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肝脏特异性致癌物的判别函数分析

Discriminant function analyses of liver-specific carcinogens.

作者信息

Beger Richard D, Young John F, Fang Hong

机构信息

Division of Chemistry, Food & Drug Administration, National Center for Toxicological Research, Jefferson, AR 72079, USA.

出版信息

J Chem Inf Comput Sci. 2004 May-Jun;44(3):1107-10. doi: 10.1021/ci0342829.

Abstract

The ability to predict organ-specific carcinogenicity would aid FDA reviewers in evaluating new chemical applications. A NCTR liver cancer database (NCTRlcdb) containing 999 compounds has been developed with three sets of descriptors. The NCTRlcdb has Cerius2, Molconn-Z, and (13)C NMR descriptors for each compound. Each compound in the database was assigned a liver cancer or a nonliver cancer classification. Compounds within the NCTRlcdb were evaluated for liver-specific carcinogenicity using partial least squares principal component discriminant function (PLS-DF) modeling. PLS-DF models based on estimated a priori classification probabilities of 0.29 for liver cancer and 0.71 for noncancer yielded an overall predictability of 70.6% which was comprised of a liver cancer sensitivity of 18.8% and a noncancer specificity of 90.8%. PLS-DF models based on equal a priori classification probabilities, 0.50 for liver cancer and 0.5 for noncancer, yielded an overall predictability of 61.0% which was comprised of a liver cancer sensitivity of 50.5% and a noncancer specificity of 65.3%.

摘要

预测器官特异性致癌性的能力将有助于美国食品药品监督管理局(FDA)的审评人员评估新的化学应用。一个包含999种化合物的国家毒理学研究中心(NCTR)肝癌数据库(NCTRlcdb)已利用三组描述符开发而成。NCTRlcdb对每种化合物都有Cerius2、Molconn-Z和碳-13核磁共振(¹³C NMR)描述符。数据库中的每种化合物都被指定为肝癌或非肝癌分类。使用偏最小二乘主成分判别函数(PLS-DF)模型对NCTRlcdb中的化合物进行肝脏特异性致癌性评估。基于肝癌先验分类概率估计为0.29、非癌先验分类概率估计为0.71的PLS-DF模型,总体预测能力为70.6%,其中肝癌敏感性为18.8%,非癌特异性为90.8%。基于肝癌和非癌先验分类概率相等(均为0.50)的PLS-DF模型,总体预测能力为61.0%,其中肝癌敏感性为50.5%,非癌特异性为65.3%。

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