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Prediction of Acute Mammalian Toxicity Using QSAR Methods: A Case Study of Sulfur Mustard and Its Breakdown Products
Computational Toxicology and Methods Development Lab, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA
Environmental Public Health Readiness Branch, National Center for Environmental Health, Atlanta, GA 30333, USA
* Author to whom correspondence should be addressed.
Received: 6 June 2012; in revised form: 19 July 2012 / Accepted: 23 July 2012 / Published: 27 July 2012
Abstract: Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR), has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance’s database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (the LD50) for determining relative toxicity of a number of substances. In general, the smaller the LD50 value, the more toxic the chemical, and the larger the LD50 value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s) of interest, during emergency responses, LD50 values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD50 models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD) are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD50 values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field.
Keywords: sulfur mustard; QSAR; SAR; mammalian oral; LD50; quantitative structure-activity relationship; in silico modeling
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Ruiz, P.; Begluitti, G.; Tincher, T.; Wheeler, J.; Mumtaz, M. Prediction of Acute Mammalian Toxicity Using QSAR Methods: A Case Study of Sulfur Mustard and Its Breakdown Products. Molecules 2012, 17, 8982-9001.
Ruiz P, Begluitti G, Tincher T, Wheeler J, Mumtaz M. Prediction of Acute Mammalian Toxicity Using QSAR Methods: A Case Study of Sulfur Mustard and Its Breakdown Products. Molecules. 2012; 17(8):8982-9001.
Ruiz, Patricia; Begluitti, Gino; Tincher, Terry; Wheeler, John; Mumtaz, Moiz. 2012. "Prediction of Acute Mammalian Toxicity Using QSAR Methods: A Case Study of Sulfur Mustard and Its Breakdown Products." Molecules 17, no. 8: 8982-9001.