoli , Brigitte Vi-Fane , Sylvie Babajko , Ali Nassif","doi":"10.1016/j.comtox.2025.100389","DOIUrl":"10.1016/j.comtox.2025.100389","url":null,"abstract":"<div><h3>Background</h3><div>Pesticide exposure during pregnancy has been proposed as a potential environmental risk factor for the development of cleft lip and palate (CLP). Several epidemiological studies have investigated this association, but results remain inconsistent.</div></div><div><h3>Objective</h3><div>This systematic review aimed to critically assess the evidence from human, animal, and <em>in vitro</em> studies regarding the potential link between pesticide exposure and CLP.</div></div><div><h3>Methods</h3><div>A comprehensive search was conducted in PubMed, Embase, and the Cochrane Library from January 1980 to June 2024, using standardized search terms combining descriptors related to pesticides and CLP. A total of 217 records were retrieved (189 from PubMed, 28 from Embase, and 0 from the Cochrane Library). After removing 61 duplicates, titles and abstracts were screened, and 87 studies were selected for full-text review. Finally, 47 articles were included in the review, including 20 epidemiological investigations in humans, 25 experimental studies in animal models (rodents and simians), and 3 <em>in vitro</em> investigations relevant to craniofacial development. The risk of bias for both observational and experimental studies was independently assessed using the JBI Critical Appraisal Tools developed by the Joanna Briggs Institute.</div></div><div><h3>Results</h3><div>Human epidemiological studies provided mixed results, whereas animal and <em>in vitro</em> studies supported a causal role for pesticide exposure in CLP. The quality assessment revealed methodological heterogeneity and varying levels of bias across studies.</div></div><div><h3>Conclusions</h3><div>The available evidence suggests that pesticide exposure may contribute to the risk of CLP, although results from human studies remain inconsistent. Further large-scale, well-designed studies are required to confirm these associations and to clarify dose–response relationships and underlying mechanisms.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100389"},"PeriodicalIF":2.9,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466319","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}
The blood–brain barrier plays a critical role in maintaining the stability of the central nervous system, yet it also limits drug delivery. Existing machine learning (ML) and deep learning (DL) approaches for predicting blood–brain barrier permeability (BBBP) often face challenges such as class imbalance, scalability, and high computational demands. To address these limitations, this study aims to develop a novel Stacking Ensemble–Quantum Support Vector Machine (SEQSVM) model that integrates classical ensemble learners (AdaBoost, XGBoost, and CatBoost) with a quantum meta-learner (QSVM). The proposed hybrid model incorporates SMOTE + Tomek for effectively handling class imbalance and a customized quantum feature map for molecular fingerprint encoding. Experimental results on two benchmark BBBP datasets demonstrate that SEQSVM achieves 95.0 % accuracy on D1 (1970 samples) and 92.0 % on D2 (8153 samples), consistently outperforming classical ensemble models by 3–6 % in accuracy, sensitivity, and specificity. Compared to existing ML and DL models, SEQSVM offers a superior balance between accuracy, interpretability, and computational efficiency. It is a promising approach for BBBP prediction in real-world drug discovery applications.
