Computational Toxicology最新文献_第5页

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

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology

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

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

Chemical warfare agents (CWAs), particularly organophosphorus (OP) nerve agents, are among the most toxic and persistent compounds known, posing significant threats to human health and security. Experimental determination of their median lethal dose (LD₅₀) values is limited by ethical, biosafety, and accessibility constraints. While conventional QSAR models provide useful approximations, they often lack mechanistic interpretability, especially for novel agents.

In this study, we present a hybrid QSAR framework that integrates mechanistically relevant descriptors derived from density functional theory (DFT) and molecular docking simulations with conventional physicochemical features to predict LD₅₀ of OP nerve agents. The key mechanistic descriptors include acetylcholinesterase (AChE) binding affinity and serine phosphorylation interaction energy, capturing distinct toxicodynamic phases of nerve agent action.

We evaluate both linear regression and random forest models to assess predictive performance and interpretability. Cross-validation confirms that incorporating mechanistic features modestly improves accuracy and generalizability. Feature importance analysis identifies interaction energy as the most influential predictor, aligning with the irreversible inhibition mechanism of AChE.

Importantly, the model is capable of predicting LD₅₀ values for structurally untested agents, including GF and Novichok compounds, thereby extending its utility to substances lacking experimental data. This study highlights the potential of mechanistically grounded in silico methods as an ethically sound and scalable alternative to animal testing for acute toxicity assessment. By aligning with regulatory needs for interpretable and reproducible predictions, the proposed approach contributes to integrated testing strategies, and new approach methodologies in computational toxicology.

化学战剂，特别是有机磷神经毒剂，是已知毒性和持久性最强的化合物之一，对人类健康和安全构成重大威胁。它们的中位致死剂量（LD50）值的实验测定受到伦理、生物安全和可及性限制。虽然传统的QSAR模型提供了有用的近似，但它们通常缺乏机制可解释性，特别是对于新的代理。在这项研究中，我们提出了一个混合QSAR框架，该框架将来自密度泛函数理论（DFT）的机械相关描述符和分子对接模拟与传统的物理化学特征相结合，以预测OP神经毒剂的LD50。关键的机制描述包括乙酰胆碱酯酶（AChE）结合亲和力和丝氨酸磷酸化相互作用能，捕捉神经毒剂作用的不同毒理学阶段。我们评估了线性回归和随机森林模型来评估预测性能和可解释性。交叉验证证实，结合机械特征适度提高了准确性和泛化性。特征重要性分析发现，相互作用能是影响最大的预测因子，符合AChE的不可逆抑制机制。重要的是，该模型能够预测未经结构测试的试剂（包括GF和Novichok化合物）的LD50值，从而将其应用于缺乏实验数据的物质。这项研究强调了机械接地在硅方法的潜力，作为一种道德健全和可扩展的替代动物试验急性毒性评估。通过与可解释和可重复预测的监管需求保持一致，提出的方法有助于综合测试策略，以及计算毒理学中的新方法方法。

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引用次数: 0

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

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-09-01 Epub Date: 2025-08-19 DOI: 10.1016/j.comtox.2025.100374

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

Consensus approaches are applied in different quantitative structure–activity relationship (QSAR) modeling contexts based on the assumption that combining individual model predictions will improve prediction reliability. This study evaluated the performance of TEST, CATMoS and VEGA models for prediction of oral rat LD₅₀, both individually and in consensus, across a dataset of 6,229 organic compounds. Predicted LD₅₀ values from the models were compared for each compound, and the lowest value was assigned as the output of the conservative consensus model (CCM). Predictive accuracy was then evaluated based on the agreement of predicted LD₅₀-based GHS category assignments with those derived experimentally. The aim was to allow for the most conservative value to be identified. Results showed that CCM had the highest over-prediction rate at 37 %, compared to TEST (24 %), CATMoS (25 %) and VEGA (8 %). Meanwhile, its under-prediction rate was lowest at 2 %, relative to TEST (20 %), CATMoS (10 %) and VEGA (5 %). Due to the method applied, CCM was the most conservative across all GHS categories. Further, structural analysis demonstrated that no specific chemical classes or functional groups were consistently underpredicted or overpredicted. The utility of CCM lies in its ability to establish a foundation for contextualizing the general use of consensus modeling, in order to derive health-protective oral rat LD₅₀ estimates under conditions of uncertainty, especially where experimental data are limited or absent.

