Improvement and Evaluation of High Throughput Fluctuation Ames Test Using 384-well Plate with Salmonella typhimurium TA100 and TA98
Hajime Sui, Kumiko Kawakami, Noriko Sakurai, Takumi Hara, Takehiko Nohmi
- 发表年份
- 2009
- 引用次数
- 7
- 访问权限
- 开放获取
摘要
Recently, it has become necessary to increase the progress of research studies into drug discovery because of the introduction of combinatorial chemistry and robotics. Therefore, genotoxicity screening assays which can be conducted with a small amount of compound, in a short time, and which can predict the results of regulatory genotoxicity tests for pharmaceuticals are required in the early stage of research. The bacterial reverse mutation test (Ames test) is a regulatory genotoxicity test and is conducted in the early stage of non-clinical safety studies. Morita established a high throughput fluctuation Ames test using 384-well plates with Salmonella typhimurium TA100 and TA98 (Environ. Mutagen Res. 2003, 25: 23-31), which is referred to as original FAT in the present study. Here, we report an improved high throughput fluctuation Ames test (improved FAT). The improved FAT indicated a higher positive response than the original FAT in several mutagens. Furthermore, we evaluated the improved FAT with TA100 and TA98 using 40 National Toxicology Program (NTP) chemicals. As a result, there was 80.0% (32/40) concordance between the Ames test and the improved FAT. In conclusion, the improved FAT can predict the results of the Ames test with high concordance (especially its negative specificity). The improved FAT requires a much smaller amount of test chemicals than the Ames test (i.e., 5 mg vs 100 mg when using two tester strains) and is able to be automated. Thus, the improved FAT is considered to be useful as a screening test in the early stage of drug discovery.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992