{"title":"Blood–brain barrier permeability prediction via novel stacking classical-quantum hybrid model","authors":"Muhamad Akrom , Supriadi Rustad , Totok Sutojo , De Rosal Ignatius Moses Setiadi , Hermawan Kresno Dipojono , Ryo Maezono , Hideaki Kasai","doi":"10.1016/j.comtox.2025.100388","DOIUrl":"10.1016/j.comtox.2025.100388","url":null,"abstract":"<div><div>The blood–brain barrier plays a critical role in maintaining the stability of the central nervous system, yet it also limits drug delivery. Existing machine learning (ML) and deep learning (DL) approaches for predicting blood–brain barrier permeability (BBBP) often face challenges such as class imbalance, scalability, and high computational demands. To address these limitations, this study aims to develop a novel Stacking Ensemble–Quantum Support Vector Machine (SEQSVM) model that integrates classical ensemble learners (AdaBoost, XGBoost, and CatBoost) with a quantum <em>meta</em>-learner (QSVM). The proposed hybrid model incorporates SMOTE + Tomek for effectively handling class imbalance and a customized quantum feature map for molecular fingerprint encoding. Experimental results on two benchmark BBBP datasets demonstrate that SEQSVM achieves 95.0 % accuracy on D1 (1970 samples) and 92.0 % on D2 (8153 samples), consistently outperforming classical ensemble models by 3–6 % in accuracy, sensitivity, and specificity. Compared to existing ML and DL models, SEQSVM offers a superior balance between accuracy, interpretability, and computational efficiency. It is a promising approach for BBBP prediction in real-world drug discovery applications.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100388"},"PeriodicalIF":2.9,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417610","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}
Accurately modeling protein–ligand interactions is a central challenge in computational protein design and drug discovery. Traditional interaction fingerprint (IFP) approaches, while valuable, struggle to capture subtle binding features and adapt to diverse structural contexts. To address these limitations, we propose GITK, a deep learning framework that integrates a modified graph inductive bias transformer (GRIT) with Kolmogorov–Arnold networks (KANs) for interpretable interaction fingerprint prediction. GRIT introduces inductive bias to effectively represent both local and global graph structures of proteins and ligands, while KAN provides a principled functional decomposition that enhances nonlinear feature learning and interpretability. Benchmarking across multiple datasets demonstrates that GITK outperforms state-of-the-art models in binding affinity prediction, functional effect classification, and virtual screening. Moreover, GITK enables reliable selectivity analysis, successfully highlighting conformational differences and key residues in adenosine receptor subtypes, consistent with experimental findings such as the selectivity of the A1 antagonist DPCPX.
{"title":"A novel deep learning framework for predicting protein-ligand interaction fingerprints from sequence data: integrating graph inductive bias transformer with Kolmogorov-Arnold networks","authors":"Lixin Lei, Qianjin Guo, Wu Liu, Zijun Wang, Kaitai Han, Chaojing Shi, Zhenxing Li, Sichao Lu, Mengqiu Wang, Zhiwei Zhang, Ruoyan Dai, Zhenghui Wang, Xingyu Liu","doi":"10.1016/j.comtox.2025.100386","DOIUrl":"10.1016/j.comtox.2025.100386","url":null,"abstract":"<div><div>Accurately modeling protein–ligand interactions is a central challenge in computational protein design and drug discovery. Traditional interaction fingerprint (IFP) approaches, while valuable, struggle to capture subtle binding features and adapt to diverse structural contexts. To address these limitations, we propose <strong>GITK</strong>, a deep learning framework that integrates a modified graph inductive bias transformer (GRIT) with Kolmogorov–Arnold networks (KANs) for interpretable interaction fingerprint prediction. GRIT introduces inductive bias to effectively represent both local and global graph structures of proteins and ligands, while KAN provides a principled functional decomposition that enhances nonlinear feature learning and interpretability. Benchmarking across multiple datasets demonstrates that GITK outperforms state-of-the-art models in binding affinity prediction, functional effect classification, and virtual screening. Moreover, GITK enables reliable selectivity analysis, successfully highlighting conformational differences and key residues in adenosine receptor subtypes, consistent with experimental findings such as the selectivity of the A1 antagonist DPCPX.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100386"},"PeriodicalIF":2.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oxidative stress is identified as a primary factor contributing to the failure of renal function. The excessive generation of oxidative stress is observed in CKDu patients in many experiments. Agrochemicals are identified as a major inducer of oxidative stress. Oxidative stress is induced mainly by direct generation of ROS through enzyme activation and by depleting antioxidant enzymes. To study how toxic exposure to agrochemicals alters the oxidative stress level in CKDu, a mathematical model of the body’s Redox system was developed and simulated how toxic exposure to agrochemicals, particularly arsenic toxicity, increases oxidative stress in cells. This model was employed to study how the molecular mechanisms of ROS generation are affected in CKDu. The study explores how arsenic concentration levels alter the oxidative stress levels and molecular interactions involved. The model indicates that the mitochondrial electron transport chain complex III is the primary contributor to ROS production, which needs to be validated through wet lab experiments. Sensitivity analyses on the model revealed that parameters associated with superoxide production are susceptible to perturbations. Further analysis shows that enzyme-driven reactions, especially those involving superoxide generation, catalase, and glutathione peroxidase, are crucial in governing oxidative stress generation in CKDu. According to the sensitivity analysis results, both NOX (NADPH oxidase) and SOD2 (superoxide dismutase 2) appear to be promising drug targets.
