MP6 Quantitative Structure Activity Relationships (QSARs)
Monday, 14 November 2005: 8:00 AM - 5:30 PM in Exhibit Hall

(TAN-1117-507801) Construction of Carcinogenicity Database CAESAR.

Tanabe, K¹, Ohmori, N¹, Ono, S¹, Suzuki, T², ¹ Chiba Institute of Technology, Narashino, Chiba, Japan² Toyo University, Bunkyoku, Tokyo, Japan

ABSTRACT- Among chemical substances available in market, quite few are ascertained on safety, and reliable data on chemical safety are few and sparse. So, it is necessary to construct a database which collect reliable data on chemical safety. Since animal test to get data on chemical safety takes much cost and period, it is impossible to get safety data on every unascertained chemical substance by animal test. Here a computer prediction with quantitative structure-activity relationship (QSAR) is meaningful for screening. Therefore many attempts based on QSAR models for estimating the chemical safety such as carcinogenicity have been made. Several systems for predicting carcinogenicity were applied to the Predictive Toxicology Challenge contest, but their performances were all low. This is because the chemical safety data, especially of carcinogenicity, consist of two discrete values (carcinogenic or non-carcinogenic) for diverse chemical substances. QSAR approaches succeeded in predicting carcinogenicity of congenial substances, using experimental data with relative carcinogenicity, but failed to predict carcinogenicity of non-congenial substances using two discrete carcinogenicity data. So, if we construct a chemical safety database, especially for carcinogenicity consisting of relative strength or reliability, QSAR approach may succeed in predicting the safety for non-congenial substances. We are constructing a chemical safety database named CAESAR (Computer-Aided Evaluation of Chemical Safety with QSAR), which consists of two databases. One contains reliable, critically reviewed, experimental hazard data with relative hazard strength or reliability on selected chemical substances. Another contains hazard data predicted with QSAR for numerous chemical substances commercially available. As a first step, we are constructing a carcinogenicity database. Reliable experimental carcinogenicity data on about 1,000 chemical substances are collected from various sources such as IARC, NTP and others, and their reliabilities are ranked into nine categories. Reliabilities of carcinogenicity for diverse chemical substances are predicted using those experimental data with a neural network method which we already developed.

Key words: QSAR, carcinogenicity, database, neural network