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Identification of structural alerts for liver and kidney toxicity using repeated dose toxicity data

Overview of attention for article published in BMC Chemistry, November 2015
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Title
Identification of structural alerts for liver and kidney toxicity using repeated dose toxicity data
Published in
BMC Chemistry, November 2015
DOI 10.1186/s13065-015-0139-7
Pubmed ID
Authors

Fabiola Pizzo, Domenico Gadaleta, Anna Lombardo, Orazio Nicolotti, Emilio Benfenati

Abstract

The potential for a compound to cause hepatotoxicity and nephrotoxicity is a matter of extreme interest for human health risk assessment. To assess liver and kidney toxicity, repeated-dose toxicity (RDT) studies are conducted mainly on rodents. However, these tests are expensive, time-consuming and require large numbers of animals. For early toxicity screening, in silico models can be applied, reducing the costs, time and animals used. Among in silico approaches, structure-activity relationship (SAR) methods, based on the identification of chemical substructures (structural alerts, SAs) related to a particular activity (toxicity), are widely employed. We identified and evaluated some SAs related to liver and kidney toxicity, using RDT data on rats taken from the hazard evaluation support system (HESS) database. We considered only SAs that gave the best percentages of true positives (TP). It was not possible to assign an unambiguous mode of action for all the SAs, but a mechanistic explanation is provided for some of them. Such achievements may help in the early identification of liver and renal toxicity of substances.

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Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Serbia 1 2%
Unknown 53 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 26%
Student > Bachelor 8 15%
Researcher 5 9%
Student > Postgraduate 5 9%
Other 4 7%
Other 7 13%
Unknown 11 20%
Readers by discipline Count As %
Chemistry 12 22%
Pharmacology, Toxicology and Pharmaceutical Science 10 19%
Agricultural and Biological Sciences 5 9%
Environmental Science 4 7%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 5 9%
Unknown 16 30%