Alves Vinicius M, Capuzzi Stephen J, Muratov Eugene, Braga Rodolpho C, Thornton Thomas, Fourches Denis, Strickland Judy, Kleinstreuer Nicole, Andrade Carolina H, Tropsha Alexander
Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.
Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil.
Green Chem. 2016 Dec 21;18(24):6501-6515. doi: 10.1039/C6GC01836J. Epub 2016 Oct 6.
Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential.
皮肤致敏是一种主要的环境和职业健康危害。尽管已经对许多化学物质进行了人体评估，但迄今为止尚未对这些数据进行建模。我们收集、整理、分析并比较了现有的人体和LLNA数据。利用这些数据，我们开发了可靠的计算模型，并将其应用于化学文库的虚拟筛选，以识别潜在的皮肤致敏剂。对于一组135种独特的化学物质，小鼠LLNA与人体皮肤致敏反应之间的总体一致性较低（R = 28-43%），尽管有几类化学物质具有较高的一致性。我们成功地开发了所有可用人体数据的预测性QSAR模型，外部正确分类率为71%。一个整合了一致的QSAR预测和LLNA结果的共识模型提供了更高的CCR，为82%，但代价是外部数据集覆盖范围缩小（52%）。我们使用开发的QSAR模型对CosIng数据库进行虚拟筛选，识别出1061种潜在的皮肤致敏剂；对于其中17种化合物，我们发现了已发表的关于其皮肤致敏作用的证据。本文报道的模型为跨多种化学数据的人体皮肤致敏评估提供了比LLNA测试更准确的替代方法。此外，它们还可用于指导有毒化合物的结构优化，以降低其皮肤致敏潜力。
