Zhang Jun, Hsieh Jui-Hua, Zhu Hao
Department of Chemistry, Rutgers University, Camden, New Jersey, United States of America; The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey, United States of America.
Biomolecular Screening Branch, Division of National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, United States of America.
PLoS One. 2014 Jun 20;9(6):e99863. doi: 10.1371/journal.pone.0099863. eCollection 2014.
In vitro bioassays have been developed and are currently being evaluated as potential alternatives to traditional animal toxicity models. Already, the progress of high throughput screening techniques has resulted in an enormous amount of publicly available bioassay data having been generated for a large collection of compounds. When a compound is tested using a collection of various bioassays, all the testing results can be considered as providing a unique bio-profile for this compound, which records the responses induced when the compound interacts with different cellular systems or biological targets. Profiling compounds of environmental or pharmaceutical interest using useful toxicity bioassay data is a promising method to study complex animal toxicity. In this study, we developed an automatic virtual profiling tool to evaluate potential animal toxicants. First, we automatically acquired all PubChem bioassay data for a set of 4,841 compounds with publicly available rat acute toxicity results. Next, we developed a scoring system to evaluate the relevance between these extracted bioassays and animal acute toxicity. Finally, the top ranked bioassays were selected to profile the compounds of interest. The resulting response profiles proved to be useful to prioritize untested compounds for their animal toxicity potentials and form a potential in vitro toxicity testing panel. The protocol developed in this study could be combined with structure-activity approaches and used to explore additional publicly available bioassay datasets for modeling a broader range of animal toxicities.
体外生物测定法已经得到发展，目前正在作为传统动物毒性模型的潜在替代方法进行评估。高通量筛选技术的进步已经产生了大量针对大量化合物的公开可用生物测定数据。当使用各种生物测定法对一种化合物进行测试时，所有测试结果都可以被视为为该化合物提供了一个独特的生物特征，该特征记录了该化合物与不同细胞系统或生物靶点相互作用时所诱导的反应。利用有用的毒性生物测定数据对具有环境或药学意义的化合物进行特征分析是研究复杂动物毒性的一种有前景的方法。在本研究中，我们开发了一种自动虚拟特征分析工具来评估潜在的动物毒物。首先，我们自动获取了一组4841种化合物的所有PubChem生物测定数据，这些化合物具有公开可用的大鼠急性毒性结果。接下来，我们开发了一个评分系统来评估这些提取的生物测定与动物急性毒性之间的相关性。最后，选择排名靠前的生物测定来对感兴趣的化合物进行特征分析。所得的反应特征被证明有助于对未测试化合物的动物毒性潜力进行排序，并形成一个潜在的体外毒性测试面板。本研究中开发的方案可以与构效关系方法相结合，并用于探索更多公开可用的生物测定数据集，以建立更广泛的动物毒性模型。
