PREDICTION OF HEPATOCARCINOGENIC EFFECT OF STRUCTURAL DIVERSE CHEMICALS BY COMPUTATIONAL TECHNOLOGIES
Published: 28 May 2015
Abstract: The primary testing strategy to identify chemical (hepato)carcinogens largely relies on the 2-year rodent bioassay, which is time-consuming and labor-intensive. There is an increasing effort to develop alternative approaches to prioritize the chemicals for, supplement, or even replace the cancer bioassay. In silico approaches based on quantitative structure-activity relationships (QSAR) are rapid and inexpensive and thus have been investigated for such purposes. Since the correlation between carcinogenicity and Ames mutagenicty test results was found to be significant enough it is expected that models based on Ames data could be used successfully for identification of chemical carcinogens. In the current study the implemented profiler for DNA binding prediction in non-commercial software tool was used to predict the hepatocarcinogenic effect of 55 representative chemicals. The obtained results show that 73% of the hepatocarcinogens can be successfully identified as genotoxic carcinogens. The role of nongentoxic mechanisms has been assessed by application of profiling scheme for identification of nongenotoxic chemical carcinogens. As a result of combined application of both profilers 87% of hepatocarcinogens have been correctly identified.
Keywords: liver, hepatocarcinogenicity, qsar, genotoxicity, metabolic activation
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