A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols.
A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols.
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DOI：
10.1016/j.comtox.2017.09.001
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发表时间：
2017-11-01
期刊：
影响因子：
--
通讯作者：
Judson, Richard
中科院分区：
文献类型：
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作者：
Pradeep, Prachi;Mansouri, Kamel;Judson, Richard
交叉读取是一种重要的数据缺口填补技术，用于监管危害识别和风险评估的类别和模拟方法。虽然有许多技术指导，描述了如何开发类别/类比方法，但仍然缺乏评估和证实类比有效性（适用性）的实用原则。该案例研究使用受阻酚作为化学类别的示例，以确定：（1）三种结构指纹/描述符方法（PubChem，ToxPrints和MoSS MCSS）识别用于交叉读取的类似物以预测雌激素受体（ER）结合活性的能力，以及（2）数据置信度测量，物理化学性质和化学R基团性质作为过滤器的实用性，以改善ER结合预测。训练数据集包括462个受阻酚和257个非受阻酚。对于每种感兴趣的化学品（靶标），从两个数据集（受阻酚和非受阻酚）中鉴定源类似物，这两个数据集已经通过指纹/描述符方法和两个截止值进行了表征：（1）最小相似性距离（范围：0.1 - 0.9）和（2）N个最接近的类似物（范围：1 - 10）。然后使用：（1）苯酚的物理化学性质（称为全局过滤）和（2）与活性羟基相邻的R-基团的物理化学性质（称为局部过滤）过滤类似物。基于N个最接近的类似物的多数投票，对每个目标化学品进行交叉预测。结果表明：（1）ER活性的一致性随着结构相似性而增加，而与结构指纹/描述符方法无关，（2）增加的数据置信度显著改善了跨读预测，以及（3）使用全局和局部特性过滤类似物可以帮助识别更合适的类似物。该案例研究表明，基础实验数据的质量和使用终点相关的化学描述符来评估源类似物对于实现稳健的交叉预测至关重要。
Read-across is an important data gap filling technique used within category and analog approaches for regulatory hazard identification and risk assessment. Although much technical guidance is available that describes how to develop category/analog approaches, practical principles to evaluate and substantiate analog validity (suitability) are still lacking. This case study uses hindered phenols as an example chemical class to determine: (1) the capability of three structure fingerprint/descriptor methods (PubChem, ToxPrints and MoSS MCSS) to identify analogs for read-across to predict Estrogen Receptor (ER) binding activity and, (2) the utility of data confidence measures, physicochemical properties, and chemical R-group properties as filters to improve ER binding predictions. The training dataset comprised 462 hindered phenols and 257 non- hindered phenols. For each chemical of interest (target), source analogs were identified from two datasets (hindered and non-hindered phenols) that had been characterized by a fingerprint/descriptor method and by two cut-offs: (1) minimum similarity distance (range: 0.1 - 0.9) and, (2) N closest analogs (range: 1 - 10). Analogs were then filtered using: (1) physicochemical properties of the phenol (termed global filtering) and, (2) physicochemical properties of the R-groups neighboring the active hydroxyl group (termed local filtering). A read-across prediction was made for each target chemical on the basis of a majority vote of the N closest analogs. The results demonstrate that: (1) concordance in ER activity increases with structural similarity, regardless of the structure fingerprint/descriptor method, (2) increased data confidence significantly improves read-across predictions, and (3) filtering analogs using global and local properties can help identify more suitable analogs. This case study illustrates that the quality of the underlying experimental data and use of endpoint relevant chemical descriptors to evaluate source analogs are critical to achieving robust read-across predictions.