{"title":"Modeling molecular level mechanisms of oxidative stress generation induced by agrochemicals in CKDu initiation","authors":"Samarawikcrama Wanni Arachchige Madushani Upamalika , Champi Thusangi Wannige , Sugandhima Mihirani Vidanagamachchi , Don Kulasiri , Mahesan Niranjan","doi":"10.1016/j.comtox.2025.100385","DOIUrl":"10.1016/j.comtox.2025.100385","url":null,"abstract":"<div><div>Oxidative stress is identified as a primary factor contributing to the failure of renal function. The excessive generation of oxidative stress is observed in CKDu patients in many experiments. Agrochemicals are identified as a major inducer of oxidative stress. Oxidative stress is induced mainly by direct generation of ROS through enzyme activation and by depleting antioxidant enzymes. To study how toxic exposure to agrochemicals alters the oxidative stress level in CKDu, a mathematical model of the body’s Redox system was developed and simulated how toxic exposure to agrochemicals, particularly arsenic toxicity, increases oxidative stress in cells. This model was employed to study how the molecular mechanisms of ROS generation are affected in CKDu. The study explores how arsenic concentration levels alter the oxidative stress levels and molecular interactions involved. The model indicates that the mitochondrial electron transport chain complex III is the primary contributor to ROS production, which needs to be validated through wet lab experiments. Sensitivity analyses on the model revealed that parameters associated with superoxide production are susceptible to perturbations. Further analysis shows that enzyme-driven reactions, especially those involving superoxide generation, catalase, and glutathione peroxidase, are crucial in governing oxidative stress generation in CKDu. According to the sensitivity analysis results, both NOX (NADPH oxidase) and SOD2 (superoxide dismutase 2) appear to be promising drug targets.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100385"},"PeriodicalIF":2.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most common liver diseases worldwide, originating from abnormal fat accumulation in the liver. Acetaminophen (APAP) is a common antipyretic, but its overdose is a leading cause of acute liver failure. Clinical studies suggest that APAP-induced hepatotoxicity can be more frequent and severe in obese patients with MASLD. To investigate this process, we have developed a new mathematical model that comprehensively incorporates lipid metabolism, APAP metabolism, and glutathione (GSH) detoxification. In MASLD patients, we found that CYP and GST activities have higher sensitivity to ROS production than UGT and SULT, which are highly effective in detoxifying APAP. We also highlighted that the upregulation of GPx poses an unanticipated risk during steatosis by inducing an increase in H2O2. This occurs due to a vicious circle in which increasing NAPQI adducts further elevate H2O2 levels. According to clinical reports, the toxicity of APAP varies depending on the progression of MASLD. We simulated that the pool of enzymatic alterations observed in steatotic patients exacerbates APAP-induced toxicity, which is thought to be due to a significant upregulation of CYP2E1. In contrast, the enzyme changes in MASH patients alleviate APAP-induced toxicity, likely due to decreased activity of CYPs and increased activity of UGT and GST. We believe that our strategy, which couples lipid and drug metabolism, offers valuable pharmacological insights for identifying enzymes that play a significant role in liver injury and for devising future therapeutic strategies in the context of MASLD.
