Document Detail


In silico screening of chemicals for bacterial mutagenicity using electrotopological E-state indices and MDL QSAR software.
MedLine Citation:
PMID:  16242226     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Quantitative structure-activity relationship (QSAR) software offers a rapid, cost effective means of prioritizing the mutagenic potential of chemicals. MDL QSAR models were developed using atom-type E-state indices and non-parametric discriminant analysis. Models were developed for Salmonella typhimurium gene mutation, combining results from strains TA97, TA98, TA100, TA1535, TA1536, TA1537, and TA1538 (n=3228), and Escherichia coli gene mutation tests WP2, WP100, and polA (n=472). Composite microbial mutation models (n=3338) were developed combining all Salmonella, E. coli, and the Bacillus subtilis rec spot test study results. The datasets contained 74% non-pharmaceuticals and 26% pharmaceuticals. Salmonella and microbial mutagenesis external validation studies included a total of 1444 and 1485 compounds, respectively. The average specificity, sensitivity, positive predictivity, concordance, and coverage of Salmonella models was 76, 81, 73, 78, and 98%, respectively, with similar performance for the microbial mutagenesis models. MDL QSAR and discriminant analysis provides rapid and highly automated mutagenicity screening software with good specificity, sensitivity, and coverage that is simpler and requires less user intervention than other similar software. MDL QSAR modules for microbial mutagenicity can provide efficient and cost effective large scale screening of compounds for mutagenic potential for the chemical and pharmaceutical industry.
Authors:
Joseph F Contrera; Edwin J Matthews; Naomi L Kruhlak; R Daniel Benz
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Publication Detail:
Type:  Journal Article     Date:  2005-10-19
Journal Detail:
Title:  Regulatory toxicology and pharmacology : RTP     Volume:  43     ISSN:  0273-2300     ISO Abbreviation:  Regul. Toxicol. Pharmacol.     Publication Date:  2005 Dec 
Date Detail:
Created Date:  2005-11-15     Completed Date:  2006-01-31     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8214983     Medline TA:  Regul Toxicol Pharmacol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  313-23     Citation Subset:  IM    
Affiliation:
US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Science, Informatics and Computational Safety Analysis Staff, 5600 Fishers Lane, Rockville, MD 20857, USA. contrerajf@cder.fda.gov
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Bacteria / drug effects*,  genetics*
Computer Simulation
Databases, Genetic
Escherichia coli / drug effects,  genetics
Models, Statistical
Mutagenicity Tests*
Quantitative Structure-Activity Relationship
Reproducibility of Results
Salmonella typhimurium / drug effects,  genetics
Software
United States
United States Environmental Protection Agency
United States Food and Drug Administration

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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