Tox21 Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Chemicals and Drugs
Tox21 Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Chemicals and Drugs
复制标题
DOI：
10.3389/fenvs.2015.00085
复制
发表时间：
2016-01-01
影响因子：
4.6
通讯作者：
Simeonov, Anton
中科院分区：
文献类型：
--
作者：
Huang, Ruili;Xia, Menghang;Simeonov, Anton
每天有成千上万种生物特性知之甚少的化学物质被释放到环境中。高通量筛选（HTS）可能是传统毒性测试的一种更有效和更具成本效益的替代方法。使用HIS，可以分析化学品的潜在不良影响，并优先考虑可管理的数量进行更深入的测试。重要的是，它可以为毒性机制提供线索。Tox 21项目已经产生了超过5000万个定量高通量筛选（qHTS）数据点。一个包含数千种化合物（包括环境化学品和药物）的库针对一组核受体（NR）和应激反应（SR）途径测定进行筛选。国家转化科学推进中心（NCATS）组织了一次国际数据挑战赛，以“众包”数据并建立预测毒性模型。这项挑战要求一群研究人员使用这些数据来阐明化合物对生物化学和细胞途径的干扰程度可以从化学结构数据中推断出来。针对Tox 21文库生成的数据用作该建模挑战的训练集。该竞赛吸引了来自18个不同国家的参与者，以开发旨在更好地预测化学品毒性的计算模型。从近400个模型提交的获奖模型都达到了>80%的准确率。几个模型超过90%的准确性，这是由受试者工作特征曲线（AUC-ROC）下的面积测量。将获奖模型与Tox 21筛选数据中获得的知识相结合，有望提高社区对潜在人类健康问题的新型化学品进行优先排序的能力。
Tens of thousands of chemicals with poorly understood biological properties are released into the environment each day. High-throughput screening (HTS) is potentially a more efficient and cost-effective alternative to traditional toxicity tests. Using HIS, one can profile chemicals for potential adverse effects and prioritize a manageable number for more in-depth testing. Importantly, it can provide clues to mechanism of toxicity. The Tox21 program has generated >50 million quantitative high-throughput screening (qHTS) data points. A library of several thousands of compounds, including environmental chemicals and drugs, is screened against a panel of nuclear receptor (NR) and stress response (SR) pathway assays. The National Center for Advancing Translational Sciences (NCATS) has organized an international data challenge in order to "crowd-source" data and build predictive toxicity models. This Challenge asks a "crowd" of researchers to use these data to elucidate the extent to which the interference of biochemical and cellular pathways by compounds can be inferred from chemical structure data. The data generated against the Tox21 library served as the training set for this modeling Challenge. The competition attracted participants from 18 different countries to develop computational models aimed at better predicting chemical toxicity. The winning models from nearly 400 model submissions all achieved >80% accuracy. Several models exceeded 90% accuracy, which was measured by area under the receiver operating characteristic curve (AUC-ROC). Combining the winning models with the knowledge already gained from Tox21 screening data are expected to improve the community's ability to prioritize novel chemicals with respect to potential human health concern.