{"title":"Computational modeling of the hepatocytes reveals new insights into alterations in drug metabolism, oxidative stress response, and glutathione detoxification in acetaminophen-induced hepatotoxicity associated with MASLD","authors":"Yuki Miura , Yasuyuki Sakai , Masaki Nishikawa , Eric Leclerc","doi":"10.1016/j.comtox.2025.100384","DOIUrl":"10.1016/j.comtox.2025.100384","url":null,"abstract":"<div><div>Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most common liver diseases worldwide, originating from abnormal fat accumulation in the liver. Acetaminophen (APAP) is a common antipyretic, but its overdose is a leading cause of acute liver failure. Clinical studies suggest that APAP-induced hepatotoxicity can be more frequent and severe in obese patients with MASLD. To investigate this process, we have developed a new mathematical model that comprehensively incorporates lipid metabolism, APAP metabolism, and glutathione (GSH) detoxification. In MASLD patients, we found that CYP and GST activities have higher sensitivity to ROS production than UGT and SULT, which are highly effective in detoxifying APAP. We also highlighted that the upregulation of GPx poses an unanticipated risk during steatosis by inducing an increase in H<sub>2</sub>O<sub>2</sub>. This occurs due to a vicious circle in which increasing NAPQI adducts further elevate H<sub>2</sub>O<sub>2</sub> levels. According to clinical reports, the toxicity of APAP varies depending on the progression of MASLD. We simulated that the pool of enzymatic alterations observed in steatotic patients exacerbates APAP-induced toxicity, which is thought to be due to a significant upregulation of CYP2E1. In contrast, the enzyme changes in MASH patients alleviate APAP-induced toxicity, likely due to decreased activity of CYPs and increased activity of UGT and GST. We believe that our strategy, which couples lipid and drug metabolism, offers valuable pharmacological insights for identifying enzymes that play a significant role in liver injury and for devising future therapeutic strategies in the context of MASLD.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100384"},"PeriodicalIF":2.9,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-26DOI: 10.1016/j.comtox.2025.100383
Yingying Feng, Tingting Huang
Polybrominated diphenyl ethers, particularly 2,2′,4,4′-tetrabromodiphenyl ether (PBDE-47), are persistent environmental pollutants with suspected neurodevelopmental toxicity. This study systematically elucidated the mechanisms underlying PBDE-47-induced neurodevelopmental toxicity by integrating network toxicology and bioinformatic approaches. From 4070 potential targets, we identified 902 genes associated with neurodevelopmental disorders (ND), among which TP53, AKT1, and MAPK1 were identified as core regulatory factors via topological analysis. KEGG pathway enrichment analysis revealed significant enrichment in the HIF-1 signaling pathway and thyroid hormone signaling pathway. Molecular docking simulations confirmed that PBDE-47 stably binds to these key targets. Expression analysis validated the biological basis of PBDE-47 neurotoxicity. Single-cell RNA sequencing demonstrated the expression of target genes in neural cells. Immunohistochemistry further revealed the expression of AKT1 and MAPK1 in cortical neurons and glial cells. Ultimately, our study clarifies the multi-target and multi-pathway-mediated mechanisms of PBDE-47-induced neurodevelopmental toxicity, leading to an increased risk of ND. Although this computational approach provides mechanistic insights into environmentally induced ND, further experimental validation, epidemiological studies, and advanced spatial transcriptomic models are warranted to support these findings and facilitate the development of precise prevention strategies.
{"title":"Integration of network toxicology and bioinformatics reveals novel neurodevelopmental toxicity mechanisms of 2,2′,4,4′-tetrabromodiphenyl ether","authors":"Yingying Feng, Tingting Huang","doi":"10.1016/j.comtox.2025.100383","DOIUrl":"10.1016/j.comtox.2025.100383","url":null,"abstract":"<div><div>Polybrominated diphenyl ethers, particularly 2,2′,4,4′-tetrabromodiphenyl ether (PBDE-47), are persistent environmental pollutants with suspected neurodevelopmental toxicity. This study systematically elucidated the mechanisms underlying PBDE-47-induced neurodevelopmental toxicity by integrating network toxicology and bioinformatic approaches. From 4070 potential targets, we identified 902 genes associated with neurodevelopmental disorders (ND), among which TP53, AKT1, and MAPK1 were identified as core regulatory factors via topological analysis. KEGG pathway enrichment analysis revealed significant enrichment in the HIF-1 signaling pathway and thyroid hormone signaling pathway. Molecular docking simulations confirmed that PBDE-47 stably binds to these key targets. Expression analysis validated the biological basis of PBDE-47 neurotoxicity. Single-cell RNA sequencing demonstrated the expression of target genes in neural cells. Immunohistochemistry further revealed the expression of AKT1 and MAPK1 in cortical neurons and glial cells. Ultimately, our study clarifies the multi-target and multi-pathway-mediated mechanisms of PBDE-47-induced neurodevelopmental toxicity, leading to an increased risk of ND. Although this computational approach provides mechanistic insights into environmentally induced ND, further experimental validation, epidemiological studies, and advanced spatial transcriptomic models are warranted to support these findings and facilitate the development of precise prevention strategies.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100383"},"PeriodicalIF":2.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-20DOI: 10.1016/j.comtox.2025.100382