共识方法应用于不同的定量构效关系（QSAR）建模情境，基于将单个模型预测结合起来可以提高预测可靠性的假设。本研究评估了TEST、CATMoS和VEGA模型在预测大鼠口服LD50方面的性能，包括单独的和一致的，涉及6229种有机化合物的数据集。对每种化合物的模型预测LD50值进行比较，并将最低值指定为保守共识模型（CCM）的输出。然后根据基于ld50的预测GHS类别分配与实验得出的类别分配的一致性来评估预测准确性。这样做的目的是为了确定最保守的价值。结果显示，与TEST（24%）、CATMoS（25%）和VEGA（8%）相比，CCM的过度预测率最高，为37%。同时，相对于TEST（20%）、CATMoS（10%）和VEGA(5%)，其预测不足率最低，为2%。由于采用的方法，CCM在所有GHS类别中是最保守的。此外，结构分析表明，没有特定的化学类别或官能团一直被低估或高估。CCM的效用在于它能够为共识模型的普遍使用建立基础，以便在不确定条件下，特别是在实验数据有限或缺乏实验数据的情况下，得出保护健康的口服大鼠LD50估计。

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引用次数: 0

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

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-09-01 Epub Date: 2025-09-02 DOI: 10.1016/j.comtox.2025.100378

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

The nanoinformatics provides a platform to refine the nanotechnology approach by controlling the parameters based on the previous informations. Nanoinformatics helps the research community by leveraging sophisticated algorithms and complex computational modeling to predict the essential properties of nanomedicine and ensure their optimal biological interaction and performance. There are numerous potential roles of nanoinformatics in enhancing therapeutic value and preventing unpredictable toxicological pathways of nanomedicine. This review article delves into the pivotal applications of various computational tools to optimize the biological behavior of nanomedicine by controlling their physicochemical characteristics. This review thus offers an insight into adequately comprehending the in silico models such as nano-QSAR, MD simulations, CGMD and Brownian simulations to optimize nanomedicine. These tools help in product development by reducing the cost and time by controlling several biological responses of nanomedicines, including their protein interaction, mitigation, extravasation, receptor interaction and toxicological responses.

纳米信息学提供了一个平台，通过控制基于先前信息的参数来改进纳米技术方法。纳米信息学通过利用复杂的算法和复杂的计算模型来帮助研究社区预测纳米药物的基本特性，并确保其最佳的生物相互作用和性能。纳米信息学在提高纳米药物的治疗价值和预防不可预测的毒理学途径方面具有许多潜在的作用。这篇综述文章深入探讨了各种计算工具的关键应用，通过控制纳米药物的物理化学特性来优化其生物行为。因此，本文综述为充分理解纳米qsar、MD模拟、CGMD和布朗模拟等计算机模型以优化纳米医学提供了参考。这些工具通过控制纳米药物的几种生物反应，包括它们的蛋白质相互作用、缓释、外渗、受体相互作用和毒理学反应，减少了成本和时间，从而有助于产品开发。

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引用次数: 0

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

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-06-01 DOI: 10.1016/j.comtox.2025.100361

Sarder Arifuzzaman MS , Md. Harun-Or-Rashid PhD , Farhina Rahman Laboni M. Pharm. , Mst Reshma Khatun MS , Nargis Sultana Chowdhury PhD

