Computational Toxicology最新文献_第2页

A comparative assessment of predictive methods for ready biodegradation using REACH experimental data 利用REACH实验数据对现成生物降解预测方法进行比较评估

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2026-03-01 Epub Date: 2025-12-18 DOI: 10.1016/j.comtox.2025.100398

Panagiotis G. Karamertzanis, Heidi Ekholm, Aliisa Yli-Tuomola, Romanas Cesnaitis, Kostas Andreou, Anna-Maija Nyman, Wim De Coen

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 in silico 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.

本研究使用2684种工业化学品的REACH实验信息整理数据集，对现成生物降解的预测方法进行了比较评估。这些结构的很大一部分不存在于模型的训练和验证集中，允许它们的无偏外部验证。我们评估了各种可以随时使用的QSAR模型，包括Biowin、Opera、Vega、catalog和Körner等人最近的模型。对这些模型进行比较的依据是它们的训练集跨越工业化学空间的程度、它们的预测性能和适用性领域覆盖范围。平衡精度范围为0.600 ~ 0.771，而识别不易生物降解物质的灵敏度范围为0.217 ~ 0.848，反映了期望与特异性之间的权衡。适用范围从28.5%到几乎整个化学领域。共识模型使用多数投票通过结合模型预测来探索敏感性和特异性的相互作用，但没有产生明显的平衡准确性或F1分数的增加，尽管它们在损害适用性领域覆盖的情况下增加了不易生物降解预测的可靠性。这项工作强调了计算机方法在预测物质命运特性方面的潜力，甚至在它们被合成或商业化之前，从而满足监管信息要求并优先考虑物质进行测试。然而，需要进一步的发展来实现与实验测试中的可变性相媲美的预测性能。整理的数据集已作为辅助信息公开提供，促进了预测方法的进一步发展和验证。

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引用次数: 0

AI-assisted QSAR framework for ecological risk assessment of pharmaceuticals: integrating experimental, mechanistic, and deep learning evidence 人工智能辅助的药品生态风险评估QSAR框架：整合实验、机制和深度学习证据

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1016/j.comtox.2026.100403

Vinicius Roveri , Alberto Teodorico Correia , Piter Gil dos Santos , Marcela Nascimento Ferreira Povoas , Walber Toma , Camilo Dias Seabra Pereira , Luciana Lopes Guimarães

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 [1], 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 > 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.

药物活性化合物（PhACs）由于其生物活性和破坏水生生态系统的潜力而成为人们关注的新兴污染物。尽管在欧洲和北美进行了广泛的研究，但对其在拉丁美洲和加勒比（LAC）的发生和风险的了解仍然有限。本研究建立在[1]小组发布的统一数据集的基础上，该数据集汇编并系统化了154项同行评议的研究，讨论了1990年至2024年间LAC水生环境中PhACs的存在，并追求两个目标：(i)通过科学计量分析绘制区域研究活动图；（ii）使用整合实验和计算机生态毒理学证据的分层框架评估生态风险（ERA）。预测无效应浓度（PNECs）来自一个结构化的证据层次，包括三个层次：验证的实验数据（第1层），VEGA QSAR在适用性领域的预测（ADI > 0.85；第2层），以及ECOSAR-TRIDENT集成输出（第3层）。在这一层，ECOSAR的机制预测被TRIDENT人工智能交叉验证。ERA综合了在巴西、墨西哥、哥伦比亚、阿根廷和玻利维亚进行的58项研究中测量的环境浓度，涵盖了24种化合物。大约71%的暴露情景被归类为可忽略或低风险，而29%表现出中度至高度的生态问题。精神药物（舍曲林、西酞普兰、氟西汀、卡马西平）、大环内酯类抗生素（红霉素、阿奇霉素、磺胺甲恶唑）和抗炎药双氯芬酸因其持久性和生物活性而成为区域优先考虑的药物。总体而言，该ERA框架提供了一种透明和资源高效的方法，用于确定phac的优先顺序和管理生态风险，适用于LAC等资源有限的地区，也适用于全球其他数据匮乏地区。

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引用次数: 0

A novel deep learning framework for predicting protein-ligand interaction fingerprints from sequence data: integrating graph inductive bias transformer with Kolmogorov-Arnold networks 从序列数据中预测蛋白质-配体相互作用指纹的一种新的深度学习框架：将图感应偏置变压器与Kolmogorov-Arnold网络集成

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-12-01 Epub Date: 2025-09-30 DOI: 10.1016/j.comtox.2025.100386

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

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.

