Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds.
Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds.
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DOI：
10.1016/j.taap.2014.12.014
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发表时间：
2015-04-15
影响因子：
3.8
通讯作者：
Tropsha A
中科院分区：
文献类型：
--
作者：
Alves VM;Muratov E;Fourches D;Strickland J;Kleinstreuer N;Andrade CH;Tropsha A
反复暴露于化学制剂可在固有易感个体中诱导免疫反应，导致皮肤致敏。虽然许多化学品已被报告为皮肤致敏剂，但很少有严格验证的QSAR模型，其具有使用大量化学上不同的化合物开发的定义的适用性域（AD）。在这项研究中，我们的目标是编译，策划和整合最大的公开可用的数据集相关的化学诱导的皮肤致敏，使用这些数据来生成严格验证和QSAR模型的皮肤致敏，并采用这些模型作为一个虚拟的筛选工具，用于识别环境化学品中的假定致敏物。我们遵循模型构建和验证的最佳实践，使用随机森林建模技术结合SiRMS和Dragon描述符，通过我们的预测QSAR工作流程实施。当在广泛AD内的多个外部验证集上进行评估时，区分致敏物和非致敏物的QSAR模型的正确分类率（CCR）为71-88%，阳性（对于致敏物）和阴性（对于非致敏物）预测率分别为85%和79%。当与OECD QSAR工具箱中包含的皮肤致敏模块以及公开可用的VEGA软件中的皮肤致敏模型相比时，我们的模型显示出对相同组的外部化合物的预测准确性显著更高，如通过阳性预测率、阴性预测率和CCR评估的。这些模型被应用于识别可能的皮肤或感觉器官毒物作为实验验证的主要候选人的记分卡数据库中的推定化学危害。
Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using random forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers were 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the ScoreCard database of possible skin or sense organ toxicants as primary candidates for experimental validation.