A.M. Steinbach , C.T. Willenbockel , P. Marx-Stoelting , M.T.D. Cronin , V. Städele
Due to increasing scientific, societal and regulatory demands as well as ethical considerations there is an urgent need for improved animal-free strategies for chemical testing. A promising development in this context is the increased application of in vitro testing and in silico tools. This study aimed at integrating quantitative in vitro to in vivo extrapolation (qIVIVE) with the adverse-outcome pathway (AOP) for liver steatosis. Liver steatosis is an important (toxicological) endpoint which constitutes the first step of metabolic-dysfunction associated steatotic liver disease (MASLD), a growing challenge in the public health sector. Focus was set on the late key event of triglyceride accumulation measured in vitro after exposure of cells to the fungicides propiconazole and tebuconzole, and the corresponding key event of liver fat vacuolation observed in vivo. The qIVIVE approach was facilitated by physiologically based kinetic (PBK) and in vitro distribution models. Concentrations predicted by PBK modelling corresponded well with experimentally determined in vivo plasma and liver concentrations of the fungicides. The in vitro concentration–response data for triglyceride accumulation, when translated to equivalent oral doses, showed good correlation to rodent in vivo data on liver fat vacuolation after oral exposure to propi- and tebuconazole. qIVIVE-derived benchmark dose values were similar to values obtained from the in vivo experiments. This case study confirms the usefulness of integrating AOPs and qIVIVE for adversity prediction particularly with regard to the “replacement” aspect of the 3R principle.
{"title":"AOP-informed qIVIVE modelling for liver steatosis using triazoles","authors":"A.M. Steinbach , C.T. Willenbockel , P. Marx-Stoelting , M.T.D. Cronin , V. Städele","doi":"10.1016/j.comtox.2025.100382","DOIUrl":"10.1016/j.comtox.2025.100382","url":null,"abstract":"<div><div>Due to increasing scientific, societal and regulatory demands as well as ethical considerations there is an urgent need for improved animal-free strategies for chemical testing. A promising development in this context is the increased application of <em>in vitro</em> testing and <em>in silico</em> tools. This study aimed at integrating quantitative <em>in vitro</em> to <em>in vivo</em> extrapolation (qIVIVE) with the adverse-outcome pathway (AOP) for liver steatosis. Liver steatosis is an important (toxicological) endpoint which constitutes the first step of metabolic-dysfunction associated steatotic liver disease (MASLD), a growing challenge in the public health sector. Focus was set on the late key event of triglyceride accumulation measured <em>in vitro</em> after exposure of cells to the fungicides propiconazole and tebuconzole, and the corresponding key event of liver fat vacuolation observed <em>in vivo</em>. The qIVIVE approach was facilitated by physiologically based kinetic (PBK) and <em>in vitro</em> distribution models. Concentrations predicted by PBK modelling corresponded well with experimentally determined <em>in vivo</em> plasma and liver concentrations of the fungicides. The <em>in vitro</em> concentration–response data for triglyceride accumulation, when translated to equivalent oral doses, showed good correlation to rodent <em>in vivo</em> data on liver fat vacuolation after oral exposure to propi- and tebuconazole. qIVIVE-derived benchmark dose values were similar to values obtained from the <em>in vivo</em> experiments. This case study confirms the usefulness of integrating AOPs and qIVIVE for adversity prediction particularly with regard to the “replacement” aspect of the 3R principle.