Liver X Receptors (LXRs) play a critical role in regulating lipid metabolism and inflammation, with their altered activity linked to several metabolic diseases. Although several LXR agonists have been identified, their clinical use has been limited due to adverse effects. In this study, we first leveraged multiple biological data repositories (including RNA-seq, Human Protein Atlas, DisGeNET, and WebGestalt) to examine the expression of LXRs at both the mRNA and protein levels across various tissues. We performed network and pathway analyses to redefine the physiological roles and disease associations of LXRs. Our findings emphasize the diverse functions of LXRs and highlight the potential for small molecules to pharmacologically modulate LXR activity for therapeutic purposes. In the second phase, we conducted an in-silico search for novel LXR modulators, beginning with molecular docking studies of eleven ligands that have been previously tested in preclinical or clinical settings. Based on docking scores and chemico-pharmacokinetic properties, we identified T0901317 and AZ876 as leading candidates, showing the highest binding affinity for LXR-α and LXR-β, respectively. In the final step, we extended our screening to discover new LXR ligands guided by the chemical structures of T0901317 and AZ876. Our docking and molecular dynamics (MD) simulations revealed that ZINC000095464663 and ZINC000021912925 exhibited the strongest binding affinities, alongside favorable pharmacokinetic profiles for both LXR subtypes. In conclusion, our in-silico approach, combining network analysis, virtual screening, molecular docking, MD simulations, and chemico-pharmacokinetic assessments, has uncovered two promising ligands for oral administration, offering potential for future therapeutic interventions targeting LXRs.

肝X受体（LXRs）在调节脂质代谢和炎症中起关键作用，其活性的改变与几种代谢性疾病有关。虽然已经确定了几种LXR激动剂，但由于其不良反应，其临床应用受到限制。在这项研究中，我们首先利用多个生物数据库（包括RNA-seq， Human Protein Atlas， DisGeNET和WebGestalt）来检查LXRs在mRNA和蛋白质水平上在不同组织中的表达。我们进行了网络和通路分析，以重新定义LXRs的生理作用和疾病关联。我们的研究结果强调了LXR的多种功能，并强调了小分子药物调节LXR活性以达到治疗目的的潜力。在第二阶段，我们进行了新型LXR调节剂的计算机搜索，从先前在临床前或临床环境中测试过的11种配体的分子对接研究开始。基于对接评分和化学药代动力学特性，我们确定T0901317和AZ876分别对LXR-α和LXR-β具有最高的结合亲和力。在最后一步，我们扩展了我们的筛选，以T0901317和AZ876的化学结构为导向，发现新的LXR配体。我们的对接和分子动力学（MD）模拟显示，ZINC000095464663和ZINC000021912925具有最强的结合亲和力，并且具有良好的药代动力学特征。总之，我们的计算机方法结合了网络分析、虚拟筛选、分子对接、MD模拟和化学药代动力学评估，发现了两种有前景的口服配体，为未来针对LXRs的治疗干预提供了潜力。

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引用次数: 0

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

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology

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

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

The use of Machine Learning (ML) models to predict the binding potency of chemicals to estrogen and androgen receptors has become well-established, helping in the prioritization of chemicals for endocrine disruption testing. However, the potential of ML models for other endocrine targets, such as the progesterone receptor (PR), remains underexplored. In this study, we developed an ML model to predict PR binding affinity and assess the agonistic/antagonistic properties of chemicals. The model achieved a training accuracy of 99.72% and a validation accuracy of 74.46%. External validation was conducted on a dataset of approximately 10,000 chemicals, including 5720 compounds from the training set for which there is a known outcome. External predictions aligned closely with experimental in vitro data, achieving an accuracy of 96.85%. Additionally, the model successfully predicted PR binding affinity and agonistic/antagonistic properties for chemicals without available experimental data. In summary, this study highlights the potential of ML as an effective tool for prioritizing chemicals for future in vitro and in vivo testing of PR binding potency and agonistic/antagonistic properties of chemicals.

