QSAR Modeling on Aromatase Inhibitory Activity of 23 Triazole Fungicides by Tritium-Water Release Assay

IF 7.3 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Environmental Pollution Pub Date : 2025-02-08 DOI:10.1016/j.envpol.2025.125832
Kun Qiao, Shuting Wang, Aoxue Wang, Zhuoying Liang, Siyu Yang, Yongfang Ma, Shuying Li, Qingfu Ye, Wenjun Gui
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Abstract

The 1,2,4-triazole fungicides are extensively used in agriculture, and their impacts on aquatic organisms by continuous release are increasingly concerned. Aromatase, a rate-limiting enzyme for androgens converting to estrogens, is considered as a potential target for triazole fungicides. To reveal and predict the aromatase inhibition capacity of the existing and future developed triazole fungicides, 23 commonly used 1,2,4-triazole fungicides were used for the evaluation of their inhibitory effects (expressed as the 50% inhibitory concentration (IC50)) on human aromatase by 3H-H2O release assay in the present study. Result showed the IC50 values spanned four orders of magnitude from the strongest of 44 nM (flusilazole) to the lowest of 0.330 mM (bitertanol). The aromatase inhibitory activity of the triazoles was also verified in vivo by zebrafish use two triazoles with relatively weak inhibition. Subsequently, the Quantitative Structure-Activity Relationship (QSAR) modeling on the triazoles as aromatase inhibitors was constructed using stepwise regression analysis with the chemical structural descriptors including physicochemical, electronic and topological parameters. The optimal QSAR model was defined as pIC50 = -22.936 - 2.668 EHomo + 0.938 logD - 0.715 NHBD. The effectiveness and robustness of the model were evaluated by internal and external validation with residual assessment. The internal validation showed that the R2 and Radj2 were both higher than 0.700. The CCC and CCCExt were in acceptable levels as the cutoff value of 0.850. The cross-validation correlation coefficient Q2 and the external predictive correlation coefficients (Q2-F1, Q2-F2, and Q2-F3) were all greater than 0.600. The results of Y-Scrambling with 2000 iterations indicated the model had no accidental correlation as the average R2 of 0.166 and Q2 of -0.378. The findings offered data support for the potential risks associated with triazole fungicides in aquatic environment and provided theoretical guidance to expedite drug development and risk assessment.

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通过氚-水释放试验建立 23 种三唑类杀菌剂芳香化酶抑制活性的 QSAR 模型
1,2,4-三唑类杀菌剂在农业中应用广泛,其连续释放对水生生物的影响日益受到关注。芳香化酶是一种雄激素转化为雌激素的限速酶,被认为是三唑类杀菌剂的潜在靶标。为了揭示和预测现有和未来开发的三唑类杀菌剂对芳香酶的抑制能力,本研究以23种常用的1,2,4-三唑类杀菌剂为研究对象,采用3H-H2O释放法评价其对人芳香酶的抑制效果(以50%抑制浓度(IC50)表示)。结果表明,IC50值从最强的44 nM(氟咪唑)到最低的0.330 mM(双醇)跨越了4个数量级。三唑类药物的芳香酶抑制活性也在斑马鱼体内得到了验证,两种三唑类药物的抑制作用相对较弱。随后,利用理化、电子和拓扑参数等化学结构描述符逐步回归分析,构建了三唑类芳香酶抑制剂的定量构效关系(QSAR)模型。最优QSAR模型定义为pIC50 = -22.936 - 2.668 EHomo + 0.938 logD - 0.715 NHBD。采用残差评价法对模型的有效性和稳健性进行了内部和外部验证。内部验证结果表明,R2和Radj2均大于0.700。CCC和CCCExt的临界值为0.850,处于可接受水平。交叉验证相关系数Q2和外部预测相关系数Q2- f1、Q2- f2、Q2- f3均大于0.600。2000次y -置乱结果表明,模型不存在意外相关,平均R2为0.166,Q2为-0.378。研究结果为三唑类杀菌剂在水生环境中的潜在风险提供了数据支持,并为加快药物开发和风险评估提供了理论指导。
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来源期刊
Environmental Pollution
Environmental Pollution 环境科学-环境科学
CiteScore
16.00
自引率
6.70%
发文量
2082
审稿时长
2.9 months
期刊介绍: Environmental Pollution is an international peer-reviewed journal that publishes high-quality research papers and review articles covering all aspects of environmental pollution and its impacts on ecosystems and human health. Subject areas include, but are not limited to: • Sources and occurrences of pollutants that are clearly defined and measured in environmental compartments, food and food-related items, and human bodies; • Interlinks between contaminant exposure and biological, ecological, and human health effects, including those of climate change; • Contaminants of emerging concerns (including but not limited to antibiotic resistant microorganisms or genes, microplastics/nanoplastics, electronic wastes, light, and noise) and/or their biological, ecological, or human health effects; • Laboratory and field studies on the remediation/mitigation of environmental pollution via new techniques and with clear links to biological, ecological, or human health effects; • Modeling of pollution processes, patterns, or trends that is of clear environmental and/or human health interest; • New techniques that measure and examine environmental occurrences, transport, behavior, and effects of pollutants within the environment or the laboratory, provided that they can be clearly used to address problems within regional or global environmental compartments.
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