Developing Structure-Activity Relationships for the Prediction of Hepatotoxicity
Developing Structure-Activity Relationships for the Prediction of Hepatotoxicity
复制标题
DOI：
10.1021/tx1000865
复制
发表时间：
2010-07-01
影响因子：
4.1
通讯作者：
Pelletier, Dennis J.
中科院分区：
文献类型：
--
作者：
Greene, Nigel;Fisk, Lilia;Pelletier, Dennis J.
药物性肝损伤是一个令人关注的主要问题，并已导致大量上市药物的撤回。了解化学品的结构活性关系（SAR）可以在药物开发过程的早期识别潜在的毒性作用，并帮助避免此类问题作出重大贡献。这一过程可以通过使用现有的毒性数据和相关化合物的生物过程的机械理解来支持。在已发表的文献中，这些信息往往分散在不同的来源，在质量和内容上可能是多样和无结构的。目前的工作探讨了收集和使用这些数据开发肝毒性终点的新SAR是否可行，并扩展了该领域目前可用的有限信息。肝毒性数据的审查被用来建立一个结构可搜索的数据库，分析该数据库以识别与对肝脏的不良影响相关的化学类别。然后对已发表的文献进行检索，以确定其他支持证据，并将所得信息纳入数据库。对这些整理的信息进行了评价，并用于确定所确定的每个类别的SAR范围。收集了超过1266种化学品的数据，并开发了38类SAR。SAR已被实施为结构警报使用Derek for Windows（DfW），一个基于知识的专家系统，允许明确支持和透明的预测。使用定制的DfW第10版知识库进行的评价活动表明，总体一致性为56%，特异性和灵敏度值分别为73%和46%。所采用的方法表明，复杂终点的SAR可源自已发表的数据，用于新化合物的计算机毒性评估。
Drug-induced liver injury is a major issue of concern and has led to the withdrawal of a significant number of marketed drugs. An understanding of structure activity relationships (SARs) of chemicals can make a significant contribution to the identification of potential toxic effects early in the drug development process and aid in avoiding such problems. This process can be supported by the use of existing toxicity data and mechanistic understanding of the biological processes for related compounds. In the published literature, this information is often spread across diverse sources and can be varied and unstructured in quality and content. The current work has explored whether it is feasible to collect and use such data for the development of new SARs for the hepatotoxicity endpoint and expand upon the limited information currently available in this area. Reviews of hepatotoxicity data were used to build a structure-searchable database, which was analyzed to identify chemical classes associated with an adverse effect on the liver. Searches of the published literature were then undertaken to identify additional supporting evidence, and the resulting information was incorporated into the database. This collated information was evaluated and used to determine the scope of the SARs for each class identified. Data for over 1266 chemicals were collected, and SARs for 38 classes were developed. The SARs have been implemented as structural alerts using Derek for Windows (DfW), a knowledge-based expert system, to allow clearly supported and transparent predictions. An evaluation exercise performed using a customized DfW version 10 knowledge base demonstrated an overall concordance of 56% and specificity and sensitivity values of 73% and 46%, respectively. The approach taken demonstrates that SARs for complex endpoints can be derived from the published data for use in the in silico toxicity assessment of new compounds.