准确地模拟蛋白质与配体的相互作用是计算蛋白质设计和药物发现的核心挑战。传统的交互指纹（IFP）方法虽然有价值，但难以捕捉微妙的绑定特征并适应不同的结构背景。为了解决这些限制，我们提出了GITK，这是一个深度学习框架，它将改进的图感应偏压变压器（GRIT）与Kolmogorov-Arnold网络（KANs）集成在一起，用于可解释的交互指纹预测。GRIT引入了归纳偏置来有效地表示蛋白质和配体的局部和全局图结构，而KAN提供了原则性的功能分解，增强了非线性特征的学习和可解释性。跨多个数据集的基准测试表明，GITK在结合亲和预测、功能效果分类和虚拟筛选方面优于最先进的模型。此外，GITK能够进行可靠的选择性分析，成功地突出腺苷受体亚型的构象差异和关键残基，与A1拮抗剂DPCPX的选择性等实验结果一致。

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引用次数: 0

Blood–brain barrier permeability prediction via novel stacking classical-quantum hybrid model 基于叠加经典-量子混合模型的血脑屏障渗透率预测

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-12-01 Epub Date: 2025-10-24 DOI: 10.1016/j.comtox.2025.100388

Muhamad Akrom , Supriadi Rustad , Totok Sutojo , De Rosal Ignatius Moses Setiadi , Hermawan Kresno Dipojono , Ryo Maezono , Hideaki Kasai

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.

血脑屏障在维持中枢神经系统的稳定性方面起着至关重要的作用，但它也限制了药物的输送。用于预测血脑屏障渗透率（BBBP）的现有机器学习（ML）和深度学习（DL）方法经常面临诸如类不平衡、可扩展性和高计算需求等挑战。为了解决这些限制，本研究旨在开发一种新的堆叠集成-量子支持向量机（SEQSVM）模型，该模型将经典集成学习器（AdaBoost， XGBoost和CatBoost）与量子元学习器（QSVM）集成在一起。所提出的混合模型结合了SMOTE + Tomek有效处理类不平衡和自定义量子特征映射用于分子指纹编码。在两个基准BBBP数据集上的实验结果表明，SEQSVM在D1（1970个样本）上的准确率为95.0%，在D2（8153个样本）上的准确率为92.0%，在准确率、灵敏度和特异性上始终优于经典集成模型3 - 6%。与现有的ML和DL模型相比，SEQSVM在准确性、可解释性和计算效率之间提供了更好的平衡。在现实世界的药物发现应用中，这是一种很有前途的BBBP预测方法。

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引用次数: 0

IoT integrated quantile principal component analysis based framework for toxic pesticides recognition and classification 基于物联网集成分位数主成分分析的有毒农药识别分类框架

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-12-01 Epub Date: 2025-09-13 DOI: 10.1016/j.comtox.2025.100375

Kanak Kumar , Anshul Verma , Pradeepika Verma

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.

农药在环境可持续性和全球稳定方面引起了重大关注。本研究调查了农药使用的类型、效益和环境挑战。为了解决这些问题，我们开发了一种创新的物联网（IoT）集成分位数主成分分析（QPCA）框架，用于识别智能农业中的有毒农药，称为IoT- tpr。提出的IoT-TPR系统是一个基于氧化锡传感器阵列的智能电子鼻，由8个商用金属氧化物半导体气体传感器组成，可检测有毒农药并将数据传输到亚马逊网络服务云进行进一步分析。采用两阶段QPCA预处理技术对传感器响应进行分析。随后，采用径向基函数（RBF）、极限学习机（ELM）、决策树（DT）和k近邻（KNN）四种分类器进行性能比较评价。结果表明，QPCA-KNN达到了99.05%的最高准确率，在所有性能指标上都优于其他方法，显示出优越的分类能力。RBF（96.24%）和ELM（95.81%）也表现出较强的性能，但略低于QPCA-KNN，而DT（92.35%）的准确率最低，但仍保持合理的性能。总体而言，QPCA-KNN是本研究中最有效和最稳健的分类模型。

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引用次数: 0

Pesticides and cleft lip/palate: A state-of-the-art review and analysis of epidemiologic evidence 农药与唇腭裂：流行病学证据的最新回顾和分析

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-12-01 Epub Date: 2025-10-30 DOI: 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.

