Computational Toxicology最新文献

{"title":"A comparative assessment of predictive methods for ready biodegradation using REACH experimental data","authors":"Panagiotis G. Karamertzanis,&nbsp;Heidi Ekholm,&nbsp;Aliisa Yli-Tuomola,&nbsp;Romanas Cesnaitis,&nbsp;Kostas Andreou,&nbsp;Anna-Maija Nyman,&nbsp;Wim De Coen","doi":"10.1016/j.comtox.2025.100398","DOIUrl":"10.1016/j.comtox.2025.100398","url":null,"abstract":"<div><div>This study presents a comparative assessment of predictive methods for ready biodegradation using a curated dataset with REACH experimental information for 2684 industrial chemicals. A large part of these structures is not present in the training and validation sets of the models allowing for their unbiased external validation. We evaluated various QSAR models that can be readily used, including Biowin, Opera, Vega, Catalogic, and a recent model by Körner et al. The models were compared based on how well their training sets span the industrial chemical space, their predictive performance and applicability domain coverage. The balanced accuracy ranged from 0.600 to 0.771, while the sensitivity for identifying non-readily biodegradable substances varied between 0.217 and 0.848, reflecting the expected trade-off with specificity. The applicability domain coverage ranged from 28.5% to nearly the entire chemical space. Consensus models were developed using majority voting to explore the sensitivity and specificity interplay by combining model predictions, but did not yield appreciable increases in balanced accuracy or F1 score, although they increased the reliability of non-readily biodegradable predictions at the detriment of applicability domain coverage. This work underscores the potential of <em>in silico</em> methods for predicting the fate properties of substances, even before they are synthesised or commercialised, thereby fulfilling regulatory information requirements and prioritizing substances for testing. However, further developments are needed to achieve predictive performance that is comparable to the variability in the experimental test. The curated dataset has been made publicly available as supporting information, facilitating the further development and validation of predictive methods.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"37 ","pages":"Article 100398"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-assisted QSAR framework for ecological risk assessment of pharmaceuticals: integrating experimental, mechanistic, and deep learning evidence","authors":"Vinicius Roveri ,&nbsp;Alberto Teodorico Correia ,&nbsp;Piter Gil dos Santos ,&nbsp;Marcela Nascimento Ferreira Povoas ,&nbsp;Walber Toma ,&nbsp;Camilo Dias Seabra Pereira ,&nbsp;Luciana Lopes Guimarães","doi":"10.1016/j.comtox.2026.100403","DOIUrl":"10.1016/j.comtox.2026.100403","url":null,"abstract":"<div><div>Pharmaceutically active compounds (PhACs) are emerging pollutants of concern due to their bioactivity and potential to disrupt aquatic ecosystems. Although extensively studied in Europe and North America, knowledge of their occurrence and risks in Latin America and the Caribbean (LAC) remains limited. This study builds upon the harmonized dataset published by our group [<span><span>1</span></span>], which compiled and systematized 154 peer-reviewed studies addressing the presence of PhACs in LAC aquatic environments between 1990 and 2024, and pursued two objectives: (i) to map regional research activity through scientometric analysis, and (ii) to assess ecological risks (ERA) using a hierarchical framework integrating experimental and in silico ecotoxicological evidence. Predicted No-Effect Concentrations (PNECs) were derived from a structured evidence hierarchy comprising three tiers: validated experimental data (Tier 1), VEGA QSAR predictions within the applicability domain (ADI &gt; 0.85; Tier 2), and ECOSAR–TRIDENT integrated outputs (Tier 3). In this tier, ECOSAR mechanistic predictions were cross-validated by TRIDENT artificial intelligence. The ERA integrated measured environmental concentrations from 58 studies conducted in Brazil, Mexico, Colombia, Argentina, and Bolivia, covering 24 compounds. Approximately 71 % of all exposure scenarios were classified as negligible or low risk, whereas 29 % exhibited moderate to high ecological concern. Psychotropic drugs (sertraline, citalopram, fluoxetine, carbamazepine), macrolide antibiotics (erythromycin, azithromycin, sulfamethoxazole), and the anti-inflammatory diclofenac emerged as regional priorities due to their persistence and bioactivity. Overall, this ERA framework provides a transparent and resource-efficient approach for prioritizing PhACs and managing ecological risks, suitable for regions with limited resources such as LAC and adaptable to other data-scarce areas worldwide.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"37 ","pages":"Article 100403"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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,&nbsp;Qianjin Guo,&nbsp;Wu Liu,&nbsp;Zijun Wang,&nbsp;Kaitai Han,&nbsp;Chaojing Shi,&nbsp;Zhenxing Li,&nbsp;Sichao Lu,&nbsp;Mengqiu Wang,&nbsp;Zhiwei Zhang,&nbsp;Ruoyan Dai,&nbsp;Zhenghui Wang,&nbsp;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":"2025-12-01","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}

