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Updated: Jun 9, 2026

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Changes in Mammary Gland Morphology and Breast Cancer Risk in Rats
Published on: October 16, 2010
Mammary carcinogen-protein binding potentials: novel and biologically relevant structure-activity relationship model
A R Cunningham1, S Qamar, C A Carrasquer
1James Graham Brown Cancer Center, University of Louisville, KY, USA. al.cunningham@louisville.edu
SAR and QSAR in Environmental Research
|September 7, 2010
Summary
This study introduces novel carcinogen prediction models using biological descriptors like ligand-receptor interactions, improving accuracy over traditional chemical descriptors for rat mammary carcinogens.
Area of Science:
- Toxicology
- Computational Chemistry
- Molecular Biology
Background:
- Structure-activity relationship (SAR) models for carcinogenesis traditionally rely on chemical descriptors.
- A gap exists in understanding the link between chemical structure and carcinogenic activity via biological targets.
Purpose of the Study:
- To develop and validate new SAR models for rat mammary carcinogens using biological descriptors.
- To investigate ligand-receptor interactions as predictors of carcinogenic potential.
- To enhance the predictivity of carcinogenicity models by incorporating biological insights.
Main Methods:
- Utilized the cat-SAR expert system to develop models based on biological descriptors (ligand-receptor interactions).
- Performed virtual screening of carcinogens, non-carcinogens, and mammary carcinogens against 5494 target proteins.
- Employed leave-one-out and external validation techniques for model assessment.
- Developed hybrid fragment-ligand models to improve predictive accuracy.
Main Results:
- Ligand-based models achieved 71% (carcinogen-non-carcinogen) and 72% (carcinogen-non-mammary carcinogen) concordance.
- Hybrid fragment-ligand models significantly improved concordance to 85% and 83% in internal validation.
- External validation of hybrid models showed concordances of 81% and 76%.
- Identified biologically relevant protein targets for known rat mammary carcinogens (PhIP, atrazine, indomethacin).
Conclusions:
- Biological descriptors, specifically ligand-receptor interactions, offer enhanced predictivity for carcinogenicity compared to chemical descriptors.
- The cat-SAR system with biological descriptors provides a valuable tool for understanding carcinogen mechanisms.
- This approach bridges the gap between chemical structure and carcinogenic activity by focusing on molecular targets.
- The developed models show promise for improving risk assessment and drug development by identifying potential carcinogens.
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