研究背景妊娠期接触农药已被认为是唇腭裂发生的潜在环境危险因素。一些流行病学研究调查了这种关联，但结果仍然不一致。目的：本系统综述旨在批判性地评估来自人类、动物和体外研究的证据，这些证据表明农药暴露与CLP之间存在潜在联系。方法采用标准化检索词，结合农药和CLP相关描述词，对1980年1月至2024年6月在PubMed、Embase和Cochrane Library进行综合检索。共检索到217条记录（189条来自PubMed， 28条来自Embase， 0条来自Cochrane图书馆）。在删除61个重复项后，对标题和摘要进行筛选，并选择87项研究进行全文综述。最终纳入47篇文献，包括20篇人类流行病学调查，25篇动物模型（啮齿动物和猿类）实验研究，以及3篇颅面发育相关的体外研究。观察性和实验性研究的偏倚风险均使用乔安娜布里格斯研究所开发的JBI关键评估工具进行独立评估。结果人类流行病学研究提供了不同的结果，而动物和体外研究支持农药暴露在CLP中的因果作用。质量评估揭示了研究方法的异质性和不同程度的偏倚。结论：现有证据表明，农药暴露可能会增加CLP的风险，尽管人体研究的结果仍不一致。需要进一步的大规模、精心设计的研究来证实这些关联，并澄清剂量-反应关系和潜在机制。

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引用次数: 0

Advancing chemical grouping: development and application of signature-based structure-activity groups for non-animal safety assessments 推进化学分组：用于非动物安全性评估的基于签名的结构-活性组的开发和应用

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-12-01 Epub Date: 2025-11-11 DOI: 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.

香料材料研究所（RIFM）已经开发出一种稳健、可靠、可重复的方法，根据化学物质的结构特征进行聚类，并推导出结构活性基团。这种方法促进了从人工评估数千种香料成分的化学配对中获得的知识的制度化。该技术提高了化学品安全评估的准确性、一致性、透明度和可解释性，同时减少了对专家判断和任何相关偏见的依赖。材料基于签名的结构-活动组是通过自顶向下的方法创建的，该方法使用基于表示重要子结构特征的指示短语（IPs）的标准化签名树。我们已经将该方法应用于超过6000种分立的芳香剂和芳香类有机化学品（例如，清单中描述的化学类别的有机化合物，如醛、酮、酯等），并已显示出在该化学空间中观察到的各种性质和参数表现良好。签名树具有很强的适应性，可以扩展到香味材料中没有的ip。结构-活性基团很容易允许将数千种化学品的库存透明和可重复地分离成具有相同ip的化学品簇。除了一个或两个相同的ip外，可以识别共享所有相同ip的相邻基团，从而毫不费力地扩大潜在的可读源物质的范围。由于易于解释，该系统促进了具有不同化学知识水平的科学家之间的讨论。除了通过读取填充数据缺口的聚类之外，其他应用还包括通过使用各种机器学习技术对ip进行编码来确定测试和预测毒理学的优先级。

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引用次数: 0

Proteintox: A multifaceted machine learning strategy for identifying cardiotoxic, neurotoxic, and enterotoxic proteins proteinx：一个多方面的机器学习策略，用于识别心脏毒性，神经毒性和肠毒性蛋白质

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-12-01 Epub Date: 2025-11-06 DOI: 10.1016/j.comtox.2025.100390

Pradnya Kamble , Anju Sharma , Aritra Banerjee , Shubham Pandey, Prabha Garg

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.

