基于对接的高质量雌激素实验数据探索性毒理学研究分类模型。
Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data.
作者信息
Trisciuzzi Daniela, Alberga Domenico, Mansouri Kamel, Judson Richard, Cellamare Saverio, Catto Marco, Carotti Angelo, Benfenati Emilio, Novellino Ettore, Mangiatordi Giuseppe Felice, Nicolotti Orazio
机构信息
Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari 'Aldo Moro', Via E. Orabona, 4, Bari I-70126, Italy.
Dipartimento Interateneo di Fisica 'M. Merlin', Università degli Studi di Bari 'Aldo Moro', INFN, Via E. Orabona, 4, Bari I-70126, Italy.
出版信息
Future Med Chem. 2015;7(14):1921-36. doi: 10.4155/fmc.15.103. Epub 2015 Oct 6.
BACKGROUND
The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals.
RESULTS
The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Moreover, external predictions were made successfully on ten representative known estrogenic chemicals and on a set consisting of >32,000 chemicals.
CONCLUSION
The authors demonstrate that structure-based methods, widely applied to drug discovery programs, can be fairly adapted to exploratory toxicology studies.
背景
动物实验在伦理和实际操作方面存在局限性，这促使人们采用计算方法来快速筛选大量化学物质。
结果
作者推导得出24个可靠的基于对接的分类模型，这些模型能够预测美国环境保护局提供的大量化学物质的雌激素潜力。通过考虑曲线下面积（AUC）、富集因子1%（EFmax = 7.1）、负对数比值（-LR，敏感性 = 0.75时）、正对数比值（+LR，敏感性 = 0.25时）以及训练集中包含的37种参考化合物，对模型性能进行了验证。此外，还成功地对十种具有代表性的已知雌激素化学物质以及一组由超过32,000种化学物质组成的样本进行了外部预测。
结论
作者证明，广泛应用于药物发现项目的基于结构的方法可以适当地用于探索性毒理学研究。
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