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100382"},"PeriodicalIF":2.9,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Physiologically based kinetic (PBK) models are becoming increasingly important in chemical risk assessment, helping in linking external and internal exposure concentrations, thereby supporting the development of regulatory health-based limits for chemicals with exposure from environmental, occupational, and consumer sources. To increase confidence in PBK models for regulatory purposes, the OECD published a guidance document in 2021 outlining the characterization, validation and reporting of PBK models. However, its use remains limited in chemical toxicology as reflected by the few publications that have applied it during model development. The aim of this study was to evaluate several published PBK models for Per- and polyfluoroalkyl substances (PFASs) as proof of concept to assess their validity and credibility for regulatory purposes, based on the OECD guidance. Out of 28 published PFASs human PBK models considered, 11 were selected for evaluation. The assessment used the OECD guidance document, encompassing two main areas: i) documentation (context/implementation, documentation, software implementation, verification, and peer engagement) and ii) assessment of model validity (biological basis, theoretical basis of model equations, input parameter’s reliability, uncertainty and sensitivity analysis, goodness-of-fit and predictivity). To standardize this process, an online evaluation system based on the OECD guidance was developed and used for this model evaluation exercise. The collected data were analysed to assess the overall quality of published models and identify limitations in the current PFAS model landscape. Our analysis revealed opportunities for improvement in the biological representation within current PFAS models, particularly regarding the inclusion of diverse population groups. Currently, PFAS models primarily focus on only four compounds, highlighting an opportunity to extend coverage to other PFASs using read-across approaches for data-poor chemicals. Furthermore, our findings show that a harmonized approach for PBK model reporting is needed. To facilitate broader adoption of the OECD guidance, we developed and hosted an R Shiny template on our group’s web server (https://app.shiny.insilicohub.org/Evaluation_PBPK/). This template can act as valuable tool for researchers evaluating PBK models according to the OECD guidance.
{"title":"Evaluation of PBK models using the OECD assessment framework taking PFAS as case study","authors":"Deepika Deepika , Kanchan Bharti , Shubh Sharma , Saurav Kumar , Trine Husøy , Marcin W. Wojewodzic , Klára Komprdová , Aude Ratier , Joost Westerhout , Thomas Gastellu , Meg-Anne Moriceau , Sanah Majid , Renske Hoondert , Johannes Kruisselbrink , Jasper Engel , Annelies Noorlander , Carolina Vogs , Vikas Kumar","doi":"10.1016/j.comtox.2025.100381","DOIUrl":"10.1016/j.comtox.2025.100381","url":null,"abstract":"<div><div>Physiologically based kinetic (PBK) models are becoming increasingly important in chemical risk assessment, helping in linking external and internal exposure concentrations, thereby supporting the development of regulatory health-based limits for chemicals with exposure from environmental, occupational, and consumer sources. To increase confidence in PBK models for regulatory purposes, the OECD published a guidance document in 2021 outlining the characterization, validation and reporting of PBK models. However, its use remains limited in chemical toxicology as reflected by the few publications that have applied it during model development. The aim of this study was to evaluate several published PBK models for Per- and polyfluoroalkyl substances (PFASs) as proof of concept to assess their validity and credibility for regulatory purposes, based on the OECD guidance. Out of 28 published PFASs human PBK models considered, 11 were selected for evaluation. The assessment used the OECD guidance document, encompassing two main areas: i) documentation (context/implementation, documentation, software implementation, verification, and peer engagement) and ii) assessment of model validity (biological basis, theoretical basis of model equations, input parameter’s reliability, uncertainty and sensitivity analysis, goodness-of-fit and predictivity). To standardize this process, an online evaluation system based on the OECD guidance was developed and used for this model evaluation exercise. The collected data were analysed to assess the overall quality of published models and identify limitations in the current PFAS model landscape. Our analysis revealed opportunities for improvement in the biological representation within current PFAS models, particularly regarding the inclusion of diverse population groups. Currently, PFAS models primarily focus on only four compounds, highlighting an opportunity to extend coverage to other PFASs using read-across approaches for data-poor chemicals. Furthermore, our findings show that a harmonized approach for PBK model reporting is needed. To facilitate broader adoption of the OECD guidance, we developed and hosted an R Shiny template on our group’s web server (<span><span>https://app.shiny.insilicohub.org/Evaluation_PBPK/</span><svg><path></path></svg></span>). This template can act as valuable tool for researchers evaluating PBK models according to the OECD guidance.</div><div>GitHub: PBPK-OECD-EVALUATION.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100381"},"PeriodicalIF":2.9,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号