ToxSci Advance Access originally published online on October 4, 2009
Toxicological Sciences 2009 112(2):385-393; doi:10.1093/toxsci/kfp231
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Weighted Feature Significance: A Simple, Interpretable Model of Compound Toxicity Based on the Statistical Enrichment of Structural Features
* Department of Health and Human Services, NIH Chemical Genomics Center, National Institutes of Health, Bethesda, Maryland 20892-3370
Department of Health and Human Services, National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27713
1 To whom correspondence should be addressed at Department of Health and Human Services, National Institutes of Health Chemical Genomics Center, National Institutes of Health, 9800 Medical Center Drive, Bethesda, MD 20892-3370. Fax: (301) 217-5736. E-mail: huangru{at}mail.nih.gov.
Received August 5, 2009; accepted September 15, 2009
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Abstract |
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In support of the U.S. Tox21 program, we have developed a simple and chemically intuitive model we call weighted feature significance (WFS) to predict the toxicological activity of compounds, based on the statistical enrichment of structural features in toxic compounds. We trained and tested the model on the following: (1) data from quantitative high–throughput screening cytotoxicity and caspase activation assays conducted at the National Institutes of Health Chemical Genomics Center, (2) data from Salmonella typhimurium reverse mutagenicity assays conducted by the U.S. National Toxicology Program, and (3) hepatotoxicity data published in the Registry of Toxic Effects of Chemical Substances. Enrichments of structural features in toxic compounds are evaluated for their statistical significance and compiled into a simple additive model of toxicity and then used to score new compounds for potential toxicity. The predictive power of the model for cytotoxicity was validated using an independent set of compounds from the U.S. Environmental Protection Agency tested also at the National Institutes of Health Chemical Genomics Center. We compared the performance of our WFS approach with classical classification methods such as Naive Bayesian clustering and support vector machines. In most test cases, WFS showed similar or slightly better predictive power, especially in the prediction of hepatotoxic compounds, where WFS appeared to have the best performance among the three methods. The new algorithm has the important advantages of simplicity, power, interpretability, and ease of implementation.
Key Words: modeling; toxicity prediction; structural features; cell viability; caspase-3,7 activation; in vivo toxicity.