使用机器学习（ML）模型来预测化学物质与雌激素和雄激素受体的结合能力已经得到了很好的应用，这有助于在内分泌干扰测试中确定化学物质的优先级。然而，ML模型对其他内分泌靶点（如孕激素受体（PR））的潜力仍未得到充分探索。在这项研究中，我们建立了一个ML模型来预测PR的结合亲和力，并评估化学物质的激动/拮抗特性。该模型的训练准确率为99.72%，验证准确率为74.46%。外部验证是在大约10,000种化学物质的数据集上进行的，其中包括来自已知结果的训练集的5720种化合物。外部预测与体外实验数据密切吻合，准确率为96.85%。此外，该模型在没有实验数据的情况下成功预测了PR的结合亲和力和化学物质的激动/拮抗特性。总之，本研究强调了ML作为一种有效工具的潜力，可以在体外和体内测试化学物质的PR结合效力和激动/拮抗特性，从而优先考虑化学物质。

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引用次数: 0

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

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology

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

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

Hazard assessment of non-genotoxic carcinogens could greatly benefit from next generation risk assessment approaches, driven by the multitude of mechanisms through which non-genotoxic carcinogens operate. One method for structuring new approach methodology-derived data is the adverse outcome pathway (AOP) concept. Currently, mostly qualitative AOPs are described, limiting their application for regulatory decision making. In contrast, quantitative AOPs use mathematical terms to describe the relationships between key events (KEs), allowing for the derivation of a Point of Departure (PoD). Here, we report quantification of the key event relationship (KER) between sustained hepatocyte proliferation and liver tumour formation, two KEs of AOP#220 relating to CYP2E1 activation leading to liver cancer. We use incidence of histopathological lesions indicative of proliferation, as well as BrdU labelling obtained from existing sub-chronic toxicity studies in rats, to quantify proliferation. For liver cancer, incidences of hepatocellular adenoma and carcinoma from 2-year rodent carcinogenicity studies were collected. Data for both KEs were combined to calibrate a response-response model, and Bayesian logistic regression analysis was applied to obtain predictions and credible intervals for carcinogenicity. Proliferative lesion incidence was observed to be a highly specific, yet insensitive predictor, and combining this with BrdU labelling yields more accurate predictions of carcinogenicity. Importantly, we demonstrate that for most of the chemicals tested, inclusion of BrdU labelling returns more precise predicted benchmark dose intervals for PoD derivation. To further explore this quantitative KER and its regulatory application, we propose to include and standardize BrdU labelling for sub-chronic toxicity studies performed for regulatory purposes.

非基因毒性致癌物的危害评估可以极大地受益于下一代风险评估方法，这些方法是由非基因毒性致癌物的多种作用机制驱动的。构建新方法方法衍生数据的一种方法是不良结果路径（AOP）概念。目前，对aop的描述大多是定性的，限制了它们在监管决策中的应用。相比之下，定量aop使用数学术语来描述关键事件（ke）之间的关系，从而允许推导出一个起点（PoD）。在这里，我们报告了持续肝细胞增殖和肝脏肿瘤形成之间的关键事件关系（KER）的量化，AOP#220的两个ke与CYP2E1激活导致肝癌有关。我们使用指示增殖的组织病理学病变发生率，以及从现有的大鼠亚慢性毒性研究中获得的BrdU标记来量化增殖。对于肝癌，收集了2年啮齿类动物致癌性研究中肝细胞腺瘤和肝癌的发生率。将两种ke的数据合并以校准响应-响应模型，并应用贝叶斯逻辑回归分析获得致癌性的预测和可信区间。观察到增生性病变发生率是一个高度特异性但不敏感的预测因子，将其与BrdU标记相结合可以更准确地预测致癌性。重要的是，我们证明，对于大多数被测试的化学品，包含BrdU标签可以为PoD衍生提供更精确的预测基准剂量间隔。为了进一步探索定量KER及其监管应用，我们建议将BrdU标签纳入并标准化用于监管目的的亚慢性毒性研究。

