Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

Journal Article · Sun Jul 01 04:00:00 EDT 2007 · Toxicology and Applied Pharmacology

DOI:https://doi.org/10.1016/j.taap.2007.03.012· OSTI ID:20976967

Valerio, Luis G ^[1]; Arvidson, Kirk B ^[2]; Chanderbhan, Ronald F ^[1]; Contrera, Joseph F ^[3]

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1. Division of Biotechnology and GRAS Notice Review, US Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Food Additive Safety, HFS-255, 5100 Paint Branch Parkway, College Park, MD 20740 (United States)
2. Division of Food Contact Notifications, US Food and Drug Administration, Center for Food Safety and Applied Nutrition, 7Office of Food Additive Safety, HFS-255, 5100 Paint Branch Parkway, College Park, MD 20740 (United States)
3. US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff, Silver Spring, MD 20993 (United States)

Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest is MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals, comprised primarily of pharmaceutical, industrial and some natural products developed under an FDA-MDL cooperative research and development agreement (CRADA). The predictive performance for this group of dietary natural products and the control group was 97% sensitivity and 80% concordance. Specificity was marginal at 53%. This study finds that the in silico QSAR analysis employing this software's rodent carcinogenicity database is capable of identifying the rodent carcinogenic potential of naturally occurring organic molecules found in the human diet with a high degree of sensitivity. It is the first study to demonstrate successful QSAR predictive modeling of naturally occurring carcinogens found in the human diet using an external validation test. Further test validation of this software and expansion of the training data set for dietary chemicals will help to support the future use of such QSAR methods for screening and prioritizing the risk of dietary chemicals when actual animal data are inadequate, equivocal, or absent.

• OSTI ID: 20976967
• Journal Information: Toxicology and Applied Pharmacology, Journal Name: Toxicology and Applied Pharmacology Journal Issue: 1 Vol. 222; ISSN TXAPA9; ISSN 0041-008X
• Country of Publication: United States
• Language: English

Related Subjects

60 APPLIED LIFE SCIENCES
BIOASSAY
CARCINOGENESIS
CARCINOGENS
COMPUTER CODES
DIET
DRUGS
EVALUATION
FOOD
FOOD ADDITIVES
FORECASTING
HEALTH HAZARDS
HUMAN POPULATIONS
NEOPLASMS
NUTRITION
PERFORMANCE
RISK ASSESSMENT
RODENTS
SAFETY
SAFETY ANALYSIS
SENSITIVITY
SIMULATION
SPECIFICITY
STRUCTURE-ACTIVITY RELATIONSHIPS
TOXICITY
TRAINING
US FDA