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Published on: August 28, 2019
In Silico Screening of Chemicals for Genetic Toxicity Using MDL-QSAR, Nonparametric Discriminant Analysis, E-State,
Joseph F Contrera1, Edwin J Matthews, Naomi L Kruhlak
1Informatics and Computational Safety Analysis Staff, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Toxicology Mechanisms and Methods
|December 22, 2009
Summary
New Quantitative Structure-Activity Relationship (QSAR) software models rapidly screen chemicals for genetic toxicity. These validated models predict mutagenicity, clastogenicity, and DNA damage, aiding safety assessments for pharmaceuticals and industrial chemicals.
Area of Science:
- Computational toxicology and cheminformatics
- Drug discovery and development
- Chemical safety and risk assessment
Background:
- Genetic toxicity testing is crucial for evaluating the safety of pharmaceuticals, food, and industrial chemicals.
- Quantitative Structure-Activity Relationship (QSAR) software provides a fast and economical method for prioritizing chemicals based on their genotoxic potential.
- Existing QSAR models require expansion to cover a comprehensive range of genetic toxicity endpoints.
Purpose of the Study:
- To develop and validate a comprehensive suite of complementary QSAR models for predicting genetic toxicity.
- To create new models for mutagenicity, clastogenicity, and DNA damage, building upon previous work.
- To assess the predictive performance and utility of these QSAR models for large-scale chemical screening.
Main Methods:
- Development of eight new MDL-QSAR models using atom-type E-state, simple connectivity, and molecular property descriptors.
- Application of nonparametric discriminant analysis for model creation.
- Internal validation using 10% leave-group-out studies, including specificity, sensitivity, and Receiver Operator Characteristic (ROC) analysis.
Main Results:
- Models demonstrated good predictive performance with specificity ranging from 63% to 88% and sensitivity from 39% to 74%.
- Receiver Operator Characteristic (ROC) values were >= 2.00, indicating robust predictive capabilities.
- MDL-QSAR models showed favorable comparison to existing MultiCase MC4PC genotoxicity models.
Conclusions:
- The developed MDL-QSAR models offer good specificity, sensitivity, and coverage for predicting genotoxic potential.
- These models provide a rapid and cost-effective solution for large-scale screening of chemical compounds.
- The QSAR models are valuable tools for the chemical and pharmaceutical industries and for regulatory decision support.
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Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Forward genetic screens
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01:25Mutagenicity and Carcinogenicity
Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
01:28Structure-Activity Relationships and Drug Design
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SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
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