{"title":"Blood–brain barrier permeability prediction via novel stacking classical-quantum hybrid model","authors":"Muhamad Akrom ,&nbsp;Supriadi Rustad ,&nbsp;Totok Sutojo ,&nbsp;De Rosal Ignatius Moses Setiadi ,&nbsp;Hermawan Kresno Dipojono ,&nbsp;Ryo Maezono ,&nbsp;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":"2025-12-01","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}

{"title":"IoT integrated quantile principal component analysis based framework for toxic pesticides recognition and classification","authors":"Kanak Kumar ,&nbsp;Anshul Verma ,&nbsp;Pradeepika Verma","doi":"10.1016/j.comtox.2025.100375","DOIUrl":"10.1016/j.comtox.2025.100375","url":null,"abstract":"<div><div>Pesticides present significant concerns regarding environmental sustainability and global stability. This study investigates the types, benefits, and environmental challenges associated with pesticide use. To address these concerns, we developed an innovative Internet of Things (IoT) integrated quantile principal component analysis (QPCA) framework for the recognition of toxic pesticides in smart farming, termed IoT-TPR. The proposed IoT-TPR system is an intelligent electronic nose based on a tin-oxide sensor array, consisting of eight commercial metal–oxide–semiconductor gas sensors, which detect toxic pesticides and transmit the data to the Amazon Web Services cloud for further analysis. A two-stage QPCA preprocessing technique is employed to analyze sensor responses. Subsequently, four classifiers such as radial basis function (RBF), extreme learning machine (ELM), decision tree (DT), and k-nearest neighbor (KNN) are used for comparative performance evaluation. The results indicate that QPCA-KNN achieves the highest accuracy at 99.05%, outperforming other methods across all performance metrics and demonstrating superior classification capability. RBF (96.24%) and ELM (95.81%) also exhibit strong performance, though slightly lower than QPCA-KNN, while DT (92.35%) shows the lowest accuracy but still maintains reasonable performance. Overall, QPCA-KNN emerges as the most effective and robust classification model in this study.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100375"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing chemical grouping: development and application of signature-based structure-activity groups for non-animal safety assessments","authors":"Jake Muldoon ,&nbsp;Holger Moustakas ,&nbsp;Terry W. Schultz ,&nbsp;Trevor M. Penning ,&nbsp;Amanda Bryant-Friedrich ,&nbsp;Danielle J. Botelho ,&nbsp;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":"2025-12-01","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}

{"title":"Pesticides and cleft lip/palate: A state-of-the-art review and analysis of epidemiologic evidence","authors":"Céline Mare ,&nbsp;Arnaud Tête ,&nbsp;Sylvie Bortoli ,&nbsp;Brigitte Vi-Fane ,&nbsp;Sylvie Babajko ,&nbsp;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":"2025-12-01","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}

{"title":"Proteintox: A multifaceted machine learning strategy for identifying cardiotoxic, neurotoxic, and enterotoxic proteins","authors":"Pradnya Kamble ,&nbsp;Anju Sharma ,&nbsp;Aritra Banerjee ,&nbsp;Shubham Pandey,&nbsp;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":"2025-12-01","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}

{"title":"In vitro transcriptomic points of departure derived from human whole transcriptome and reduced S1500+ gene panel are highly comparable","authors":"James Johnson ,&nbsp;Joseph L. Bundy ,&nbsp;Joshua A. Harrill ,&nbsp;Logan J. Everett","doi":"10.1016/j.comtox.2025.100392","DOIUrl":"10.1016/j.comtox.2025.100392","url":null,"abstract":"<div><div>Previous high-throughput transcriptomic screening of over 1,300 chemicals in three different human cell lines relied on a whole transcriptome targeted RNA-seq assay that measures the expression of over 19,000 genes and a signature-based concentration–response analysis method to derive an overall transcriptomic point of departure (tPOD) for each chemical. To explore the impacts of switching to a reduced representation version of the assay (“S1500+”) measuring only 2730 genes, we re-analyzed the existing data using only the S1500+ gene panel, and performed concentration–response modeling using the same methodology previously applied to the whole transcriptome data. The tPODs derived from the S1500+  genes were highly concordant with the tPODs derived from the whole transcriptome expression data regardless of cell line, with over 93 % of the corresponding tPODs falling within 1 order of magnitude of each other. The overall root mean squared deviation between tPODs derived from the two gene sets was less than what was observed between duplicate samples of the same chemical within screening studies. Importantly, the total number of active gene signatures shrunk by only 13–17 % (depending on cell line) when reducing the analysis to the S1500+  genes. However, examination of the individual active gene signatures showed systematic differences between the two TempO-Seq gene panels as a function of source database or associated protein target. Overall, our analysis suggests that switching to the reduced representation assay in the context of high-throughput transcriptomic screening would likely have minimal impacts on the inference of overall tPODs, but could impact inference of specific mechanisms-of-action.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"36 ","pages":"Article 100392"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of network toxicology and bioinformatics reveals novel neurodevelopmental toxicity mechanisms of 2,2′,4,4′-tetrabromodiphenyl ether","authors":"Yingying Feng,&nbsp;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":"2025-12-01","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}