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引用次数: 0

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

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology

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

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

Evaluation of skin-sensitizing potential is important to confirm the safety of cosmetics. As animal testing is no longer permitted, several alternative methods based on the adverse outcome pathway (AOP) approach have been reported. In addition, integrated approaches to testing and assessment (IATA), which combine the results of multiple alternative methods to assess skin sensitization potential, have been developed. We have reported an artificial neural network (ANN) model for sensitization risk assessment using commercial software, QwikNet. In the present study, we constructed a new sensitization prediction model for substances with known structures using the free and open-source software R for statistical analysis, and compared the results with those of the QwikNet model. The R model was confirmed to show similar predictive performance for estimated concentration three (EC3) which is the concentration of a test substance needed to produce a stimulation index of 3 to the QwikNet model on the same training set of 134 compounds. The accuracy, overpredicted rate, and underpredicted rate of the R model were 81.3%, 10.4%, and 8.2%, respectively, versus 79.9%, 10.4%, and 9.7% for the QwikNet model. In case studies of compounds not included in the training set, the R model showed generally good predictive ability. For less-well-predicted substances, additional in silico and read-across evaluations complemented the ANN model and improved the predictive accuracy. This study demonstrates that the ANN model is portable to the R software system. Furthermore, the combination of ANN prediction with in silico predictions and read-across taking account of substructures improves the prediction of skin-sensitizing potential in a weight-of-evidence approach.

皮肤致敏电位的评估对于确认化妆品的安全性非常重要。由于动物试验不再被允许，一些基于不良结果通路（AOP）方法的替代方法已经被报道。此外，已经开发了综合测试和评估方法（IATA），将多种替代方法的结果结合起来评估皮肤致敏潜力。我们报道了一个人工神经网络（ANN）模型，用于使用商业软件QwikNet进行敏化风险评估。在本研究中，我们利用免费开源软件R对已知结构物质构建了新的敏化预测模型进行统计分析，并与QwikNet模型进行了比较。在134个化合物的同一训练集上，R模型对产生刺激指数为3所需的测试物质的浓度（EC3）的预测性能与QwikNet模型相似。R模型的准确率、高估率和低估率分别为81.3%、10.4%和8.2%，而QwikNet模型的准确率为79.9%、10.4%和9.7%。在未包含在训练集中的化合物的案例研究中，R模型显示出良好的预测能力。对于预测较差的物质，额外的计算机和读取评估补充了人工神经网络模型，提高了预测准确性。研究表明，该人工神经网络模型可移植到R软件系统。此外，将人工神经网络预测与计算机预测和考虑子结构的读取相结合，在证据权重方法中提高了对皮肤致敏电位的预测。

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引用次数: 0

The Alarming Consequences of Workforce Reductions at the FDA, EPA, NIH and CDC in the United States 美国FDA、EPA、NIH和CDC裁员的惊人后果

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-06-01 Epub Date: 2025-05-02 DOI: 10.1016/j.comtox.2025.100352

Martin van den Berg PhD (Editor-in-Chief, Regulatory Toxicology Pharmacology Current Opinion in Toxicology) , Daniel R. Dietrich PhD (Editor-in-Chief, Chemico-Biological Interactions, Computational Toxicology, Journal of Toxicology and Regulatory Policy) , Sonja von Aulock PhD (Editor-in-Chief, ALTEX – Alternatives to Animal Experimentation) , Anna Bal-Price PhD (Editor-in-Chief, Reproductive Toxicology) , Michael D. Coleman PhD (Editor-in-Chief, Environmental Toxicology and Pharmacology) , Mark T.D. Cronin PhD (Editor-in-Chief, Computational Toxicology) , Paul Jennings PhD (Editor-in-Chief, Toxicology in Vitro) , Angela Mally PhD (Editor-in-Chief, Toxicology Letters) , Mathieu Vinken PhD (Editor-in-Chief, Toxicology NAM Journal) , Matthew C. Wright PhD (Editor-in-Chief, Food and Chemical Toxicology)

引用次数: 0

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

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology

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

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

The number of chemicals being placed on the market is increasing. As such, there is an increased need for screening and evaluation of chemical hazards and risks. Particularly, chemicals with intrinsic properties that are considered of very high concern are ideally identified and regulated before wide-spread use and exposure. The use of in silico tools can help to identify potential substances of very high concern (SVHCs).