准确预测蛋白质毒性在从药物开发到环境风险评估等各个领域至关重要，因为它可以早期识别和减轻与蛋白质接触有关的潜在有害影响。心脏毒性、肠毒性和神经毒性是需要在药物开发的早期阶段进行严格评估的关键问题。这项研究解决了对准确预测模型的需求，以识别具有潜在心脏毒性、肠毒性或神经毒性作用的蛋白质和肽。通过利用机器学习（ML）技术（支持向量机（SVM）、随机森林（RF）、k近邻（kNN））和包含广泛分子特征的综合数据集，开发了稳健的预测模型，根据蛋白质和肽的潜在毒性特征可靠地分类。这些模型整合了多种特征，包括氨基酸组成（Compo）、联合三元组（CTriad）、组成-过渡-分布（CTD）和从蛋白质一级序列中获得的理化n-gram性质（PnGT），从而能够全面分析分子的毒性潜力。使用孤立的特征集和四个特征集的组合开发了各种模型。RF模型在毒性预测方面一直优于其他模型，Compo + CTriad + CTD特征集被推荐，因为它能够捕获复杂的分子相互作用和结构细节。提出的模型proteinx平衡了详细的结构见解与实用性，增强了其评估涉及分子相互作用的影响的能力。它在所有测试场景中提供高精度、灵敏度和特异性，同时保持计算效率和可解释性。该研究还强调了在不增加复杂性的情况下选择合适的特征集来增强模型性能的重要性，表明添加更多的特征并不总是转化为改进的预测能力。这项工作的意义在于，它有可能通过提供早期毒性预测来简化药物发现过程，从而减少对昂贵和耗时的实验分析的依赖。数据和源代码可从https://github.com/PGlab-NIPER/Proteintox获得。

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引用次数: 0

In vitro transcriptomic points of departure derived from human whole transcriptome and reduced S1500+ gene panel are highly comparable 人类全转录组和减少的S1500+基因组的体外转录组出发点具有高度可比性

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-12-01 Epub Date: 2025-11-18 DOI: 10.1016/j.comtox.2025.100392

James Johnson , Joseph L. Bundy , Joshua A. Harrill , Logan J. Everett

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.

之前对三种不同人类细胞系中超过1300种化学物质的高通量转录组筛选依赖于测量超过19,000个基因表达的全转录组靶向RNA-seq测定和基于特征的浓度-反应分析方法，以获得每种化学物质的总体转录组出发点（tPOD）。为了探索切换到仅测量2730个基因的减少表示版本的检测（“S1500+”）的影响，我们仅使用S1500+基因面板重新分析了现有数据，并使用先前应用于整个转录组数据的相同方法进行了浓度响应建模。来自S1500+基因的tpod与来自整个转录组表达数据的tpod高度一致，无论细胞系如何，超过93%的tpod相互之间的差异在1个数量级以内。从两个基因组衍生的tpod之间的总体均方根偏差小于在筛选研究中相同化学物质的重复样本之间观察到的偏差。重要的是，当减少对S1500+基因的分析时，活性基因签名的总数仅减少了13 - 17%（取决于细胞系）。然而，对单个活性基因特征的检查显示，两个TempO-Seq基因面板之间存在系统差异，这是源数据库或相关蛋白靶点的功能。总的来说，我们的分析表明，在高通量转录组学筛选的背景下，切换到减少代表性的分析可能对总体tpod的推断影响最小，但可能会影响对特定作用机制的推断。

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引用次数: 0

Integration of network toxicology and bioinformatics reveals novel neurodevelopmental toxicity mechanisms of 2,2′,4,4′-tetrabromodiphenyl ether 网络毒理学和生物信息学的结合揭示了2,2 ',4,4 ' -四溴联苯醚新的神经发育毒性机制

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-12-01 Epub Date: 2025-09-26 DOI: 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.

多溴联苯醚，特别是2,2 ',4,4 ' -四溴联苯醚（PBDE-47），是一种持久性环境污染物，怀疑具有神经发育毒性。本研究结合网络毒理学和生物信息学方法，系统阐明了pbde -47诱导神经发育毒性的机制。从4070个潜在靶点中，我们确定了902个与神经发育障碍（ND）相关的基因，其中通过拓扑分析确定了TP53、AKT1和MAPK1为核心调控因子。KEGG通路富集分析显示HIF-1信号通路和甲状腺激素信号通路显著富集。分子对接模拟证实了PBDE-47与这些关键靶点的稳定结合。表达分析证实了PBDE-47神经毒性的生物学基础。单细胞RNA测序证实了靶基因在神经细胞中的表达。免疫组化进一步揭示了AKT1和MAPK1在皮质神经元和胶质细胞中的表达。最终，我们的研究阐明了pbde -47诱导神经发育毒性，导致ND风险增加的多靶点和多途径介导的机制。虽然这种计算方法提供了环境诱导ND的机制见解，但需要进一步的实验验证、流行病学研究和先进的空间转录组模型来支持这些发现，并促进精确预防策略的发展。

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引用次数: 0