Indiana University-Purdue University Indianapolis (IUPUI) / Since exposure to carcinogens can occur in the environment from various point sources, cancer risk assessment attempts to define and limit potential exposure such that the risk of developing cancer is negligible. While cancer risk assessment is widely used with certain methodologies well accepted in the scientific literature and regulatory guidances, there are still gaps which increase uncertainties when assessing risk including: (1) mixtures of genotoxins, (2) genotoxic metabolites, and (3) nongenotoxic carcinogens. An in silico model was developed to predict the cancer risk of a genotoxin which improved methodology for a single compound and mixtures. Monte Carlo simulations performed with a carcinogenicity potency database to estimate the overall carcinogenic risk of a mixture of genotoxic compounds showed that structural similarity would not likely increase the overall cancer risk. A cancer risk model was developed for genotoxic metabolites using excretion material in both animals and humans to determine the probability not exceeding a 1 in 100,000 excess cancer risk. Two model nongenotoxic compounds (fenofibrate and methapyraline) were tested in short-term microarray studies to develop a framework for cancer risk assessment. It was determined that a threshold for potential key events could be derived using benchmark dose analysis in combination with well developed ontologies (Kegg/GO), which were at or below measured tumorigenic and precursor events. In conclusion, informatics was effective in advancing toxicology-based cancer risk assessment using databases and predictive techniques which fill critical gaps in its methodology.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/2130 |
| Date | 03 May 2010 |
| Creators | Bercu, Joel P. |
| Contributors | Mahoui, Malika, Romero, Pedro R., Stevens, James L., Jones, Josette F., Palakal, Mathew J. |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
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