Earlier, predictive models have been developed that identify potential SVHCs based on global structural similarity to known SVHCs. Here in this study, these read-across similarity models have been extended with other similarity modules, including toxicophore, biological and physicochemical similarity.

The newly developed SVHC similarity profiles do individually not outperform the existing global similarity model. However, combining these new modules in an extended similarity approach results in more comprehensive predictions and allows for improved interpretability and applicability to the broader chemical universe. As such, this new approach is thought to support model users in interpretation of the model-prediction, and can thereby contribute to better screening and prioritization of potential SVHCs.

投放市场的化学品越来越多。因此，越来越需要筛选和评价化学品的危害和风险。特别是那些被认为具有高度关注的内在特性的化学品，在广泛使用和接触之前，最好能确定并加以管制。使用计算机工具可以帮助识别潜在的高度关注物质（svhc）。此前，已经开发出预测模型，根据与已知svhc的整体结构相似性来识别潜在的svhc。在本研究中，这些跨读相似性模型已经扩展到其他相似性模块，包括毒理学，生物和物理化学相似性。新开发的SVHC相似度曲线并不优于现有的全局相似度模型。然而，将这些新模块结合在一个扩展的相似方法中，可以得到更全面的预测，并允许改进的可解释性和更广泛的化学领域的适用性。因此，这种新方法被认为支持模型用户解释模型预测，从而有助于更好地筛选和优先考虑潜在的svhc。

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引用次数: 0

Optimal experimental designs for big and small experiments in toxicology with applications to studying hormesis via metaheuristics 毒理学中大型和小型实验的最佳实验设计，并应用元启发式方法研究激效

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology

Pub Date : 2025-06-01 Epub Date: 2025-04-12 DOI: 10.1016/j.comtox.2025.100345

Brian P.H. Wu , Ray-Bing Chen , Weng Kee Wong

There are theoretical methods for constructing model-based optimal designs for a given design criterion when the sample size is large. Some of these methods may work for certain models or design criteria and some may find the optimal designs only under a restrictive setting. When the sample size is small, the theory-based methods may become invalid and the optimal designs may also not be implementable. Our first goal is to introduce nature-inspired metaheuristics to efficiently find all types of model-based optimal designs. These metaheuristic algorithms, widely used in engineering, computer science, and artificial intelligence, are generally fast and free of stringent assumptions. For our second goal, we introduce an efficient rounding method to produce an implementable, exact design for small-sized experiments based on large-sample optimal designs. To provide toxicologists with easy access to a variety of model-based optimal designs for both large and small experiments, our third goal is to develop a web-based app. This app will generate different types of model-based optimal designs, allow comparisons, and evaluate the efficiency of any design. As an application, we focus on hormesis and find model-based designs for detecting the presence of hormesis, estimating model parameters and estimating the threshold dose. The methodology is not restricted to studying hormesis only and is broadly applicable for designing other studies in toxicology and beyond.

对于给定的设计准则，当样本量较大时，已有理论方法来构建基于模型的最优设计。其中一些方法可能适用于某些模型或设计标准，而另一些方法可能仅在限制性设置下才能找到最佳设计。当样本量较小时，基于理论的方法可能失效，优化设计也可能无法实现。我们的第一个目标是引入自然启发的元启发式来有效地找到所有类型的基于模型的最优设计。这些元启发式算法广泛应用于工程、计算机科学和人工智能，通常速度快，不需要严格的假设。对于我们的第二个目标，我们引入了一种有效的舍入方法，以基于大样本优化设计为小型实验提供可实现的精确设计。为了使毒理学家能够轻松访问各种基于模型的优化设计，无论是大型还是小型实验，我们的第三个目标是开发一个基于web的应用程序。该应用程序将生成不同类型的基于模型的优化设计，允许比较，并评估任何设计的效率。作为一个应用，我们专注于激效，并找到基于模型的设计来检测激效的存在，估计模型参数和估计阈值剂量。该方法不仅限于研究激效，而且广泛适用于设计毒理学等领域的其他研究。

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引用次数: 0