Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species.
Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species.
复制标题
DOI：
10.1021/tx900326k
复制
发表时间：
2010-01
影响因子：
4.1
通讯作者：
Tropsha, Alexander
中科院分区：
文献类型：
--
作者：
Fourches, Denis;Barnes, Julie C.;Day, Nicola C.;Bradley, Paul;Reed, Jane Z.;Tropsha, Alexander
药物性肝损伤(DILI)是引起药物磨损的主要原因之一。根据候选药物的化学结构预测其对肝脏的影响的能力，对于帮助指导实验性药物发现项目走向更安全的药物至关重要。在这项研究中，我们汇编了951种化合物的数据集，据报道，这些化合物在不同物种的肝脏中产生了广泛的影响，包括人类、啮齿动物和非啮齿动物。这个数据集的肝脏效应是作为断言元数据获得的，这些元数据是使用词汇和语言方法以及本体规则的独特组合从MEDLINE摘要生成的。我们使用传统的化学信息学方法分析了这一数据集，并解决了与肝脏效应的跨物种一致性、人类肝脏效应的化学决定因素以及预测给定化合物是否可能引起人类肝脏效应有关的几个问题。我们发现，不同物种之间肝脏效应的一致性相对较低(约39-44%)，这提出了物种特异性可能取决于特定的化学结构特征。根据化合物的化学相似性对其进行聚类，并检查相似化合物的物种依赖肝效应谱的预期相似性。在大多数情况下，观察到同一簇成员的相似轮廓，但一些化合物显示为异常值。离群值是从MEDLINE和其他数据源重新生成的集中断言的主题。在某些情况下，根据化合物的化学相似性，确定了与预期一致的其他生物学断言。这些断言被进一步转换为潜在化学物质的二进制注释(即肝脏效应与无肝脏效应)，并产生了二元QSAR模型来预测一种化合物是否会在人类身上产生肝脏效应。尽管数据存在明显的异质性，但通过外部五重交叉验证程序评估，模型已显示出良好的预测能力。通过对模型开发后检索或研究的化合物的应用，进一步证实了二元QSAR模型的外部预测能力。据我们所知，这是第一次对化学毒性预测进行研究，将QSAR建模和其他化学信息学技术应用于通过有限人工管理的自动文本挖掘方法生成的观测数据，为生成和建模化学毒理学数据开辟了新的机会。
Drug Induced Liver Injury (DILI) is one of the main causes of drug attrition. The ability to predict the liver effects of drug candidates from their chemical structure is critical to help guiding experimental drug discovery projects towards safer medicines. In this study, we have compiled a dataset of 951 compounds reported to produce a wide range of effects in the liver in different species, comprising humans, rodents, and non-rodents. The liver effects for this dataset were obtained as assertional meta-data, generated from MEDLINE abstracts using a unique combination of lexical and linguistic methods and ontological rules. We have analyzed this dataset using conventional cheminformatics approaches and addressed several questions pertaining to cross-species concordance of liver effects, chemical determinants of liver effects in humans, and the prediction of whether a given compound is likely to cause a liver effect in humans. We found that the concordance of liver effects was relatively low (ca. 39–44%) between different species raising the possibility that species specificity could depend on specific features of chemical structure. Compounds were clustered by their chemical similarity, and similar compounds were examined for the expected similarity of their species-dependent liver effect profiles. In most cases, similar profiles were observed for members of the same cluster, but some compounds appeared as outliers. The outliers were the subject of focused assertion re-generation from MEDLINE, as well as other data sources. In some cases, additional biological assertions were identified which were in line with expectations based on compounds' chemical similarity. The assertions were further converted to binary annotations of underlying chemicals (i.e., liver effect vs. no liver effect), and binary QSAR models were generated to predict whether a compound would be expected to produce liver effects in humans. Despite the apparent heterogeneity of data, models have shown good predictive power assessed by external five-fold cross validation procedures. The external predictive power of binary QSAR models was further confirmed by their application to compounds that were retrieved or studied after the model was developed. To the best of our knowledge, this is the first study for chemical toxicity prediction that applied QSAR modeling and other cheminformatics techniques to observational data generated by the means of automated text mining with limited manual curation, opening up new opportunities for generating and modeling chemical toxicology data.
登录
查看更多内容
影响因子：
11.8
作者：
Kaplowitz, N
通讯作者：
Kaplowitz, N
影响因子：
5.6
作者：
Tetko, Igor V.;Sushko, Iurii;Varnek, Alexandre
通讯作者：
Varnek, Alexandre
影响因子：
1.7
作者：
Varnek, Alexandre;Fourches, Denis;Marcou, Gilles
通讯作者：
Marcou, Gilles
影响因子：
2.9
作者：
Clark, RD;Wolohan, PRN;Sussman, NL
通讯作者：
Sussman, NL
影响因子：
3.4
作者：
Olson, H;Betton, G;Heller, A
通讯作者：
Heller, A