Young John, Tong Weida, Fang Hong, Xie Qian, Pearce Bruce, Hashemi Ray, Beger Richard, Cheeseman Mitchell, Chen James, Chang Yuan-Chin, Kodell Ralph
Division of Biometry and Risk Assessment, Food and Drug Administration, National Center for Toxicological Research, Jefferson, Arkansas, USA.
J Toxicol Environ Health A. 2004 Sep 10;67(17):1363-89. doi: 10.1080/15287390490471479.
FDA reviewers need a means to rapidly predict organ-specific carcinogenicity to aid in evaluating new chemicals submitted for approval. This research addressed the building of a database to use in developing a predictive model for such an application based on structure-activity relationships (SAR). The Internet availability of the Carcinogenic Potency Database (CPDB) provided a solid foundation on which to base such a model. The addition of molecular structures to the CPDB provided the extra ingredient necessary for SAR analyses. However, the CPDB had to be compressed from a multirecord to a single record per chemical database; multiple records representing each gender, species, route of administration, and organ-specific toxicity had to be summarized into a single record for each study. Multiple studies on a single chemical had to be further reduced based on a hierarchical scheme. Structural cleanup involved removal of all chemicals that would impede the accurate generation of SAR type descriptors from commercial software programs; that is, inorganic chemicals, mixtures, and organometallics were removed. Counterions such as Na, K, sulfates, hydrates, and salts were also removed for structural consistency. Structural modification sometimes resulted in duplicate records that also had to be reduced to a single record based on the hierarchical scheme. The modified database containing 999 chemicals was evaluated for liver-specific carcinogenicity using a variety of analysis techniques. These preliminary analyses all yielded approximately the same results with an overall predictability of about 63%, which was comprised of a sensitivity of about 30% and a specificity of about 77%.
美国食品药品监督管理局（FDA）的审评人员需要一种方法来快速预测特定器官的致癌性，以协助评估提交审批的新化学物质。这项研究致力于构建一个数据库，用于基于构效关系（SAR）开发此类应用的预测模型。致癌潜能数据库（CPDB）在互联网上的可用性为此类模型提供了坚实的基础。向CPDB添加分子结构为SAR分析提供了必要的额外要素。然而，CPDB必须从每个化学物质的多记录数据库压缩为单记录数据库；代表每种性别、物种、给药途径和特定器官毒性的多个记录必须汇总为每项研究的单记录。基于分层方案，必须进一步减少对单一化学物质的多项研究。结构清理包括去除所有会妨碍从商业软件程序准确生成SAR类型描述符的化学物质；也就是说，去除无机化学物质、混合物和有机金属化合物。为保持结构一致性，还去除了诸如钠、钾、硫酸盐、水合物和盐等抗衡离子。结构修饰有时会导致重复记录，这些记录也必须根据分层方案减少为单记录。使用多种分析技术对包含999种化学物质的修改后数据库进行了肝脏特异性致癌性评估。这些初步分析均得出大致相同的结果，总体可预测性约为63%，其中灵敏度约为30%，特异性约为77%。
