Literature DB >> 17975849

Structure-activity relationships for thiol reactivity and rat or human hepatocyte toxicity induced by substituted p-benzoquinone compounds.

Katie Chan1, Neil Jensen, Peter J O'Brien.   

Abstract

Covalent binding of toxic chemicals to cellular targets is a molecular interaction that initiates a wide array of adverse biological effects. The creation of a covalent bond can be cited as a key initiating step along many toxicity pathways which must be predicted in order to predict the potential of a chemical to cause specific harmful effects. Currently, quantitative structure-activity relationship (QSAR) models are being improved by focusing on endpoints such as simple electrophile reactivity for covalent interactions rather than on commonly used complex toxicity endpoints. The cytotoxicity and electrophilic reactivity of 10 p-substituted benzoquinone derivatives, which are well known electrophilic alkylating agents, were investigated under the premise that QSAR toxicity models can be improved when the molecular triggering event is considered. Hepatocyte toxicity was determined by incubation of individual compounds with freshly isolated rat or cryopreserved human hepatocyte suspensions. The potential for chemical reactivity between a chemical and cellular target was measured by determining non-enzymic reactivity with glutathione, representing thiol nucleophiles. The decline in free thiol moieties was measured to characterize the electrophile reactivity. It was found that the degree of rat hepatotoxicity induced by benzoquinones correlated with the rate at which they non-enzymically react with glutathione and to various global and atomic electronic frontier orbital parameters which described electrophilicity. Human hepatocytes showed similar results but the statistical significance was much lower. The QSAR expressions suggest that covalent binding reactivity serves as a good correlate to hepatotoxicity and could improve QSAR modeling for potential toxicity risks. 2008 John Wiley & Sons, Ltd

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Year:  2008        PMID: 17975849     DOI: 10.1002/jat.1312

Source DB:  PubMed          Journal:  J Appl Toxicol        ISSN: 0260-437X            Impact factor:   3.446


  16 in total

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10.  Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites.

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Journal:  J Chem Inf Model       Date:  2020-09-16       Impact factor: 4.956

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